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To investigate whether a smartphone-based gait analysis tool can reliably output gait quality parameters that can be cross-analyzed to establish individual & disease-based changes in gait quality patterns. A cross-sectional study made up of a 48-patients undergoing disability certification at the "Dr. José Castro Villagrana" or the "Dr. David Fragoso Lizalde" Health Centers in Mexico City, Mexico. Their sensorimotor performance was evaluated through an in-house smartphone/IMU based digital tool. Gait was analyzed by means of frequency analysis of the acceleration of the body mass measured at the sternum. A composite gait quality score was determined through principal component analysis based primarily on the explainability and uniformity of gait. Quality independence against demographic variables (age & weight) was tested through ANCOVA. The association between gait quality and gait parameters was analyzed by using multiple linear regression. A multiple regression model developed with a limited set of gait quality parameters successfully predicted gait smoothness with a 97.05 % accuracy with a mean square error of 0.085 between predicted and actual quality scores. The model demonstrates different predictive capacities across disease groups, with Osteoarthrosis + Osteoporosis having the highest R2 at 0.98 (p < 0.001) and Coxarthrosis having the lowest explained R2 at 0.79 (p < 0.001). The assessment of gait quality, in family medicine, with low-cost digital tools is an area of opportunity yet to be explored. This tool can potentially disrupt the current disability workflow between primary and specialty care to have an objective method of assessing gait within a clinical consult. Individual patient-level benchmarking can give us insights into the patient's disease status, develop practical intervention strategies, and control the cost and quality of medical care by predicting an individualized course of disability or rehabilitation. Further studies are needed to validate digital gait assessments as clinical decision support tools for day-to-day clinical operations. Gait Analysis, Smartphone, Primary Health Care, Osteoarthrosis • Gait is a vital biomarker for patient monitoring and diagnosis. • Abnormal gait patterns signify disease progression and rehabilitation needs. • Step length and speed change contribute to mobility impairments in osteoarthrosis. • Disease-based gait patterns can be distinguished through gait quality assessment. • Gait assessment can enable more accurate diagnosis and management in primary care. [ABSTRACT FROM AUTHOR]

Objective: The present study aimed to assess the consistencies and performances of deep learning (DL) models in the diagnosis of condylar osteoarthritis (OA) among patients with dentofacial deformities using panoramic temporomandibular joint (TMJ) projection images. Methods: A total of 68 TMJs with or without condylar OA in dentofacial deformity patients were tested to verify the consistencies and performances of DL models created using 252 TMJs with or without OA in TMJ disorder and dentofacial deformity patients; these models were used to diagnose OA on conventional panoramic (Con-Pa) images and open (Open-TMJ) and closed (Closed-TMJ) mouth TMJ projection images. The GoogLeNet and VGG-16 networks were used to create the DL models. For comparison, two dental residents with < 1 year of experience interpreting radiographs evaluated the same condyle data that had been used to test the DL models. Results: On Open-TMJ images, the DL models showed moderate to very good consistency, whereas the residents' demonstrated fair consistency on all images. The areas under the curve (AUCs) of both DL models on Con-Pa (0.84 for GoogLeNet and 0.75 for VGG-16) and Open-TMJ images (0.89 for both models) were significantly higher than the residents' AUCs (p < 0.01). The AUCs of the DL models on Open-TMJ images (0.89 for both models) were higher than the AUCs on Closed-TMJ images (0.72 for both models). Conclusions: The DL models created in this study could help residents to interpret Con-Pa and Open-TMJ images in the diagnosis of condylar OA. [ABSTRACT FROM AUTHOR]

The use of three-dimensional (3D) gait analysis to image femorotibial translation can aid in the diagnosis of pathology and provide additional insight into the severity of KOA (knee osteoarthritis). Femorotibial translation is of particular importance in patients undergoing UKA (unicompartmental knee arthroplasty), as the absence or elongation of ligamentous structures results in changes in the kinematic alignment. The aim of the study was to evaluate the parameters of femorotibial translation in patients with MOA (medial unicompartmental OA). An artificial model was employed to develop a method for calculating femorotibial translation in vitro. In a prospective cohort study, gait data using three-dimensional gait analysis were collected from 11 patients (68.73 ± 9.22 years) with severe OA scheduled for UKA and 29 unmatched healthy participants (22.07 ± 2.23 years). The discrete variables characterising femorotibial translation were compared for statistical significance (p < 0.05) using the Student's t-test and the Mann–Whitney U-test. The results of the study validated an artificial model to mimic femorotibial translation. The comparison of patients scheduled for UKA and a healthy unmatched control group showed no statistically significant differences concerning femorotibial translation in all three planes (p > 0.05). However, the PROMs (patient-reported outcome measures), spatiotemporal, and kinematic parameters showed statistically significant differences between the groups (p < 0.001). The data presented here demonstrate typical changes in PROMs as well as spatiotemporal and kinematic outcomes for MOA as seen in knee OA. The results of the clinical gait analyses demonstrate individualised femorotibial translation. The extent of individual femorotibial translation may prove to be an important parameter for altered joint kinematics in patients with MOA, especially prior to UKA implantation. [ABSTRACT FROM AUTHOR]

Background: Administrative healthcare databases, such as Medicare, are increasingly used to identify groups at risk of a crash. However, they only contain information on crash-related injuries, not all crashes. If the driver characteristics associated with crash and crash-related injury differ, conflating the two may result in ineffective or imprecise policy interventions. Methods: We linked 10 years (2008–2017) of Medicare claims to New Jersey police crash reports to compare the demographics, clinical diagnoses, and prescription drug dispensings for crash-involved drivers ≥ 68 years with a police-reported crash to those with a claim for a crash-related injury. We calculated standardized mean differences to compare characteristics between groups. Results: Crash-involved drivers with a Medicare claim for an injury were more likely than those with a police-reported crash to be female (62.4% vs. 51.8%, standardized mean difference [SMD] = 0.30), had more clinical diagnoses including Alzheimer's disease and related dementias (13.0% vs. 9.2%, SMD = 0.20) and rheumatoid arthritis/osteoarthritis (69.5% vs 61.4%, SMD = 0.20), and a higher rate of dispensing for opioids (33.8% vs 27.6%, SMD = 0.18) and antiepileptics (12.9% vs 9.6%, SMD = 0.14) prior to the crash. Despite documented inconsistencies in coding practices, findings were robust when restricted to claims indicating the injured party was the driver or was left unspecified. Conclusions: To identify effective mechanisms for reducing morbidity and mortality from crashes, researchers should consider augmenting administrative datasets with information from police crash reports, and vice versa. When those data are not available, we caution researchers and policymakers against the tendency to conflate crash and crash-related injury when interpreting their findings. [ABSTRACT FROM AUTHOR]

Background: Anterior approach total hip arthroplasty (THA) decreases the rate of dislocation but increases femoral-sided complications in the way of periprosthetic fractures and component loosening. A cemented prosthesis may reduce femoral-sided complications and improve the risk:benefit profile of anterior approach THA. Methods: Data from the Australian National Joint Replacement Registry were analysed for patients undergoing primary THA via the anterior or posterior approach using a cemented polished femoral stem from January 2015 to December 2021. The primary outcome measure was the cumulative percent revision (CPR) for all causes and CPR for femoral component loosening and fracture. The CPR for the primary outcome measures were compared between the anterior and posterior approach and adjusted for age, sex, ASA score, BMI and femoral head size. Results: The study included 60,739 THAs with cemented stems (10,742 anterior, 49,997 posterior). The rate of revision of the anterior versus the posterior approach did not significantly differ (HR 0.87 (95% CI, 0.74–1.03), p = 0.100). Anterior approach THA had a significantly higher rate of revision for femoral component loosening (HR 5.06 [95% CI, 3.08–8.30], p < 0.001); and a decreased rate of revision for infection (HR 0.59 [95% CI, 0.43–0.81], p = 0.001) and dislocation/instability (HR 0–3 months 0.48 [95% CI, 0.27–0.83], p = 0.008; HR >3 months 0.30 [95% CI, 0.15–0.61], p < 0.001). There was no difference in the rate of revision surgery for fracture between the 2 approaches (HR 1.01 [95% CI, 0.71–1.43]), p = 0.975). Conclusions: There is no significant difference in overall revision rates with cemented femoral fixation performed with an anterior or posterior approach. Cemented fixation performed with the anterior approach partly mitigates femoral complications with no difference in the revision rate for fracture but an increased rate of femoral component loosening. [ABSTRACT FROM AUTHOR]

