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Background: Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores the use of GANs to generate synthetic sagittal radiographs from coronal views in AIS patients., Methods: A dataset of 3,935 AIS patients who underwent spine and pelvis radiographic examinations using the EOS system, which simultaneously acquires coronal and sagittal images, was analyzed. The dataset was divided into training-set (85%, n = 3,356) and test-set (15%, n = 579). GAN model was trained to generate sagittal images from coronal views, with real sagittal views as reference standard. To assess accuracy, 100 subjects from the test-set were randomly selected for manual measurement of lumbar lordosis (LL), sacral slope (SS), pelvic incidence (PI), and sagittal vertical axis (SVA) by two radiologists in both synthetic and real images., Results: Sixty-nine synthetic images were considered assessable. The intraclass correlation coefficient ranged 0.93-0.99 for measurements in real images, and from 0.83 to 0.88 for synthetic images. Correlations between parameters of real and synthetic images were 0.52 (LL), 0.17 (SS), 0.18 (PI), and 0.74 (SVA). Measurement errors showed minimal correlation with scoliosis severity. Mean ± standard deviation absolute errors were 7 ± 7° (LL), 9 ± 7° (SS), 9 ± 8° (PI), and 1.1 ± 0.8 cm (SVA)., Conclusion: While the model generates sagittal images visually consistent with reference images, their quality is not sufficient for clinical parameter assessment, except for promising results in SVA., Relevance Statement: AI can generate synthetic sagittal radiographs from coronal views to reduce radiation exposure in monitoring adolescent idiopathic scoliosis (AIS). However, while these synthetic images appear visually consistent with real ones, their quality remains insufficient for accurate clinical assessment., Key Points: AI can be exploited to generate synthetic sagittal radiographs from coronal views. Dataset of 3,935 subjects was used to train and test AI-model; spinal parameters from synthetic and real images were compared. Synthetic images were visually consistent with real ones, but quality was generally insufficient for accurate clinical assessment., Competing Interests: Declarations. Ethics approval and consent to participate: This retrospective study, based on anonymized data, received approval from the Local Ethics Committee of San Raffaele Hospital, Milan, Italy (protocol code: RETRORAD; registry number: 61/int/2017). Consent for publication: Not applicable. Competing interests: FG and LMS are members of the Scientific Editorial Board (section: musculoskeletal) for European Radiology Experimental. As such, they did not participate in the selection nor review processes for this article. The remaining authors declare that they have no conflict of interest., (© 2025. The Author(s).)

Introduction: The Minimal Clinically Important Change (MCIC) is used in conjunction with Patient-Reported Outcome Measures (PROMs) to determine the clinical relevance of changes in health status. MCIC measures a change within the same person or group over time. This study aims to evaluate the variability in computing MCIC for the Core Outcome Measure Index (COMI) using different methods., Methods: Data from a spine centre in Switzerland were used to evaluate variations in MCIC for the COMI score. Distribution-based and anchor-based methods (predictive and nonpredictive) were applied. Bayesian bootstrap estimated confidence intervals., Results: From 27,003 cases, 9821 met the inclusion criteria. Distribution-based methods yielded MCIC values from 0.4 to 1.4. Anchor-based methods showed more variability, with MCIC values from 1.5 to 4.9. Predictive anchor-based methods also provided variable MCIC values for improvement (0.3-2.4), with high sensitivity and specificity., Discussion: MCIC calculation methods produce varying values, emphasizing careful method selection. Distribution-based methods likely measure minimal detectable change, while non-predictive anchor-based methods can yield high MCIC values due to group averaging. Predictive anchor-based methods offer more stable and clinically relevant MCIC values for improvement but are affected by prevalence and reliability corrections., Competing Interests: Declarations. Conflict of interest: The authors declare that they have no competing interests. Ethical Approval: Ethics approval and consent to participate. The study was performed in accordance with the Declaration of Helsinki and was approved by ethics (KEK-ZH-2023-01683). Patients gave their written informed consent before being included in the database. IRB: Ethics committee approval, KEK-ZH-2023-01683, (© 2024. The Author(s).)

Purpose: Our objective was to analysis the barycentremetry, obtained from the external envelope reconstruction of biplanar radiographs, in adolescent idiopathic scoliosis (AIS) and to determine whether assessing would help predict the distinction between progressive and stable AIS at the early stage., Methods: A retrospective study with a multicentre cohort of 205 AIS was conducted. All AIS underwent a biplanar X-ray between 2013 and 2020. Inclusion criteria were Cobb angle between 10° and 25°; Risser sign lower than 3; age higher than 10 years; and no previous treatment. A 3D spine reconstruction was performed, and the barycentremetry parameters were computed, i.e., the center of mass position at the apex and the axial torque at the apex, the upper and lower junction. A severity index, helping to distinguish stable and progressive AIS, was computed on the first radiograph, and weighted according to these parameters. A clinical and radiographic monitoring determined if AIS were classified such a stable or progressive scoliosis., Results: One hundred and sixty-two AIS were included (i.e., 87 were classified as stable and 75 as progressive). The apex center of mass position was different between the stable and progressive AIS groups (6 mm, SD = 4 mm for the whole cohort; 5 mm, SD = 4 mm for stable AIS versus 7 mm, SD = 4 mm for progressive AIS; p = 0.02). In AIS thoracic, the specificity and positive predictive value of the severity index increased by 19% and 16%, respectively, by adding the apex vertebral axial torque., Conclusion: Early assessment of the external envelope from biplanar X-ray reconstruction of idiopathic scoliosis showed that the apex centre of mass position was significantly different between progressive and stable scoliosis. The inclusion of the axial torque of the apex vertebra in the severity index is promising to help the clinician distinguish between stable and progressive thoracic AIS at an early stage., Level of Evidence: II - Prognostic studies., (© 2024. The Author(s), under exclusive licence to Scoliosis Research Society.)

Purpose: Large language models (LLM) have the potential to bridge knowledge gaps in patient education and enrich patient-surgeon interactions. This study evaluated three chatbots for delivering empathetic and precise adolescent idiopathic scoliosis (AIS) related information and management advice. Specifically, we assessed the accuracy, clarity, and relevance of the information provided, aiming to determine the effectiveness of LLMs in addressing common patient queries and enhancing their understanding of AIS., Methods: We sourced 20 webpages for the top frequently asked questions (FAQs) about AIS and formulated 10 critical questions based on them. Three advanced LLMs-ChatGPT 3.5, ChatGPT 4.0, and Google Bard-were selected to answer these questions, with responses limited to 200 words. The LLMs' responses were evaluated by a blinded group of experienced deformity surgeons (members of the European Spine Study Group) from seven European spine centers. A pre-established 4-level rating system from excellent to unsatisfactory was used with a further rating for clarity, comprehensiveness, and empathy on the 5-point Likert scale. If not rated 'excellent', the raters were asked to report the reasons for their decision for each question. Lastly, raters were asked for their opinion towards AI in healthcare in general in six questions., Results: The responses among all LLMs were 'excellent' in 26% of responses, with ChatGPT-4.0 leading (39%), followed by Bard (17%). ChatGPT-4.0 was rated superior to Bard and ChatGPT 3.5 (p = 0.003). Discrepancies among raters were significant (p < 0.0001), questioning inter-rater reliability. No substantial differences were noted in answer distribution by question (p = 0.43). The answers on diagnosis (Q2) and causes (Q4) of AIS were top-rated. The most dissatisfaction was seen in the answers regarding definitions (Q1) and long-term results (Q7). Exhaustiveness, clarity, empathy, and length of the answers were positively rated (> 3.0 on 5.0) and did not demonstrate any differences among LLMs. However, GPT-3.5 struggled with language suitability and empathy, while Bard's responses were overly detailed and less empathetic. Overall, raters found that 9% of answers were off-topic and 22% contained clear mistakes., Conclusion: Our study offers crucial insights into the strengths and weaknesses of current LLMs in AIS patient and parent education, highlighting the promise of advancements like ChatGPT-4.o and Gemini alongside the need for continuous improvement in empathy, contextual understanding, and language appropriateness., (© 2024. The Author(s).)