An electrochemical sensor sensitive to coenzyme A (CoA) was designed using a CoA-responsive polyallylamine–manganese oxide–polymer dot nanogel coated on the electrode surface to detect various genetic models of osteoarthritis (OA). The CoA-responsive nanogel sensor responded to the abundance of CoA in OA, causing the breakage of MnO2 in the nanogel, thereby changing the electroconductivity and fluorescence of the sensor. The CoA-responsive nanogel sensor was capable of detecting CoA depending on the treatment time and distinguishing the response towards different OA genetic models that contained different levels of CoA (wild type/WT, NudT7 knockout/N7KO, and Acot12 knockout/A12KO). The WT, N7KO, and A12KO had distinct resistances, which further increased as the incubation time were changed from 12 h (R12h = 2.11, 2.40, and 2.68 MΩ, respectively) to 24 h (R24h = 2.27, 2.59, and 2.92 MΩ, respectively) compared to the sensor without treatment (Rcontrol = 1.63 MΩ). To simplify its application, the nanogel sensor was combined with a wireless monitoring device to allow the sensing data to be directly transmitted to a smartphone. Furthermore, OA-indicated anabolic (Acan) and catabolic (Adamts5) factor transcription levels in chondrocytes provided evidence regarding CoA and nanogel interactions. Thus, this sensor offers potential usage in simple and sensitive OA diagnostics. [ABSTRACT FROM AUTHOR]

Objective: Femoroacetabular impingement is a dysfunction of the hip joint with the potential to cause early hip osteoarthritis. The aim of this study was to examine the Alpha Angle and Femoral Neck-Head Offset measurements of patients who underwent pelvic Computed Tomography examination for any indication with radiologists. Material and Method: The results of 1,782 right and left hip joints of 891 individuals aged 18-45 years who underwent pelvic computed tomography examination (with or without contrast) for various reasons were evaluated retrospectively. Alpha Angle and Femoral Neck-Head Offset measurements were performed on computed tomography sections. In this study, an Alpha Angle of 55° or more and a Femoral Neck-Head Offset distance of less than 8 mm were considered abnormal. Results: A total of 891 individuals, 758 (85.1%) males and 133 (14.9%) females, were included in this study. The mean age of the individuals was 24.65 ± 6.01 years. The mean right Alpha Angle was 45.42 ± 4.4 (min 38.00, max 62.00), the mean left Alpha Angle was 46.65 ± 5.1 (min 38.00, max 72.10), the mean right Femoral Neck-Head Offset was 10.21 ± 0.02 (min 6.2, max 11.4), and the mean left Femoral Neck-Head Offset was 10.57 ± 0.01 (min 5.6, max 12.0). The total number of individuals with Alpha Angle = 55° in both hip joints was 37, 33 of whom were male and 4 were female. There were no individuals with Femoral Neck-Head Offset < 8 mm in both hip joints. Conclusion: Imaging features known to be associated with femoroacetabular impingement are seen in asymptomatic adult hip joints, particularly in males. If anthropometric measurements are outside normal limits in asymptomatic young male and female individuals, clinical correlation of the findings of these individuals should be recommended. [ABSTRACT FROM AUTHOR]

Objectives: The study aimed to investigate the diagnostic values of different musculoskeletal ultrasound (MSUS) signs, serum uric acid (SUA), and their combined detection for gouty arthritis (GA). Patients and methods: In this retrospective study, 70 patients (62 males, 8 females; mean age: 46.1±14.1 years; range, 25 to 86 years) diagnosed with GA (the GA group) between August 2022 and March 2023 and 70 patients (54 females, 16 males; mean age: 49.0±14.1 years; range, 21 to 75 years) diagnosed with rheumatoid arthritis and osteoarthritis during the same period (the non-GA group) were included. The positive rate of MSUS signs and SUA in both groups was recorded to compare the differences. The correlations of MSUS signs and SUA with GA were analyzed using Spearman's rank correlation analysis. The diagnostic values of different MSUS signs, SUA, and their combined detection for GA were analyzed using a receiver operating characteristic, the area under the curve (AUC), sensitivity, specificity, and the Youden index. Results: The positive rate of the double contour (DC) sign (chi-squared [Χ²]=102.935, p<0.001), hyperechoic spots (Χ²=56.395, p<0.001), bone erosions (Χ²=10.080, p<0.001), and SUA (Χ²=41.117, p<0.001) were higher in the GA group than in the non-GA group. The positive rate of the DC sign (rs=0.829, p=0.001), hyperechoic spots (rs=0.631, p<0.001), bone erosion (rs=0.268, p=0.001), and SUA (rs=0.542, p<0.001) were positively correlated with GA. Among the single-indicator measures, the DC sign exhibited the highest diagnostic value (AUC=0.907, sensitivity=81.4%, specificity=100%, p<0.001). Among the combined-indicator measures, the DC sign combined with SUA exhibited the highest diagnostic value (AUC=0.929, sensitivity=91.4%, specificity=94.3%, p<0.001), higher than DC sign detection alone. Conclusion: The DC sign combined with SUA yielded a high diagnostic value and can thus provide a reliable basis for effectively and efficiently diagnosing GA. [ABSTRACT FROM AUTHOR]

While there has been increased attention to the use of reverse total shoulder arthroplasty (RTSA) to treat rotator cuff intact glenohumeral osteoarthritis (RCIOA) for older age groups, there has not been as precise an assessment of the differences in utilization for female vs. male patients or in specific age groups. Our purpose was to determine if differences existed in the utilization of shoulder arthroplasty to treat RCIOA based on gender and age in North America. Anatomic total shoulder arthroplasty (ATSA) and RTSA cases were queried from the American Academy of Orthopaedic Surgeons Shoulder and Elbow Registry between January 2015 and December 2021. Cases were included if they had a diagnosis of RCIOA, defined by the International Classification of Diseases-10 codes M19.011, M19.012, and M19.019. Cases were stratified by procedure, age, gender, and year of surgery. Chi-square and Fisher's exact tests were calculated to assess the associations between procedure type and patient demographics. There were 2748 (48.06%) ATSA and 2970 (51.94%) RTSA procedures reported to the American Academy of Orthopaedic Surgeons Shoulder and Elbow Registry. There was a significant relationship between procedure type and age group (P <.001) in that ATSA was utilized more frequently than RTSA for patients ages <70 years old, and this relationship reversed for ages ≥ 70 years old. Female patients were more likely to receive RTSA (P <.001). When looking at the relationship between procedure type and gender by age group, both genders were more likely to receive ATSA compared to RTSA for age groups 50-59 and 60-69 (P =.0097 and P =.0005, respectively) but not for other age groups. For patients ≥ 70 years old, both females and males were more likely to receive RTSA, but this relationship did not reach statistical significance (P =.1094). For both genders and ages ≥ 70 years old, there was a significant relationship between year and procedure type (P <.0001) in that RTSA was more commonly utilized in 2017 and onward. When assessing patients with RCIOA, the use of ATSA and RTSA was similar, but for patients 50 to 69 years old, for both genders, the use of ATSA was greater. Although RTSA was more commonly used for both genders in the ≥ 70 years old population, this difference was not significant. Interestingly, for both genders, ages ≥ 70 years old, RTSA was significantly more utilized from 2017 onward. This analysis highlights the influence of age and gender in use of ATSA and RTSA. [ABSTRACT FROM AUTHOR]

Objective: Automated machine learning (autoML) platforms allow health care professionals to play an active role in the development of machine learning (ML) algorithms according to scientific or clinical needs. The aim of this study was to develop and evaluate such a model for automated detection and grading of distal hand osteoarthritis (OA). Methods: A total of 13,690 hand radiographs from 2,863 patients within the Swiss Cohort of Quality Management (SCQM) and an external control data set of 346 non‐SCQM patients were collected and scored for distal interphalangeal OA (DIP‐OA) using the modified Kellgren/Lawrence (K/L) score. Giotto (Learn to Forecast [L2F]) was used as an autoML platform for training two convolutional neural networks for DIP joint extraction and subsequent classification according to the K/L scores. A total of 48,892 DIP joints were extracted and then used to train the classification model. Heatmaps were generated independently of the platform. User experience of a web application as a provisional user interface was investigated by rheumatologists and radiologists. Results: The sensitivity and specificity of this model for detecting DIP‐OA were 79% and 86%, respectively. The accuracy for grading the correct K/L score was 75%, with a κ score of 0.76. The accuracy per DIP‐OA class differed, with 86% for no OA (defined as K/L scores 0 and 1), 71% for a K/L score of 2, 46% for a K/L score of 3, and 67% for a K/L score of 4. Similar values were obtained in an independent external test set. Qualitative and quantitative user experience testing of the web application revealed a moderate to high demand for automated DIP‐OA scoring among rheumatologists. Conversely, radiologists expressed a low demand, except for the use of heatmaps. Conclusion: AutoML platforms are an opportunity to develop clinical end‐to‐end ML algorithms. Here, automated radiographic DIP‐OA detection is both feasible and usable, whereas grading among individual K/L scores (eg, for clinical trials) remains challenging. [ABSTRACT FROM AUTHOR]