Purpose: Radiation-free systems based on dorsal surface topography can potentially represent an alternative to radiographic examination for early screening of scoliosis, based on the ability of recognizing the presence of deformity or classifying its severity. This study aims to assess the effectiveness of a deep learning model based on convolutional neural networks in directly predicting the Cobb angle from rasterstereographic images of the back surface in subjects with adolescent idiopathic scoliosis., Methods: Two datasets, comprising a total of 900 individuals, were utilized for model training (720 samples) and testing (180). Rasterstereographic scans were performed using the Formetric4D device. The true Cobb angle was obtained from radiographic examination. The best model configuration was identified by comparing different network architectures and hyperparameters through cross-validation in the training set. The performance of the developed model in predicting the Cobb angle was assessed on the test set. The accuracy in classifying scoliosis severity (non-scoliotic, mild, and moderate category) based on Cobb angle was evaluated as well., Results: The mean absolute error in predicting the Cobb angle was 6.1° ± 5.0°. Moderate correlation (r = 0.68) and a root-mean-square error of 8° between the predicted and true values was reported. The overall accuracy in classifying scoliosis severity was 59%., Conclusion: Despite some improvement over previous approaches that relied on spine shape reconstruction, the performance of the present fully automatic application is below that of radiographic evaluation performed by human operators. The study confirms that rasterstereography cannot be considered a valid non-invasive alternative to radiographic examination for clinical purposes., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol., Methods: A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than - 1 or (3) lower than - 2.5. The models took as inputs either the images or radiomics features derived from them, alone or in combination with metadata (age, sex, body size, vertebral level, parameters of the imaging protocol)., Results: The best-performing models achieved mean absolute errors of 0.15-0.16 g/cm 2 for the direct estimation of bone mineral density, and areas under the receiver operating characteristic curve of 0.82 (MRIs) - 0.80 (radiographs) for the classification of T-scores lower than - 1, and 0.80 (MRIs) - 0.65 (radiographs) for T-scores lower than - 2.5., Conclusions: The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Accurate identification of local changes in the biomechanical properties of the normal and degenerative meniscus is critical to better understand knee joint osteoarthritis onset and progression. Ex-vivo material characterization is typically performed on specimens obtained from different locations, compromising the tissue's structural integrity and thus altering its mechanical behavior. Therefore, the aim of this in-silico study was to establish a non-invasive method to determine the region-specific material properties of the degenerated human meniscus. In a previous experimental magnetic resonance imaging (MRI) study, the spatial displacement of the meniscus and its root attachments in mildly degenerated (n = 12) and severely degenerated (n = 12) cadaveric knee joints was determined under controlled subject-specific axial joint loading. To simulate the experimental response of the lateral and medial menisci, individual finite element models were created utilizing a transverse isotropic hyper-poroelastic constitutive material formulation. The superficial displacements were applied to the individual models to calculate the femoral reaction force in an inverse finite element analysis. During particle swarm optimization, the four most sensitive material parameters were varied to minimize the error between the femoral reaction force and the force applied in the MRI loading experiment. Individual global and regional parameter sets were identified. In addition to in-depth model verification, prediction errors were determined to quantify the reliability of the identified parameter sets. Both compressibility of the solid meniscus matrix (+141 %, p ≤ 0.04) and hydraulic permeability (+53 %, p ≤ 0.04) were significantly increased in the menisci of severely degenerated knees compared to mildly degenerated knees, irrespective of the meniscus region. By contrast, tensile and shear properties were unaffected by progressive knee joint degeneration. Overall, the optimization procedure resulted in reliable and robust parameter sets, as evidenced by mean prediction errors of <1 %. In conclusion, the proposed approach demonstrated high potential for application in clinical practice, where it might provide a non-invasive diagnostic tool for the early detection of osteoarthritic changes within the knee joint., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Study Design: Heterogeneous data collection via a mix of prospective, retrospective, and ambispective methods., Objective: To evaluate the effect of biological sex on patient-reported outcomes after spinal fusion surgery for lumbar degenerative disease., Summary of Background Data: Current literature suggests sex differences regarding clinical outcome after spine surgery may exist. Substantial methodological heterogeneity and limited comparability of studies warrants further investigation of sex-related differences in treatment outcomes., Methods: We analyzed patients who underwent spinal fusion with or without pedicle screw insertion for lumbar degenerative disease included within a multinational study, comprising patients from 11 centers in 7 countries. Absolute values and change scores (change from pe-operative baseline to post-operative follow-up) for 12-month functional impairment (Oswestry disability index [ODI]) and back and leg pain severity (numeric rating scale [NRS]) were compared between male and female patients. Minimum clinically important difference (MCID) was defined as > 30% improvement., Results: Six-hundred-sixty (59%) of 1115 included patients were female. Female patients presented with significantly baseline ODI (51.5 ± 17.2 vs. 47.8 ± 17.9, P<0.001) and back pain (6.96 ± 2.32 vs. 6.60 ± 2.30, P=0.010) and leg pain (6.49 ± 2.76 vs. 6.01 ± 2.76, P=0.005). At 12-months, female patients still reported significantly higher ODI (22.76 ± 16.97 vs. 20.50 ± 16.10, P=0.025), but not higher back (3.13 ± 2.38 vs. 3.00 ± 2.40, P=0.355) or leg pain (2.62 ± 2.55 vs. .34 ± 2.43, P=0.060). Change scores at 12 months did not differ significantly among male and female patients in ODI (∆ 1.31, 95% CI -3.88-1.25, P=0.315), back (∆ 0.22, 95% CI -0.57-0.12, P=0.197) and leg pain (∆ 0.16, 95% CI -0.56-0.24, P=0.439). MCID at 12-months was achieved in 330 (77.5%) male patients and 481 (76.3%) female patients (P=0.729) for ODI., Conclusion: Both sexes experienced a similar benefit from surgery in terms of relative improvement in scores for functional impairment and pain. Although female patients reported a higher degree of functional impairment and pain preoperatively, at 12 months only their average scores for functional impairment remained higher than those for their male counterparts, while absolute pain scores were similar for female and male patients., Competing Interests: Conflict of Interest: The authors declare that the article and its content were composed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Introduction: Epimuscular fat (EF) has rarely been studied in the context of low back pain (LBP)., Research Question: This study aims to assess the presence and extent of EF in the lumbar muscles and its association with vertebral level in patients with low back disorders and to explore correlations between EF, demographics, BMI, and LBP., Material and Methods: T2 axial MRIs from L1 to L5 were manually segmented to analyze the cross-sectional area (CSA) of EF (mm 2 ), and fat infiltration (FI,%) of 40 patients (23 females, 17 males; mean age:65.9 years) with lumbar degenerative pathologies awaiting a surgical procedure. COMI, LBP, demographic, and clinical data were extracted from the institutional registry. Statistical analyses included Wilcoxon and Mann-Whitney tests for differences in EF between sides and sexes, the Friedman test for EF size differences among lumbar levels, and Spearman's correlation for associations, adjusted for BMI, age, and sex., Results: EF was found in 77.5% of subjects at L1, 92.5% at L2, 100% at L3 and L4, and 95.0% at L5. EF was significantly larger at L4 (253.1 ± 183.6 mm 2 ) and L5 (220.2 ± 194.9 mm 2 ) than at L1 (36.1 ± 37.8 mm 2 ) and L2 (72.2 ± 84.4 mm 2 ). No significant EF differences were found between sides and sexes. EF correlated strongly with BMI (r s  = 0.65,p < 0.001) and moderately with FI (r s  = 0.31,p = 0.04), though its correlation with FI was not significant after adjustment. EF did not correlate with COMI scores but correlated with LBP in the adjusted analysis (r s :0.31,p = 0.04)., Discussion and Conclusion: EF is present across all lumbar levels, with higher concentrations at L4 and L5, and a significant correlation between EF and LBP intensity was observed. The present findings are limited to a specific subset of patients with lumbar degenerative disorders who are awaiting surgical procedures., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP)., Methods: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer-Lemeshow (HL), AUC, sensitivity and specificity., Results: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing., Conclusions: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting., (© 2024. The Author(s).)

Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD through biomechanical simulations. However, the process of generating personalised FE models is often complex and time-consuming. To address this challenge, we present a dataset of FE models with diverse spine morphologies that statistically represent real geometries from a cohort of patients. These models are generated using EOS images, which are utilized to reconstruct 3D surface spine models. Subsequently, a Statistical Shape Model (SSM) is constructed, enabling the adaptation of a FE hexahedral mesh template for both the bone and soft tissues of the spine through mesh morphing. The SSM deformation fields facilitate the personalization of the mean hexahedral FE model based on sagittal balance measurements. Ultimately, this new hexahedral SSM tool offers a means to generate a virtual cohort of 16807 thoracolumbar FE spine models, which are openly shared in a public repository., (© 2024. The Author(s).)

Machine learning (ML), a subset of artificial intelligence, is crucial for spine care and research due to its ability to improve treatment selection and outcomes, leveraging the vast amounts of data generated in health care for more accurate diagnoses and decision support. ML's potential in spine care is particularly notable in radiological image analysis, including the localization and labeling of anatomical structures, detection and classification of radiological findings, and prediction of clinical outcomes, thereby paving the way for personalized medicine. The manuscript discusses ML's application in spine care, detailing supervised and unsupervised learning, regression, classification, and clustering, and highlights the importance of both internal and external validation in assessing ML model performance. Several ML algorithms such as linear models, support vector machines, decision trees, neural networks, and deep convolutional neural networks, can be used in the spine domain to analyze diverse data types (visual, tabular, omics, and multimodal).