Objective: The study aims to evaluate the accuracy of an MRI-based artificial intelligence (AI) segmentation cartilage model by comparing it to the natural tibial plateau cartilage. Methods: This study included 33 patients (41 knees) with severe knee osteoarthritis scheduled to undergo total knee arthroplasty (TKA). All patients had a thin-section MRI before TKA. Our study is mainly divided into two parts: (i) In order to evaluate the MRI-based AI segmentation cartilage model's 2D accuracy, the natural tibial plateau was used as gold standard. The MRI-based AI segmentation cartilage model and the natural tibial plateau were represented in binary visualization (black and white) simulated photographed images by the application of Simulation Photography Technology. Both simulated photographed images were compared to evaluate the 2D Dice similarity coefficients (DSC). (ii) In order to evaluate the MRI-based AI segmentation cartilage model's 3D accuracy. Hand-crafted cartilage model based on knee CT was established. We used these hand-crafted CT-based knee cartilage model as gold standard to evaluate 2D and 3D consistency of between the MRI-based AI segmentation cartilage model and hand-crafted CT-based cartilage model. 3D registration technology was used for both models. Correlations between the MRI-based AI knee cartilage model and CT-based knee cartilage model were also assessed with the Pearson correlation coefficient. Results: The AI segmentation cartilage model produced reasonably high two-dimensional DSC. The average 2D DSC between MRI-based AI cartilage model and the tibial plateau cartilage is 0.83. The average 2D DSC between the AI segmentation cartilage model and the CT-based cartilage model is 0.82. As for 3D consistency, the average 3D DSC between MRI-based AI cartilage model and CT-based cartilage model is 0.52. However, the quantification of cartilage segmentation with the AI and CT-based models showed excellent correlation (r = 0.725; P values < 0.05). Conclusion: Our study demonstrated that our MRI-based AI cartilage model can reliably extract morphologic features such as cartilage shape and defect location of the tibial plateau cartilage. This approach could potentially benefit clinical practices such as diagnosing osteoarthritis. However, in terms of cartilage thickness and three-dimensional accuracy, MRI-based AI cartilage model underestimate the actual cartilage volume. The previous AI verification methods may not be completely accurate and should be verified with natural cartilage images. Combining multiple verification methods will improve the accuracy of the AI model. [ABSTRACT FROM AUTHOR]

Objective: Diagnosing musculoskeletal infections in children is challenging. In recent years, with the advancement of ultrasound technology, high-resolution ultrasound has unique advantages for musculoskeletal children. The aim of this work is to summarize the ultrasonographic and clinical characteristics of children with pyogenic arthritis and osteomyelitis. This study provides a simpler and more effective diagnostic basis for clinical treatment. Methods: Fifty children with osteomyelitis or arthritis were diagnosed via ultrasound, and the results of the ultrasound diagnosis were compared with those of magnetic resonance imaging and surgery. Clinical and ultrasound characteristics were also analyzed. Results: Out of 50 patients, 46 were confirmed to have suppurative infection by surgical and microbiological examination. Among these 46 patients, 26 were diagnosed with osteomyelitis and 20 had arthritis. The manifestations of osteomyelitis were subperiosteal abscess (15 patients), bone destruction (17 patients), bone marrow abscess (9 patients), and adjacent joint abscess (13 patients). Osteomyelitis mostly affects the long bones of the limbs, femur and humerus (10 and 9 patients, respectively), followed by the ulna, radius, tibia and fibula (one patient each). The manifestations of arthritis were joint pus (20 patients) and joint capsule thickening (20 patients), and hip dislocation (8 patients). All the patients had arthritis involving the hip joint. Conclusion: Subperiosteal abscess, bone destruction, and joint abscess with dislocation are ultrasonographic features of pyogenic osteoarthritis. The findings of this work can improve the early diagnosis and differentiation of pyogenic osteoarthritis and provide a reliable basis for treatment. [ABSTRACT FROM AUTHOR]

Glenoid bone grafting is an option to correct severe glenoid retroversion and posterior bone loss in the setting of primary total shoulder arthroplasty (TSA). However, limited studies report the midterm outcomes of this procedure. This was a retrospective case series of patients undergoing TSA with an autologous humeral head glenoid bone graft by a single surgeon between 2004 and 2014. Outcome variables included preoperative and postoperative forward elevation, external rotation, internal rotation, visual analog scale score, American Shoulder and Elbow Surgeons score, and simple shoulder test score. Radiographic variables included the acromiohumeral interval and Lazarus glenoid loosening score. In total, 7 cases were included in our analysis. The mean age was 65.0 years (range 55-80), and the mean follow-up was 6.8 years (range 4.2-8.2). All patients had a diagnosis of osteoarthritis as the primary indication for surgery. The glenoid implant types were 2 pegged (28.6%) and 5 keeled (71.4%). The Walch glenoid classifications were 3 B2 (42.9%), 1 B3 (14.3%), and 3 with unavailable preoperative computed tomography scans (42.9%). Mean forward elevation, external rotation, visual analog scale, and American Shoulder and Elbow Surgeons all improved significantly from preoperative to postoperative. Mean internal rotation and simple shoulder test trended toward improvement. Radiographic glenoid loosening was observed in 1 patient, and 1 patient underwent revision to reverse TSA due to graft resorption and glenoid loosening at 8.3 years following the index procedure. Glenoid bone grafting is a viable option for patients requiring TSA with severe glenoid retroversion and posterior bone loss. Graft resorption and glenoid loosening remain concerns; however, the majority of patients in our study demonstrated significant improvement following their procedure. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to evaluate the long-term functional and MRI results of 35 patients who underwent bicruciate ligament reconstruction combining an ACL autograft using the gracilis and semitendinosus tendons and double-bundle PCL reconstruction using the LARS artificial ligament. Methods: The outcomes were measured using the Lysholm score, the Tegner activity level scale and the International Knee Documentation Committee form (IKDC 2000). KT-1000 was used to assess the clinical anterior knee laxity. Radiographs and Magnetic Resonance Imaging (MRI) was used to evaluate osteoarthritis, the continuity and integrity of ACL autograft and LARS. Results: This retrospective study examined 35 patients who underwent single-stage bicruciate ligament reconstruction between May 2005 and January 2017 with a follow-up period ranging from 3 to 15 years (a mean of 7.5 years). The mean Lysholm score was 74, mean IKDC 2000 was 71. There was a statistically significant difference with a higher Lysholm score (78.9) in early versus delayed surgical intervention (p = 0.023). Using the Kellgren Lawrence osteoarthritis classification system, radiographic findings showed stage II or III in 83% of the sample population. The MRI results revealed a rupture rate of 22% of the anterior autografted ligament and 28% of the posterior LARS artificial ligament. However, there were no long-term artificial ligament-induced complications. There was no correlation between artificial ligament rupture and poor functional results (Lysholm < 65). Conclusion: The results of this study with a mean follow-up of 7.5 years show satisfactory functional scores considering the initial trauma. It seems reasonable to propose early surgical treatment with double reconstruction of the cruciate ligaments within the first 21 days of the trauma. Post-traumatic osteoarthritis is inevitable in multi-ligament knee injuries despite anatomical reconstruction. The use of a LARS artificial ligament appears to be a valid alternative for PCL reconstruction in the context of multi-ligament injury and in the absence of sufficient autologous transplants. [ABSTRACT FROM AUTHOR]

Purpose: The primary goal of this study was to investigate whether superior humeral head osteophyte (SHO) size is associated with rotator cuff insufficiency, including rotator cuff tear (RCT), supraspinatus tendon thickness, and fatty infiltration of the rotator cuff muscles. Methods: Patients ≥ 18 years who were diagnosed with glenohumeral osteoarthritis were retrospectively reviewed. SHO size was determined by radiograph. MRI measured SHO and RCT presence, type, and size; supraspinatus tendon thickness; and fatty infiltration of rotator cuff musculature. Results: A total of 461 patients were included. Mean SHO size was 1.93 mm on radiographs and 2.13 mm on MRI. Risk ratio for a RCT was 1.14. For each 1-mm increase in SHO size on radiograph, supraspinatus tendon thickness decreased by 0.20 mm. SHO presence was associated with moderate-to-severe fatty infiltration of the supraspinatus with a risk ratio of 3.16. Conclusion: SHOs were not associated with RCT but were associated with higher risk of supraspinatus FI and decreased tendon thickness, which could indicate rotator cuff insufficiency. Level of evidence: IV. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a chronic and low-grade inflammatory disease associated with metabolism disorder and multiple cell death types in the synovial tissues. Sulfur metabolism has not been studied in OA. Methods: First, we calculated the single sample gene set enrichment analysis score of sulfur metabolism-associated annotations (i.e., cysteine metabolism process, regulation of sulfur metabolism process, and disulfidptosis) between healthy and synovial samples from patients with OA. Sulfur metabolism-related differentially expressed genes (DEGs) were analyzed in OA. Least absolute shrinkage and selection operator COX regression were used to identify the sulfur metabolism-associated gene signature for diagnosing OA. Correlation and immune cell deconvolution analyses were used to explore the correlated functions and cell specificity of the signature gene, TM9SF2. TM9SF2's effect on the phagocytosis of macrophages M2 was analyzed by coculturing macrophages with IgG-coated beads or apoptotic Jurkat cells. Results: A diagnostic six gene signature (i.e., MTHFD1, PDK4, TM9SF2, POU4F1, HOXA2, NCKAP1) was identified based on the ten DEGs, validated using GSE12021 and GSE1919 datasets. TM9SF2 was upregulated in the synovial tissues of OA at both mRNA and protein levels. The relationship between TM9SF2 and several functional annotations, such as antigen processing and presentation, lysosome, phagosome, Fcγ-mediated phagocytosis, and tyrosine metabolism, was identified. TM9SF2 and macrophages M2 were significantly correlated. After silencing TM9SF2 in THP-1-derived macrophages M2, a significantly reduced phagocytosis and attenuated activation of PLC-γ1 were observed. Conclusion: A sulfur metabolism-associated six-gene signature for OA diagnosis was constructed and upregulation of the phagocytosis-associated gene, TM9SF2, was identified. The findings are expected to deepen our understanding of the molecular mechanism underlying OA development and be used as potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a common degenerative disease involving articular cartilage, in which ferroptosis of chondrocytes plays an important role. Baicalin (BAI) exerts regulatory effects in a wide range of orthopedic diseases including OA, but its effect on ferroptosis of chondrocytes (CHs) is still unclear. The purpose of this study was to determine the effect of BAI on ferroptosis in human OA chondrocytes (OACs), and to explore its possible mechanism. Methods: CHs were treated with IL-1β (10 ng/mL) to simulate inflammation in vitro. Immunofluorescence, quantitative RT-PCR, Western blotting and cell viability assay were performed to evaluate the impacts of BAI on Fe2+ level, mitochondrial dysfunction, ferroptosis-related proteins, oxidative stress and cytotoxicity in CHs. Additionally, siRNA was made use of to knock out nuclear factor E2-related factor 2 (Nrf2) to analyze the role played by Nrf2 in BAI-induced CH ferroptosis. Results: BAI eliminated IL-1β-induced Fe2+ accumulation, changes in mitochondrial membrane potential and ferroptosis-related protein GPX4, SLC7A11, P53 and ACSL4 levels, as well as reactive oxygen species (ROS), lipid peroxidation (LPO) and malondialdehyde (MDA) accumulation in CHs. Besides, BAI reversed IL-1β-induced decrease of Collagen II and increase of MMP13 in CHs. Meanwhile, BAI attenuated IL-1β-induced CH toxicity and promoted Nrf2 antioxidant system activation. When Nrf2 was knocked down by siRNA, the effects of BAI on IL-1β-induced ferroptosis-related proteins and antioxidant stress in CHs were significantly weakened. Conclusions: This study demonstrates that IL-1β can induce CH ferroptosis. BAI is able to inhibit IL-1β-induced CH ferroptosis and ECM degradation, and the specific mechanism may be that it can inhibit IL-1β-induced CH ferroptosis by activating Nrf2 antioxidant system to attenuate the accumulation of intracellular ROS and lipid ROS. [ABSTRACT FROM AUTHOR]

Foot and ankle weightbearing CT (WBCT) imaging has emerged over the past decade. However, a systematic review of diagnostic applications has not been conducted so far. A systematic literature search was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines after Prospective Register of Systematic Reviews (PROSPERO) registration. Studies analyzing diagnostic applications of WBCT were included. Main exclusion criteria were: cadaveric specimens and simulated WBCT. The Methodological Index for Non-Randomized Studies (MINORS) was used for quality assessment. A total of 78 studies were eligible for review. Diagnostic applications were identified in following anatomical area's: ankle (n = 14); hindfoot (n = 41); midfoot (n = 4); forefoot (n = 19). Diagnostic applications that could not be used on weightbearing radiographs (WBRX) were reported in 56/78 studies. The mean MINORS was 9.8/24 (range: 8–12). Diagnostic applications of WBCT were most frequent in the hindfoot, but other areas are on the rise. Post-processing of images was the main benefit compared to WBRX based on a moderate quality of the identified studies. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a degenerative disease. It is common in middle-aged and elderly people and is one of the main causes of disability. Currently, the etiology of OA is unclear, and no specific biomarkers for the diagnosis of OA have been identified. Therefore, finding a highly sensitive biomarker is essential for a proper diagnosis. TRNA-derived fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs) are newly discovered classes of noncoding RNAs. tRF has been proven in several studies to have significant associations with tumor diagnosis, making it a promising biomarker in cancer research. However, the diagnostic utility of tRF in OA patients and the correlation between OA progression and trf differential expression have yet to be elaborated. The purpose of this research was to identify tRFs with differential expression in OA to assess their potential as OA biomarkers. To determine the tRF-5022B expression level in this research, real-time fluorescence quantitative PCR has been employed. Agarose gel electrophoresis, Sanger sequencing, and other investigations have been employed for evaluating tRF-5022B's molecular properties. Receiver operating characteristic curve analysis has been utilized for assessing the diagnostic effectiveness of the tRF-5022B. The findings demonstrated that tRF-5022B expression was considerably lower in OA serum. The Kellgren–Lawrence grading scale was shown to correspond with serum expression levels. The ROC curve confirmed that tRF-5022B serum expression levels might differentiate OA cases from healthy individuals and RA patients. According to the aforementioned findings, tRF-5022B may be employed as a novel biomarker for OA diagnosis due to its excellent diagnostic value. [ABSTRACT FROM AUTHOR]

Objectives To explore clustering of comorbidities among patients with a new diagnosis of OA and estimate the 10-year mortality risk for each identified cluster. Methods This is a population-based cohort study of individuals with first incident diagnosis of OA of the hip, knee, ankle/foot, wrist/hand or 'unspecified' site between 2006 and 2020, using SIDIAP (a primary care database representative of Catalonia, Spain). At the time of OA diagnosis, conditions associated with OA in the literature that were found in ≥1% of the individuals (n  = 35) were fitted into two cluster algorithms, k -means and latent class analysis. Models were assessed using a range of internal and external evaluation procedures. Mortality risk of the obtained clusters was assessed by survival analysis using Cox proportional hazards. Results We identified 633 330 patients with a diagnosis of OA. Our proposed best solution used latent class analysis to identify four clusters: 'low-morbidity' (relatively low number of comorbidities), 'back/neck pain plus mental health', 'metabolic syndrome' and 'multimorbidity' (higher prevalence of all studied comorbidities). Compared with the 'low-morbidity' cluster, the 'multimorbidity' cluster had the highest risk of 10-year mortality (adjusted hazard ratio [HR]: 2.19 [95% CI: 2.15, 2.23]), followed by the 'metabolic syndrome' cluster (adjusted HR: 1.24 [95% CI: 1.22, 1.27]) and the 'back/neck pain plus mental health' cluster (adjusted HR: 1.12 [95% CI: 1.09, 1.15]). Conclusion Patients with a new diagnosis of OA can be clustered into groups based on their comorbidity profile, with significant differences in 10-year mortality risk. Further research is required to understand the interplay between OA and particular comorbidity groups, and the clinical significance of such results. [ABSTRACT FROM AUTHOR]

X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion is performed using a canonical correlation approach and obtained a feature vector that hasmore information than the original feature vector. After that, an improved whale optimization algorithm is developed for dimensionality reduction. The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine (SVM) and neural networks for classification purposes. The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%. Also, the system is explained using Explainable Artificial Intelligence (XAI) technique called occlusion, and results are compared with recent research. Based on the results compared with recent techniques, it is shown that the proposed method's accuracy significantly improved. [ABSTRACT FROM AUTHOR]