Study Design: Basic science (finite element analysis)., Objectives: Pedicle subtraction osteotomy (PSO) at L5 is an effective treatment for sagittal imbalance, especially in select cases of patients showing kyphosis with the apex at L4-L5 but has been scarcely investigated. The aim of this study was to simulate various "high-demand" instrumentation approaches, including varying numbers of rods and sacropelvic implants, for the stabilization of a PSO at L5., Methods: A finite element model of T10-pelvis was modified to simulate posterior fixation with pedicle screws and rods from T10 to S1, alone or in combination with an L5 PSO. Five additional configurations were then created by employing rods and novel porous fusion/fixation implants across the sacroiliac joints, in varying numbers. All models were loaded using pure moments of 7.5 Nm in flexion-extension, lateral bending, and axial rotation., Results: The osteotomy resulted in a general increase in motion and stresses in posterior rods and S1 pedicle screws. When the number of rods was varied, three- and four-rod configurations were effective in limiting the maximal rod stresses; values approached those of posterior fixation with no osteotomy. Maximum stresses in the accessory rods were similar to or less than those observed in the primary rods. Multiple sacropelvic implants were effective in reducing range of motion, particularly of the SIJ., Conclusions: Multi-rod constructs and sacropelvic fixation generally reduced maximal implant stresses and motion in comparison with standard posterior fixation, suggesting a reduced risk of rod breakage and increased joint stability, respectively, when a high-demand construct is utilized for the correction of sagittal imbalance., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RDC, DPL, and SAY are employees of and have stock/stock options in SI-BONE, Inc. DWP is a consultant of SI-BONE, Inc. FG received funding support for this study from SI-BONE, Inc.

Background: Intervertebral disc degeneration is frequent in dogs and can be associated with symptoms and functional impairments. The degree of disc degeneration can be assessed on T2-weighted MRI scans using the Pfirrmann classification scheme, which was developed for the human spine. However, it could also be used to quantify the effectiveness of disc regeneration therapies. We developed and tested a deep learning tool able to automatically score the degree of disc degeneration in dog spines, starting from an existing model designed to process images of human patients., Methods: MRI midsagittal scans of 5991 lumbar discs of dog patients were collected and manually evaluated with the Pfirrmann scheme and a modified scheme with transitional grades. A deep learning model was trained to classify the disc images based on the two schemes and tested by comparing its performance with the model processing human images., Results: The determination of the Pfirrmann grade showed sensitivities higher than 83% for all degeneration grades, except for grade 5, which is rare in dog spines, and high specificities. In comparison, the correspondent human model had slightly higher sensitivities, on average 90% versus 85% for the canine model. The modified scheme with the fractional grades did not show significant advantages with respect to the original Pfirrmann grades., Conclusions: The novel tool was able to accurately and reliably score the severity of disc degeneration in dogs, although with a performance inferior than that of the human model. The tool has potential in the clinical management of disc degeneration in canine patients as well as in longitudinal studies evaluating regenerative therapies in dogs used as animal models of human disorders., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Authors. JOR Spine published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society.)

Introduction: Generative AI is revolutionizing patient education in healthcare, particularly through chatbots that offer personalized, clear medical information. Reliability and accuracy are vital in AI-driven patient education., Research Question: How effective are Large Language Models (LLM), such as ChatGPT and Google Bard, in delivering accurate and understandable patient education on lumbar disc herniation?, Material and Methods: Ten Frequently Asked Questions about lumbar disc herniation were selected from 133 questions and were submitted to three LLMs. Six experienced spine surgeons rated the responses on a scale from "excellent" to "unsatisfactory," and evaluated the answers for exhaustiveness, clarity, empathy, and length. Statistical analysis involved Fleiss Kappa, Chi-square, and Friedman tests., Results: Out of the responses, 27.2% were excellent, 43.9% satisfactory with minimal clarification, 18.3% satisfactory with moderate clarification, and 10.6% unsatisfactory. There were no significant differences in overall ratings among the LLMs (p = 0.90); however, inter-rater reliability was not achieved, and large differences among raters were detected in the distribution of answer frequencies. Overall, ratings varied among the 10 answers (p = 0.043). The average ratings for exhaustiveness, clarity, empathy, and length were above 3.5/5., Discussion and Conclusion: LLMs show potential in patient education for lumbar spine surgery, with generally positive feedback from evaluators. The new EU AI Act, enforcing strict regulation on AI systems, highlights the need