Osteoarthritis of the knee is a common problem in the elderly, leading to severe morbidity. Total Knee Arthroplasty (TKA) is a widely validated surgery to provide a remarkable extent of knee function and simultaneously alleviates pain for knee osteoarthritis (OA). It is clearly understood that precision of the rotational alignment and accuracy of the technique in the placement of the femoral component is a prerequisite for excellent and successful outcomes of TKA. Advanced technology has now allowed surgeons to understand patient-specific variabilities in anatomical reference landmarks and the relationship of component positioning in relation to the reference landmarks to achieve accurate gap balancing with minimal soft tissue release.14 Robotic Arm Assisted-Total Knee Arthroplasty (RAA-TKA) is a semi-automated system that enables us in replicating the same. Using this technology, the bony resections, component positions, probable component sizing and gap balancing can be tentatively planned preoperatively with CT Scan Analysis and executed intraoperatively. Hence this study was undertaken to estimate the relationship between femoral component placement to normal rotational landmarks such as the Posterior Condylar Axis (PCA) and to quantitatively evaluate coronal and sagittal plane correction obtained. Also, we aimed to use the data to detect any anatomical variations in the study population and evaluate the accuracy of predicted component sizing, including gender-based evaluation. A Prospective Observational Study of 1073 knees of patients of either sex above 50 years of age with Kellgren Lawrence Grade 4 Osteoarthritis of the knee which were confirmed with X-Ray undergoing RAA-TKA using MAKO Robotic System using Stryker Triathlon (Cruciate Substituting) CS Knee was conducted during the period between 2022 and 2023 in two South Indian hospitals specializing in joint replacement surgeries. We found a statistically significant difference between the native Posterior Condylar Axis (PCA) (4.82 ± 2.15°) and final femoral component external rotation (3.24 ± 1.29°) with a p-value of <0.001 at 95% confidence interval. The accuracy of component size prediction was 99.8%. Also, analysis in our study has shown the most common implant sizes to be 4 in males and 2 in females. We also found no statistically significant difference based on age, size, laterality, or primary varus deformity. RAA-TKA provides patient-specific alignment/restricted kinematic alignment which might further enhance the outcome for the patient. Reliable deformity correction in coronal and sagittal planes can be achieved. Accurate flexion and extension gap balancing can be done through component placement and with minimal soft tissue dissection. Irrespective of all the advantages noted in RAA-TKA, further follow-up and long-term outcome studies are required to properly gauge and analyze this new technology. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a prominent musculoskeletal disorder that affects approximately 303 million people worldwide. The challenges that language barriers present to the Latina population in regard to the diagnosis and treatment of OA remain largely unknown. The objective of this study was to examine disparities in the diagnosis and treatment of arthritic conditions in English- and Spanish-speaking Latinas over 40 years of age. We analyzed data from the CDC's Behavioral Risk Screening and Surveillance System (BRFSS), combining the 2017–2020 cycles using sampling weights provided by BRFSS, adjusted for multiple cycles. Determination of English- or Spanish-speaking groups was based on the language of the survey submitted. We calculated population estimates for arthritis diagnosis, physical limitations, and mean joint pain among language groups and by age (40–64 and 65+) and determined associations via odds ratios. Rates of arthritis diagnosis between groups were similar; however we found that Spanish-speaking Latinas 65+ were statistically more likely to report being limited by pain (AOR: 1.55; 95% CI: 1.14–2.09), and among both age groups Spanish-speaking Latinas reported higher pain scores than the English-speaking group (40–64 age group: Coef: 0.74, SE = 0.14, P <.001; 65 + age group: Coef: 1.05, SE = 0.2, P <.001). Results from this study show that while there were no significant differences in rates of diagnosis, Spanish-speaking Latinas were more likely to be limited by joint pain and report higher pain scores. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is the most common chronic joint disease and how ferroptosis contributes to OA has garnered much attention recently. Bioinformatics promoted the discovery of ferroptosis-related biomarkers for OA. But since OA is a whole-joint disease, sensitive biomarkers for OA are still limited. We herein focused on subchondral bone, a joint component often-ignored by existing bioinformatic reports, to identify ferroptosis-related diagnostic biomarkers for OA. Method: Microarray datasets GSE51588 and GSE55457 were downloaded from Gene Expression Omnibus database. Ferroptosis-related differential expression genes (Ferr-DEGs) between OA and normal samples were identified and their functional enrichment was analyzed. Common genes for OA diagnosis were selected from Ferr-DEGs using the combination of SVM-RFE, LASSO regression, and RandomForest machine learning algorithms. Common genes' diagnostic value was verified by receiver operating characteristic (ROC) curve and their association with immune infiltration was analyzed by CIBERSORT. Finally, candidate gene's expression was verified in chondrocytes from OA patients and in an in vitro IL-1β-induced OA model, by RT-PCR. Results: Two ferroptosis-related genes, LPCAT3 and PGD, were identified as OA diagnostic biomarkers and confirmed by ROC diagnostic test. The association of LPCAT3 and PGD with the infiltration of several types of immune cells was identified. The decreased expression of LPCAT3 and PGD was both confirmed in OA chondrocytes and IL-1β-induced OA condition. Conclusions: We identified ferroptosis-related genes LPCAT3 and PGD as potential diagnostic biomarkers for OA, which may offer insight into the role of ferroptosis in OA and provides useful information for the diagnosis and treatment of OA. [ABSTRACT FROM AUTHOR]

Background: The use of artificial intelligence has many advantages, especially in the field of oral and maxillofacial radiology. Early diagnosis of temporomandibular joint osteoarthritis by artificial intelligence may improve prognosis. Objective: The aim of this study is to perform the classification of temporomandibular joint (TMJ) osteoarthritis and TMJ segmentation on cone beam computed tomography (CBCT) sagittal images with artificial intelligence. Methods: In this study, the success of YOLOv5 architecture, an artificial intelligence model, in TMJ segmentation and osteoarthritis classification was evaluated on 2000 sagittal sections (500 healthy, 500 erosion, 500 osteophyte, 500 flattening images) obtained from CBCT DICOM images of 290 patients. Results: The sensitivity, precision and F1 scores of the model for TMJ osteoarthritis classification are 1, 0.7678 and 0.8686, respectively. The accuracy value for classification is 0.7678. The prediction values of the classification model are 88% for healthy joints, 70% for flattened joints, 95% for joints with erosion and 86% for joints with osteophytes. The sensitivity, precision and F1 score of the YOLOv5 model for TMJ segmentation are 1, 0.9953 and 0.9976, respectively. The AUC value of the model for TMJ segmentation is 0.9723. In addition, the accuracy value of the model for TMJ segmentation was found to be 0.9953. Conclusion: Artificial intelligence model applied in this study can be a support method that will save time and convenience for physicians in the diagnosis of the disease with successful results in TMJ segmentation and osteoarthritis classification. [ABSTRACT FROM AUTHOR]

Context: Ultrasound imaging is a clinically feasible tool to assess femoral articular cartilage and may have utility in tracking early knee osteoarthritis development. Traditional assessment techniques focus on measurements at a single location, which can be challenging to adopt for novice raters. Objective: To introduce a novel semiautomated ultrasound segmentation technique and determine the intrarater and interrater reliability of average regional femoral articular cartilage thickness and echo intensity of a novice and expert rater. Design: Descriptive observational study. Setting: Orthopedic clinic. Patients or Other Participants: Fifteen participants (mean [SD]; age 23.5 [4.6] y, height = 172.6 [9.3] cm, mass = 79.8 [15.7] kg) with a unilateral history of anterior cruciate ligament reconstruction participated. Intervention: None. Main Outcome Measures: One rater captured anterior femoral cartilage images of the participants’ contralateral knees using a transverse suprapatellar ultrasound assessment. The total femoral cartilage cross-sectional area of each image was segmented by a novice and expert rater. A novel custom program automatically separated the cartilage segmentations into medial, lateral, and intercondylar regions to determine the crosssectional area and cartilage length. The average cartilage thickness in each region was calculated by dividing the cross-sectional area by the cartilage length. Echo intensity was calculated as the average gray-scale pixel value of each region. Two-way random effect intraclass correlations coefficient (ICC) for absolute agreement were used to determine the interrater reliability between a novice and expert rater, as well as the intrarater reliability of the novice rater. Results: The novice rater demonstrated excellent intrarater (ICC [2,k] range = .993–.997) and interrater (ICC [2,k] range = .944–.991) reliability with the expert rater of all femoral articular cartilage average thickness and echo intensity regions. Conclusions: The novel semiautomated average cartilage thickness and echo-intensity assessment is efficient, systematic, and reliable between an expert and novice rater with minimal training. [ABSTRACT FROM AUTHOR]

Gait modification using real-time biofeedback is a conservative intervention associated with positive outcomes. Results from systematic reviews corroborate the effectiveness of various strategies employing real-time biofeedback for reducing estimated knee joint load. The effects on the nonmodified limb, however, remain unclear. Biomechanical changes to the nonmodified limb were investigated during unilaterally implemented medial knee thrust, lateral trunk lean, and toe-in foot progression. Nineteen healthy participants were recruited. Ten trials were completed for each gait condition including baseline. Assigned magnitude for each gait modification strategy was individualized based on the mean and SD of the gait parameter during baseline. Visual real-time biofeedback was provided. During medial knee thrust, participants' nonmodified limb presented with increased: first peak medial knee contact force, internal first peak knee extensor moment, as well as knee- and hip-flexion angles at internal first peak knee extensor moment. Observed biomechanical changes are elucidative of the body's attempt to attenuate increased external loads. These findings may carry significant implications for pathological populations. Load redistribution to the nonmodified side may result in unfavorable long-term outcomes particularly in patients with bilateral diagnosis. Future studies should explore acute and chronic changes in the nonmodified limb of individuals with knee osteoarthritis. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis is an important public health problem and the most common musculoskeletal disease in the World. The pathogenesis and etiology of osteoarthritis is still unclear. We aimed to make an amino acids analysis that will contribute to the pathogenesis, diagnosis and treatment of knee osteoarthritis. Materail and Methods:The study included according to the radiological grading scale of Kellgren-Lawrence, 30 patients at Grade 1-2; Group 1 (Grade 1-2), 30 patients at Grade 3-4; Group 2 (Grade 3-4), 30 healthy controls; Group 3. We compared between groups age, sex, body mass index, Western Ontario and McMaster Universities, Short form-36 findings, and plasma-free amino acid levels. Results:A comparison of the serum norvaline, leucine, isoleucine, allo-isoleucine, cystathionine, phenylalanine, 1-methyl hystidine, arginine, alanine, cystine, valine, threonine, and tryptophane levels of the knee osteoarthritis and control groups compared a statistically significant difference (p=0.001, p=0.001, p=0.001, p=0.001, p=0.044, p=0.003, p=0.001, p=0.035, p=0.010, p=0.011, p=0.001, p=0.001, p=0.003). Conclusions We consider that, norvaline leucine; isoleucine, allo-isoleucine, cystathionine, phenylalanine, 1-methyl hystidine, arginine, alanine, cystine, valine, threonine, and tryptophane amino acids could be, as potential systemic serum biomarkers for diagnosis of knee osteoarthritis. [ABSTRACT FROM AUTHOR]

Background: N6-methyladenosine (m6A) is a universal RNA modification pattern regulated by multiple m6A regulators. In osteoarthritis (OA), m6A regulators influence disease progression by regulating cartilage degradation. However, the function of m6A regulators in synovial tissue remains unclear. In this work, we investigated the biological significance of m6A regulators in osteoarthritic synovitis. Methods: Datasets were acquired from Gene Expression Omnibus. Differential analysis of merged data identified the differentially expressed m6A regulators. Machine learning models were used to evaluate genetic importance. To predict disease risk, a nomogram was constructed based on above m6A regulators. Cluster analysis divided the OA sample into different subgroups. Immune infiltration revealed the immune m6A regulators, which were validated using clinical samples. Eventually, a competing endogenous RNA (ceRNA) network was constructed. Results: We acquired five differentially expressed m6A regulators and a random forest model. The nomogram accurately predicted disease risk. We identified 122 differentially expressed genes between two m6A subgroups. The analysis of immune infiltration showed that YTHDF2 was an immune-related m6A regulator closely related with macrophages. In clinical samples, the protein and mRNA contents of YTHDF2 were consistent with the results of bioinformatic analysis. The ceRNA network based on YTHDF2 revealed 75 lncRNA nodes and 19 miRNA nodes. Conclusion: YTHDF2 has a high diagnostic value in the synovitis of OA and significantly influences the immune status of patients. Hence, YTHDF2, a critical m6A regulator, may provide a biomarker for diagnosis and immune therapy of osteoarthritic synovitis. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (OA) is a chronic degenerative joint disease affecting millions worldwide, particularly those over 60. It is a significant cause of disability and can impact an individual’s quality of life. The condition occurs when the cartilage in the knee joint wears away over time, leading to bone-onbone contact, which can result in pain, stiffness, swelling, and decreased range of motion. Deep neural networks, especially convolutional neural networks (CNN), are powerful tools in medical applications such as diagnosis and detection. This research proposes a CNN model to classify knee osteoarthritis into five categories using x-ray images. These classes are labeled: Minimal, Healthy, Moderate, Doubtful, and Severe. Furthermore, the proposed CNN model has been compared with two pre-trained transfer learning models: Xception and InceptionResNet V2. These models were evaluated based on precision, recall, F1 score, and accuracy. The results showed that although all three models performed very well, the proposed model outperformed both transfer learning models with 98% accuracy. It also achieved the highest values for other parameters such as precision, recall, and F1 score. The proposed model has several potential applications in clinical practice, such as assisting doctors in accurately classifying knee osteoarthritis severity levels by analyzing single X-ray images. [ABSTRACT FROM AUTHOR]

Degenerative osteoarthritis of the knee (KOA) affects the knee compartments and worsens over 10–15 years. Knee osteoarthritis is the major cause of activity restrictions and impairment in older persons. Clinicians' expertise affects visual examination interpretation. Hence, achieving early detection requires fast, accurate, and affordable methods. Deep learning (DL) convolutional neural networks (CNN) are the most accurate knee osteoarthritis diagnosis approach. CNNs require a significant amount of training data. Knee X-rays can be analyzed by models that use deep learning to extract the features and reduce number of training cycles. This study suggests the usage of DL system that is based on a trained network on fiveclass knee X-rays with VGG16, SoftMax (Normal, Doubtful, Mild, Moderate, Severe). Two deep CNNs are used to grade knee OA instantly using the Kellgren-Lawrence (KL) methodology. The experimental analysis makes use of two sets of 1650 different knee X-ray images. Each set consists of 514 normal, 477 doubtful, 232 mild, 221 moderate, and 206 severe cases of osteoarthritis of the knee. The suggested model for knee osteoarthritis (OA) identification and severity prediction using knee X-ray radiographs has a classification accuracy of more than 95%, with training and validation accuracy of 95% and 87%, respectively. [ABSTRACT FROM AUTHOR]

Purpose: The management of infrapatellar fat pad (IPFP) during total knee arthroplasty (TKA) is the subject of ongoing debate. There is conflicting evidence whether IPFP should be preserved or resected during TKA to improve clinical outcomes. This study sought to establish if there is a benefit of one over another in terms of clinical function & patient satisfaction. Material & Methods: Total of 67 patients (96 knees), 11 males & 56 females with a mean age of 66.29 years, who underwent TKA between 2016 and 2020 were included in the study. The planning for IPFP preservation (IPFP-P) on right side (50 knees) & IPFP resection (IPFP-R) on left side (46 knees) was done prospectively. We prospectively evaluated clinical outcomes post-TKA, comparing the right knee to the left knee in terms of IPFP-P vs IPFP-R using the oxford knee scoring (OKS) system. The SF-12 was used to evaluate and compare patient satisfaction between the two groups. Results: The mean OKS in the IPFP-P group was 42.86 ± 2.63 & in the IPFP-R group was 44.22 ± 2.40. The OKS differed significantly between the two groups (p < 0.05). Patient with IPFP-R group had significantly better OKS. The mean Sf-12 (physical & mental component) in the IPFP-P was 51.05 ± 4.15 & 59.29 ± 2.53 & in IPFP-R was 51.23 ± 4.74 & 59.24 ± 2.78, showing no significant differences. Conclusion: Our study shows that IPFP-R gave marginally better patient-reported functional outcome scores (OKS), but there was no difference in patient satisfaction (SF-12 scores) between the two groups. [ABSTRACT FROM AUTHOR]

Objective: This review aimed to systematically analyse the influence of clinical variables, diagnostic parameters and the overall image acquisition process on automation and deep learning in TMJ disorders. Methods: Articles were screened in late 2022 according to a predefined eligibility criteria adhering to the PRISMA protocol. Eligible studies were extracted from databases hosted by MEDLINE, EBSCOHost, Scopus, PubMed and Web of Science. Critical appraisals were performed on individual studies following Nature Medicine's MI‐CLAIM checklist while a combined appraisal of the image acquisition procedures was conducted using Cochrane's GRADE approach. Results: Twenty articles were included for full review following eligibility screening. The average experience possessed by the clinical operators within the eligible studies was 13.7 years. Bone volume, trabecular number and separation, and bone surface‐to‐volume ratio were clinical radiographic parameters while disc shape, signal intensity, fluid collection, joint space narrowing and arthritic changes were successful parameters used in MRI‐based deep machine learning. Entropy was correlated to sclerosis in CBCT and was the most stable radiomic parameter in MRI while contrast was the least stable across thermography and MRI. Adjunct serum and salivary biomarkers, or clinical questionnaires only marginally improved diagnostic outcomes through deep learning. Substantial data was classified as unusable and subsequently discarded owing to a combination of suboptimal image acquisition and data augmentation procedures. Inadequate identification of the participant characteristics and multiple studies utilising the same dataset and data acquisition procedures accounted for serious risks of bias. Conclusion: Deep‐learned models diagnosed osteoarthritis as accurately as clinicians from 2D and 3D radiographs but, in comparison, performed poorly when detecting disc disorders from MRI datasets. Complexities in clinical classification criteria; non‐standardised diagnostic parameters; errors in image acquisition; cognitive, contextual or implicit biases were influential variables that generally affected analyses of inflammatory joint changes and disc disorders. [ABSTRACT FROM AUTHOR]

To identify the impact of kinematic limitations on hand osteoarthritis patients' ability to perform daily living activities. An experiment was performed on 33 patients and 32 healthy subjects. Active ranges of motion (AROM) of 16 hand joint angles were measured, together with scores of different hand tests of dexterity (Box and Block, Nine Hole Peg, Kapandji) and function (Sollerman Hand Function Test, SHFT). Functional ranges of motion (FROM) were recorded during SHFT tasks. Results by task are also reported. Patients' AROM is limited in flexion of thumb carpometacarpal and interphalangeal, and finger metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints, and in palmar arch. Patients scored worse in gross dexterity and opposition, but only Kapandji score was correlated with AROM limitations. Pain is mostly reported in patients with limited extension of finger MCP and PIP joints. Patients used significantly different FROM in almost all the joints, and needed more time to accomplish the SHFT tasks. AROM measurements can be used as indicators for early diagnosis. Patients use specific strategies to accomplish each task, arising from AROM limitations; some tasks with very extreme postures. The tasks where precision or force are required for thumb are the most affected ones. Active range of motion is an indicator for early hand osteoarthritis diagnosis. Patients' functional ranges are reduced, and thumb opposition and gross dexterity are hindered. Rehabilitation should focus especially on tasks requiring precision and thumb strength. Rehabilitation should favor the improvement of task completion times. [ABSTRACT FROM AUTHOR]

Physical function decline due to aging or disease can be assessed with quantitative motion analysis, but this currently requires expensive laboratory equipment. We introduce a self-guided quantitative motion analysis of the widely used five-repetition sit-to-stand test using a smartphone. Across 35 US states, 405 participants recorded a video performing the test in their homes. We found that the quantitative movement parameters extracted from the smartphone videos were related to a diagnosis of osteoarthritis, physical and mental health, body mass index, age, and ethnicity and race. Our findings demonstrate that at-home movement analysis goes beyond established clinical metrics to provide objective and inexpensive digital outcome metrics for nationwide studies. [ABSTRACT FROM AUTHOR]