for rigorous oversight in medical contexts. In the current study, the variability in evaluations and occasional inaccuracies underline the need for continuous improvement. Future research should involve more advanced models to enhance patient-physician communication., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Purpose: The aim of this study was to investigate the risks and outcomes of patients with long-term oral anticoagulation (OAC) undergoing spine surgery., Methods: All patients on long-term OAC who underwent spine surgery between 01/2005 and 06/2015 were included. Data were prospectively collected within our in-house Spine Surgery registry and retrospectively supplemented with patient chart and administrative database information. A 1:1 propensity score-matched group of patients without OAC from the same time interval served as control. Primary outcomes were post-operative bleeding, wound complications and thromboembolic events up to 90 days post-surgery. Secondary outcomes included intraoperative blood loss, length of hospital stay, death and 3-month post-operative patient-rated outcomes., Results: In comparison with the control group, patients with OAC (n = 332) had a 3.4-fold (95%CI 1.3-9.0) higher risk for post-operative bleeding, whereas the risks for wound complications and thromboembolic events were comparable between groups. The higher bleeding risk was driven by a higher rate of extraspinal haematomas (3.3% vs. 0.6%; p = 0.001), while there was no difference in epidural haematomas and haematoma evacuations. Risk factors for adverse events among patients with OAC were mechanical heart valves, posterior neck surgery, blood loss > 1000 mL, age, female sex, BMI > 30 kg/m 2 and post-operative PTT levels. At 3-month follow-up, most patients reported favourable outcomes with no difference between groups., Conclusion: Although OAC patients have a higher risk for complications after spine surgery, the risk for major events is low and patients benefit similarly from surgery., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Introduction: Our objective was to assess abnormalities of the odontoid-hip axis (OD-HA) angle in a mild scoliotic population to determine whether screening for malalignment would help predict the distinction between progressive and stable adolescent idiopathic scoliosis (AIS) at early stage., Materials and Methods: All patients (non-scoliotic and AIS) underwent a biplanar X-ray between 2013 and 2020. In AIS, inclusion criteria were Cobb angle between 10° and 25°; Risser sign lower than 3; age higher than 10 years; and no previous treatment. A 3D spine reconstruction was performed, and the OD-HA was computed automatically. A reference corridor for OD-HA values in non-scoliotic subjects was calculated as the range [5th-95th percentiles]. A severity index, helping to distinguish stable and progressive AIS, was calculated and weighted according to the OD-HA value., Results: Eighty-three non-scoliotic and 205 AIS were included. The mean coronal and sagittal OD-HA angles in the non-scoliotic group were 0.2° and -2.5°, whereas in AIS values were 0.3° and -0.8°, respectively. For coronal and sagittal OD-HA, 27.5% and 26.8% of AIS were outside the reference corridor compared with 10.8% in non-scoliotic (OR = 3.1 and 3). Adding to the severity index a weighting factor based on coronal OD-HA, for thoracic scoliosis, improved the positive predictive value by 9% and the specificity by 13%., Conclusion: Analysis of OD-HA suggests that AIS patients are almost three times more likely to have malalignment compared with a non-scoliotic population. Furthermore, analysis of coronal OD-HA is promising to help the clinician distinguish between stable and progressive thoracic scoliosis., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulations. This narrative review presents a summary of the techniques that are used to ensure that all these requirements are fulfilled, with special emphasis on radiological imaging and freely available software solutions that can be directly employed by the interested researcher. Topics discussed include key imaging concepts, such as image resolution and pixel depth; file formats for medical image data storage; free software solutions for medical image processing; anonymisation and pseudonymisation to protect patient privacy, including compliance with regulations such as the Regulation (EU) 2016/679 "General Data Protection Regulation" (GDPR) and the 1996 United States Act of Congress "Health Insurance Portability and Accountability Act" (HIPAA); methods to eliminate patient-identifying features within images, like facial structures; free and commercial tools for image annotation; and techniques for data harmonisation and normalisation.Relevance statement This review provides an overview of the methods and tools that can be used to ensure high-quality data for machine learning and artificial intelligence applications in radiology.Key points• High-quality datasets are essential for reliable artificial intelligence algorithms in medical imaging.• Software tools like ImageJ and 3D Slicer aid in processing medical images for AI research.• Anonymisation techniques protect patient privacy during dataset preparation.• Machine learning models can accelerate image annotation, enhancing efficiency and accuracy.• Data curation ensures dataset integrity, compliance, and quality for artificial intelligence development., (© 2024. The Author(s).)