Assessing co-activation characteristics in knee osteoarthritis (knee OA) using method of quantification of the activity ratio (such as the co-contraction index (CCI) or the directed co-activation ratios (DCAR)) for surface electromyography (EMG) has been reported. However, no studies have discussed the differences in results between non-negative matrix factorization (NNMF) and the DCAR. Does DCAR or NNMF reflect the characteristic co-activation pattern of knee OA while using EMG normalized by the peak dynamic method? Ten elderly control participants (EC) and ten knee OA patients (KOA) volunteered to participate in this study. EMG data from 20 participants were obtained from our previous study. Patients with knee OA were recruited from a local orthopedic clinic. The DCAR of agonist and antagonist muscles and the number of modules using NNMF were calculated to evaluate multiple muscle co-activations. An independent t-test statistical parametric mapping approach was used to compare the DCAR between the two groups. The difference in the number of modules between EC and KOA was evaluated using the Wilcoxon rank-sum test. There was no significant difference in the DCAR between the two groups. However, NNMF had significantly fewer modules with KOA than with EC. The NNMF with the ratio of the amplitude of each muscle and duration of activity as variables reflected the co-activation of KOA, characterized by the high synchronous and prolonged activity of each muscle. Therefore, the NNMF is suitable for extracting characteristic muscle activity patterns of knee OA independent of the normalization method. • The non-negative matrix factorization showed knee osteoarthritis' characteristics. • No difference in the directed co-activation ratios between the two groups. • Non-negative matrix factorization extracts characteristic muscle activity patterns. [ABSTRACT FROM AUTHOR]

Background: We hypothesized the risk of prosthetic instability would be similar in patients treated for femoral neck fracture (FNF) comparatively to patients treated for osteoarthritis (OA) using a dual-mobility cup—total hip arthroplasty (DMC-THA). Methods: One hundred and nine patients (109 hips), with a mean age of 69.6 ± 3 years (65–75 years), underwent DMC-THA using a posterolateral approach for a FNF (study group). Patients were matched for age and sex with 109 OA patients treated by the same procedure (control). Results: Postoperatively, there were four dislocations of the large articulation in the study group, and none in the control (p = 0.12). Periprosthetic joint infection (PJI) was the most common surgical complication and was more frequent in the study group. Postoperatively, functional results were at least good in > 90% patients in both groups. At the last follow-up, patients in the study group had recovered autonomy and physical activity comparable to those of the preoperative period. Conclusion: We found no increased risk of dislocation after DMC-THA using a posterolateral approach for FNF as compared to the same procedure for OA. In this study, function and independence were restored postoperatively in the vast majority of patients. DMC-THA appears as a viable therapeutic option in autonomous patients with a displaced FNF. [ABSTRACT FROM AUTHOR]

Total knee arthroplasty is an effective way to manage osteoarthritis patients surgically. However, patients may encounter post-surgical complications, such as quadriceps rupture in rare instances, in addition to surgical complications. In our clinical practice, we encountered a 67-year-old Saudi male patient with a rare bilateral quadriceps rupture two weeks post-total knee arthroplasty. The cause of the bilateral rupture was a history of falls in both knees. The patient was reported to our clinic with clinical features like pain in the knee joint, immobility, and bilateral swelling in the knees. The X-ray did not show any periprosthetic fracture, but an ultrasound of the anterior thigh revealed a complete cut of the quadriceps tendon on both sides. The repair of the bilateral quadriceps tendon was done by direct repair using the Kessler technique and was reinforced with fiber tape. Following knee immobilization for six weeks, the patient began intensive physical therapy management to decrease pain, enhance muscle strength, and increase range of motion. After rehabilitation, the patient regained a complete range of motion in the knee and improved functionality, and he could walk independently without crutches. [ABSTRACT FROM AUTHOR]

Context: Knee osteoarthritis (OA) frequently develops following knee injury/surgery. It is accepted that knee injury/surgery precipitates OA with previous studies examining this link in terms of years after injury/surgery. However, postinjury OA prevalence has not been examined by decade of life; thereby, limiting our understanding of the age at which patients are diagnosed with posttraumatic knee OA. Objective: Evaluate the association between the knee injury and/or surgical history, present age, and history of receiving a diagnosis of knee OA. Design: Cross-sectional survey. Setting: Online survey. Participants: A total of 3660 adults were recruited through ResearchMatch©. Of these, 1723 (47.1%) were included for analysis due to history of (1) knee surgery (SURG: n = 276; age = 53.8 [15.3] y; and body mass index [BM1]=29.9 [8.0] kg/m²), (2) nonsurgical knee injury (INJ: n = 449; age = 46.0 [15.61 y; and BMI = 27.5 [6.9] kg/m²), or (3) no knee injury (CTRL: n = 998; age = 44.0 [25.2] y; and BMI = 26.9 [6.6] kg/m2). Respondents were subdivided by decade of life (20-29 through 70+). Intervention: An electronic survey regarding knee injury history, treatment, and diagnosis of knee OA. Main Outcome Measures: Binary logistic regression determined the association between knee surgical status and OA by decade of life. Participants with no histories of OA or lower-extremity injury were the referent categories. BMI was a covariate in all analyses. Results: SURG respondents were more likely to report having knee OA than CTRL for all age groups (odds ratios: 11.43-53.03; P< .001). INJ respondents aged 30 years and older were more likely to have OA than CTRL (odds ratios: 2.99-14.22; P< .04). BMI influenced associations for respondents in their 50s (P = .001) and 60s (P < .001) only. Conclusions: INJ increased the odds of reporting a physician diagnosis of knee OA in adults as young as 30 to 39 years. Importantly, SURG yielded 3 to 4 times greater odds of being diagnosed with knee OA compared with INJ in adults as young as 20 to 29 years. Delaying disease onset in these young adults is imperative to optimize the quality of life long term after surgery. [ABSTRACT FROM AUTHOR]

Context: Individuals who sustain a knee surgery have been shown to have an increased likelihood to develop osteoarthritis (OA). Objective: Identify the consequences of knee surgery in a cohort of former college athletes. Design: Cross-sectional. Setting: Research laboratory. Participants: A group of 100 former Division I college athletes aged 40–65 years (60 males and 40 females) participated in the study. Interventions: All individuals self-reported whether they sustained a knee injury during college requiring surgery and if they have been diagnosed with knee OA by a medical physician post knee injury. Individuals were categorized into 3 groups: no history of knee injury requiring surgery (33 males and 24 females; 54.53 [5.95] y), history of knee surgery in college with no diagnosis of OA later in life (4 males and 6 females; 51.26 [7.29] y), and history of knee surgery in college with physician diagnosed OA later in life (23 males and 10 females; 54.21 [7.64] y). All individuals completed the knee injury and osteoarthritis outcome score (KOOS) and short form-36 version 2. Main Outcome Measures: Scores on the KOOS and short form-36 version 2. Results: A majority (76.7%) of individuals who had a knee surgery in college did develop OA. The largest mean differences were between the healthy knee and surgical knee/OA groups on the KOOS-quality of life scale (mean difference: 49.76; χ2(3) = 44.65; P <.001) and KOOS-sports scale (mean difference: 43.69; χ2(3) = 28.69; P <.001), with the surgical knee/OA group scoring worse. Conclusions: Later in life functional limitations were observed in individuals who sustained a knee injury requiring surgery and developed OA. These findings support increased efforts toward prevention of knee injuries and consideration of the long-term implication when making treatment and return to activity decisions. [ABSTRACT FROM AUTHOR]