Due to lack of reference validation data, the common strategy in characterizing adolescent idiopathic scoliosis (AIS) by musculoskeletal modelling approach consists in adapting structure and parameters of validated body models of adult individuals with physiological alignments. Until now, only static postures have been replicated and investigated in AIS subjects. When aiming to simulate trunk motion, two critical factors need consideration: how distributing movement along the vertebral motion levels (lumbar spine rhythm), and if neglecting or accounting for the contribution of the stiffness of the motion segments (disc stiffness). The present study investigates the effect of three different lumbar spine rhythms and absence/presence of disc stiffness on trunk muscle imbalance in the lumbar region and on intervertebral lateral shear at different levels of the thoracolumbar/lumbar scoliotic curve, during simulated trunk motions in the three anatomical planes (flexion/extension, lateral bending, and axial rotation). A spine model with articulated ribcage previously developed in AnyBody software and adapted to replicate the spinal alignment in AIS subjects is employed. An existing dataset of 100 subjects with mild and moderate scoliosis is exploited. The results pointed out the significant impact of lumbar spine rhythm configuration and disc stiffness on changes in the evaluated outputs, as well as a relationship with scoliosis severity. Unfortunately, no optimal settings can be identified due to lack of reference validation data. According to that, extreme caution is recommended when aiming to adapt models of adult individuals with physiological alignments to adolescent subjects with scoliotic deformity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: Validated deep learning models represent a valuable option to perform large-scale research studies aiming to evaluate muscle quality and quantity of paravertebral lumbar muscles at the population level. This study aimed to assess lumbar spine muscle cross-sectional area (CSA) and fat infiltration (FI) in a large cohort of subjects with back disorders through a validated deep learning model., Methods: T2 axial MRI images of 4434 patients (n = 2609 females, n = 1825 males; mean age: 56.7 ± 16.8) with back disorders, such as fracture, spine surgery or herniation, were retrospectively collected from a clinical database and automatically segmented. CSA, expressed as the ratio between total muscle area (TMA) and the vertebral body area (VBA), and FI, in percentages, of psoas major, quadratus lumborum, erector spinae, and multifidus were analyzed as primary outcomes., Results: Male subjects had significantly higher CSA (6.8 ± 1.7 vs. 5.9 ± 1.5 TMA/VBA; p < 0.001) and lower FI (21.9 ± 8.3% vs. 15.0 ± 7.3%; p < 0.001) than females. Multifidus had more FI (27.2 ± 10.6%; p < 0.001) than erector spinae (22.2 ± 9.7%), quadratus lumborum (17.5 ± 7.0%) and psoas (13.7 ± 5.8%) whereas CSA was higher in erector spinae than other lumbar muscles. A high positive correlation between age and total FI was detected (r s  = 0.73; p < 0.001) whereas a negligible negative correlation between total CSA and age was observed (r s  =  - 0.24; p < 0.001). Subjects with fractures had lower CSA and higher FI compared to those with herniations, surgery and with no clear pathological conditions., Conclusion: CSA and FI values of paravertebral muscles vary a lot in accordance with subjects' sex, age and clinical conditions. Given also the large inter-muscle differences in CSA and FI, the choice of muscles needs to be considered with attention by spine surgeons or physiotherapists when investigating changes in lumbar muscle morphology in clinical practice., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Musculoskeletal (MSK) models offer great potential for predicting the muscle forces required to inform more detailed simulations of vertebral endplate loading in adolescent idiopathic scoliosis (AIS). In this work, simulations based on static optimization were compared with in vivo measurements in two AIS patients to determine whether computational approaches alone are sufficient for accurate prediction of paraspinal muscle activity during functional activities. We used biplanar radiographs and marker-based motion capture, ground reaction force, and electromyography (EMG) data from two patients with mild and moderate thoracolumbar AIS (Cobb angles: 21° and 45°, respectively) during standing while holding two weights in front (reference position), walking, running, and object lifting. Using a fully automated approach, 3D spinal shape was extracted from the radiographs. Geometrically personalized OpenSim-based MSK models were created by deforming the spine of pre-scaled full-body models of children/adolescents. Simulations were performed using an experimentally controlled backward approach. Differences between model predictions and EMG measurements of paraspinal muscle activity (both expressed as a percentage of the reference position values) at three different locations around the scoliotic main curve were quantified by root mean square error (RMSE) and cross-correlation (XCorr). Predicted and measured muscle activity correlated best for mild AIS during object lifting (XCorr's ≥ 0.97), with relatively low RMSE values. For moderate AIS as well as the walking and running activities, agreement was lower, with XCorr reaching values of 0.51 and comparably high RMSE values. This study demonstrates that static optimization alone seems not appropriate for predicting muscle activity in AIS patients, particularly in those with more than mild deformations as well as when performing upright activities such as walking and running., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Modic Changes (MCs) are MRI alterations in spine vertebrae's signal intensity. This study introduces an end-to-end model to automatically detect and classify MCs in lumbar