Background: The incidence of osteoarthritis (OA) is high in patients suffering from 25-hydroxyvitamin D3 (25(OH)D) deficiency. The goal of this study is to examine the association between saliva and serum 25(OH)D and knee OA. Methods: Serum and saliva 25(OH)D levels of 30 patients with knee OA and 30 matched healthy people in a control group were measured by ELISA. Knee pain was assessed by Western Ontario and McMaster Universities Arthritis Index (WOMAC). Data were analyzed through student's t-test, Pearson correlation test and receiver operating characteristic (ROC). Results: The mean serum and saliva 25(OH)D levels were lower in knee OA group than the healthy group. WOMAC negatively correlated with serum (r = -0.37; P = 0.02) and with unstimulated (r = -0.30; P = 0.04) saliva 25(OH)D. The unstimulated saliva 25(OH)D cutoff value was 27.8 pg/m1 regarding the diagnosis of knee OA. Conclusion: Serum 25(OH)D levels were positively associated with saliva 25(OH)D, and 25(OH)D level in saliva, as in serum, was low in knee OA. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis is the most common form of arthritis worldwide. Growth Differentiation Factor 5 (GDF5) is closely related to bone morphogenic protein, and the defect in this gene might be correlated to the abnormal joint development. The aim of this study was to detect GDF5 gene polymorphism in osteoarthritis (OA) Egyptian population and its association with OA severity. Methods: A case-control study was conducted in the Rheumatology and Rehabilitation Department, Zagazig University Hospitals. This study included 195 patients with primary OA, and 195 healthy control subjects. They were subjected to history taking, clinical examination and laboratory investigations including CBC, ESR, CRP, RF, lipid profile. Detection of GDF5 gene polymorphism was performed using PCR RFLP (restriction fragment length polymorphism) technique. Assessment of OA severity was assessed using WOMAC and Lequesne index of severity for knee and hip OA, AUSCUAN questionnaire for hand OA and K/L radiological grading of knee OA. Results: The study showed statistically significant difference between OA patients and control groups regarding genotype frequency of GDF5 gene polymorphism. No statistically significant association between total WOMAC index, Lequesne index in OA hip and knee and AUSCUAN questionnaire for hands OA with different genotype of GDF5. However, TC genotype was higher as compared to patients with TT and CC genotypes. There was a statistically significant association between different GDF5 genotypes and K/L radiological grading of knee OA. Conclusions: This study revealed a genetic association between GDF5, and primary OA and TC genotype may be associated with more severe and disability of primary OA. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM), among other modern diseases, certainly occupies a leading position today. Moreover, the comorbid combination of T2DM and osteoarthritis (OA) is a common example of the development of complications, disability and mortality these days. As you know, T2DM develops against the background of obesity and overweight. A significant group of patients prone to these pathologies are peri- and postmenopausal women. Estrogen deficiency during this period worsens the course of these interdependent conditions. Therefore, searching for ways of early diagnosis of OA in women with T2DM is an urgent task of internal medicine. Purpose. To determine the interdependencies of indicators of glucose metabolism and lipid metabolism with levels of visfatin (VF) and homocysteine (HC) in women with T2DM and OA in peri- and postmenopause. Methods. For the study, 120 thematic women in peri- and postmenopause were selected and were divided into three groups: 1st (n = 20) -- women with a mono-course of T2DM; 2nd (n = 20) -- with a mono-course of OA; 3rd (n = 64) -- with the comorbid course of T2DM and OA. The control group (CG) consisted of 16 relatively healthy women of the appropriate age. Diagnoses of T2DM and OA were established in accordance with the current Orders of the Ministry of Health of Ukraine. Laboratory diagnostic methods were performed according to standard methods. The results. According to the obtained results, the presence of interdependencies of all indicators of lipid metabolism and glucose-insulin metabolism was reliably determined, which indicated direct correlations (mostly of moderate strength) with the levels of HC and VF in the blood of the examinees, most pronounced in the comorbid combination of T2DM and OA in peri- and postmenopause. Conclusions. In work, an analysis of the correlations of indicators of glucose metabolism and lipid metabolism with disturbances in the metabolism of VF and HC in women with T2DM and OA in pre- and postmenopause was carried out, and their interdependent relationships were determined. The highest levels of VF and HC were recorded in the group of women with comorbid pathology of T2DM and OA. [ABSTRACT FROM AUTHOR]

Background: Degenerative joint disease (DJD) of the temporomandibular joint (TMJ) is an important type of temporomandibular disorders (TMDs) potentially leading to orofacial pain and jaw dysfunction. Magnetic resonance imaging (MRI) is important in TMD diagnosis; however, its diagnostic ability for DJD remains unknown. Objective: To explore the utility of MRI in diagnosing DJD according to the latest diagnostic criteria for TMD and detecting condylar bone abnormalities and their severity. Methods: Overall, 122 participants were examined using cone‐beam computed tomography (CBCT) and MRI. The sensitivity, specificity and accuracy of MRI for detecting DJD and different types of TMJ condylar bone abnormalities were calculated (considering CBCT as gold standard); in addition, we tested MRI and CBCT's consistency in scoring five types of condylar bone abnormalities. Results: The sensitivity and specificity of MRI for DJD were 95.3% and 43.1%, respectively. The MRI sensitivities for condylar flattening, erosion, osteophytes, sclerosis and cysts were 98.6%, 96.2%, 79.4%, 50%, and 79.2% (specificity, 53.6%, 48.3%, 81.6%, 83.3%, and 88.2%, respectively), respectively. The consistency between MRI and CBCT in assessing the severity of condylar bone abnormalities was fair‐to‐moderate (kappa coefficient: 0.278–0.491). The inter‐observer consistency for CBCT was good, whereas for MRI, it was relatively poor. Conclusion: MRI can detect DJD and condylar bone abnormalities. However, MRI could not efficiently detect the severity of condylar bone abnormalities. [ABSTRACT FROM AUTHOR]

Osteoa rthritis is a heterogeneous group of diseases with typical joint damage by articular cartilage damage, along with degenerative changes in the epiphyses of the bones adjacent to the affected joint and the synovial joint. Osteoarthritis affects people over 50 years, but the onset is much earlier if an occupational factor is present. Due to the high prevalence, osteoarthritis is the leading cause of presentation to primary care, either in the family or occupational medicine. Osteoarthritis also poses an economic burden to society due to direct costs such as the price paid for a visit to the doctor, the cost of drugs or implanted prostheses, or indirect costs generated by decreased fitness for a job or even disability. The occupational medicine specialist has the leading role in primary and secondary prevention in this sense. The collaboration between the family doctor – rehabilitation and occupational physician has the function of tertiary prevention maintaining the quality of life and socioeconomic life in case of these types of patients. [ABSTRACT FROM AUTHOR]

Abnormal loading of the knee joint contributes to the pathogenesis of knee osteoarthritis. Gait retraining is a noninvasive intervention that aims to reduce knee loads by providing audible, visual, or haptic feedback of gait parameters. The computational expense of joint contact force prediction has limited real-time feedback to surrogate measures of the contact force, such as the knee adduction moment. We developed a method to predict knee joint contact forces using motion analysis and a statistical regression model that can be implemented in near real-time. Gait waveform variables were deconstructed using principal component analysis, and a linear regression was used to predict the principal component scores of the contact force waveforms. Knee joint contact force waveforms were reconstructed using the predicted scores. We tested our method using a heterogenous population of asymptomatic controls and subjects with knee osteoarthritis. The reconstructed contact force waveforms had mean (SD) root mean square differences of 0.17 (0.05) bodyweight compared with the contact forces predicted by a musculoskeletal model. Our method successfully predicted subject-specific shape features of contact force waveforms and is a potentially powerful tool in biofeedback and clinical gait analysis. [ABSTRACT FROM AUTHOR]

The aim of functional diagnostic is detection of the arthrosis, the classification of the structural findings, functional disturbance and the restriction of the patient. Helpful is the ICD - Classification with the assessment of the ICF - Classification for judging the activity and participation of the patient with instruments for self or external assessments. The clinical symptoms are related by stage. The most importand symptom is pain, related by rest and strain, a look out for the persistent pain at night. The functional clinical investigation includes investigation of the joint movement and course, evaluation of the soft tissue function, joint compartments as well as ligaments and meniscus. Joint stress tests are helpful to detect function disturbences and are helpful in the way of diagnostic. A manual investigation completes the assessment of functional disturbences, local in the joint structures and the functional chains. In addition of the investigation we need imagings (x-ray, sonography) corresponding guidelines. To evaluate the restriction of functional severity and restriction are assessments so well as generic health (SF 36), specific health (WOMAC) and pain (VAS) helpful. The focus in the diagnosis procedure assess pain, functional loss and other symptoms as well as the medication. Non-surgical or conservative measures includes different treatment steps. Adaption of lifestyle and avoidance of stress for movement system is the first action followed by helpful sports activities. In relation of the personal specific situation of the patient an instruction of the doctor is helpful. Physical treatments reduce functional restrictions, pain and also optimize muscular actions and processes for regulation. For that is used technics from thermotherapy included cryotherapy, electrotherapy, phytotherapy, tape, orthosis and technical aids, technics from complementary medicine. Physiotherapy as a "joint school", mobilization, muscle strengthening, optimize of tension and coordination are best to use. The best evidence and recommendation are sports and training, physiotherapy and manual medicine, recommended by the international societies, always related to the local clinical findings. Different examples for therapy of gonarthrosis are showed in the article. Orthopedic aids are helpful. Complaints of arthosis have slow progression overall groups. Every single person is different. We are able to influence predictors often with the conservative therapy - most of all pain and function. The therapy is as good as long as we need other therapy and surgical intervention. [ABSTRACT FROM AUTHOR]

The prevalence of joint complaints in the general population is very high. These diseases are therefore considered a so-called lifestyle disease. With increasing age, the arthrosis rate increases and in a large proportion of aging patients joint complaints are caused by arthrosis. Before planning therapy, an individual analysis of the symptoms (pain, functional impairment and reduction in quality of life) in combination with an exact clinical examination and a meaningful application of further, usually radiological procedures is always required in each individual case. The synopsis of these various diagnostic components then prescribes an arthrosis stage in most cases, which then prescribes the individual therapy planning. However, it must be clear to the practitioner that there is no diagnostic gold standard for the diagnosis of this very complex clinical picture. Again and again, it must be warned to overestimate paraclinical findings and thus provoke overtreatment. In recent years, the paraclinical methods have been refined and in the future further improvements in the diagnostic possibility of the clinical picture of osteoarthritis are to be expected. [ABSTRACT FROM AUTHOR]