MRIs. The model's two-step process involves locating intervertebral regions and then categorizing MC types (MC0, MC1, MC2) using paired T1-and T2-weighted images. This approach offers a promising solution for efficient and standardized MC assessment., Research Question: The aim is to investigate how different MRI normalization techniques affect MCs classification and how the model can be used in a clinical setting., Material and Methods: A combination of Faster R-CNN and a 3D Convolutional Neural Network (CNN) is employed. The model first identifies intervertebral regions and then classifies MC types (MC0, MC1, MC2) using paired T1-and T2-weighted lumbar MRIs. Two datasets are used for model development and evaluation., Results: The detection model achieves high accuracy in identifying intervertebral areas, with Intersection over Union (IoU) values above 0.7, indicating strong localization alignment. Confidence scores above 0.9 demonstrate the model's accurate levels identification. In the classification task, standardization proves the best performances for MC type assessment, achieving mean sensitivities of 0.83 for MC0, 0.85 for MC1, and 0.78 for MC2, along with balanced accuracy of 0.80 and F1 score of 0.88., Discussion and Conclusion: The study's end-to-end model shows promise in automating MC assessment, contributing to standardized diagnostics and treatment planning. Limitations include dataset size, class imbalance, and lack of external validation. Future research should focus on external validation, refining model generalization, and improving clinical applicability., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Purpose: The study aims to assess if the angle of trunk rotation (ATR) in combination with other readily measurable clinical parameters allows for effective non-invasive scoliosis screening., Methods: We analysed 10,813 patients (4-18 years old) who underwent clinical and radiological evaluation for scoliosis in a tertiary clinic specialised in spinal deformities. We considered as predictors ATR, Prominence (mm), visible asymmetry of the waist, scapulae and shoulders, familiarity, sex, BMI, age, menarche, and localisation of the curve. We implemented a Logistic Regression model to classify the Cobb angle of the major curve according to thresholds of 15, 20, 25, 30, and 40 degrees, by randomly splitting the dataset into 80-20% for training and testing, respectively., Results: The model showed accuracies of 74, 81, 79, 79, and 84% for 15-, 20-, 25-, 30- and 40-degrees thresholds, respectively. For all the thresholds ATR, Prominence, and visible asymmetry of the waist were the top five most important variables for the prediction. Samples that were wrongly classified as negatives had always statistically significant (p ≪ 0.01) lower values of ATR and Prominence. This confirmed that these two parameters were very important for the correct classification of the Cobb angle. The model showed better performances than using the 5 and 7 degrees ATR thresholds to prescribe a radiological examination., Conclusions: Machine-learning-based classification models have the potential to effectively improve the non-invasive screening for AIS. The results of the study constitute the basis for the development of easy-to-use tools enabling physicians to decide whether to prescribe radiographic imaging., (© 2023. The Author(s).)

Purpose: Radiological degenerative phenotypes provide insight into a patient's overall extent of disease and can be predictive for future pathological developments as well as surgical outcomes and complications. The objective of this study was to develop a reliable method for automatically classifying sagittal MRI image stacks of cervical spinal segments with respect to these degenerative phenotypes., Methods: We manually evaluated sagittal image data of the cervical spine of 873 patients (5182 motion segments) with respect to 5 radiological phenotypes. We then used this data set as ground truth for training a range of multi-class multi-label deep learning-based models to classify each motion segment automatically, on which we then performed hyper-parameter optimization., Results: The ground truth evaluations turned out to be relatively balanced for the labels disc displacement posterior, osteophyte anterior superior, osteophyte posterior superior, and osteophyte posterior inferior. Although we could not identify a single model that worked equally well across all the labels, the 3D-convolutional approach turned out to be preferable for classifying all labels., Conclusions: Class imbalance in the training data and label noise made it difficult to achieve high predictive power for underrepresented classes. This shortcoming will be mitigated in the future versions by extending the training data set accordingly. Nevertheless, the classification performance rivals and in some cases surpasses that of human raters, while speeding up the evaluation process to only require a few seconds., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Study Design: Retrospective data analysis., Objectives: This study aims to develop a deep learning model for the automatic calculation of some important spine parameters from lateral cervical radiographs., Methods: We collected two datasets from two different institutions. The first dataset of 1498 images was used to train and optimize the model to find the best hyperparameters while the second dataset of 79 images was used as an external validation set to evaluate the robustness and generalizability of our model. The performance of the model was assessed by calculating the median absolute errors between the model prediction and the ground truth for the following parameters: T1 slope, C7 slope, C2-C7 angle, C2-C6 angle, Sagittal Vertical Axis (SVA), C0-C2, Redlund-Johnell distance (RJD), the cranial tilting (CT) and the craniocervical angle (CCA)., Results: Regarding the angles, we found median errors of 1.66° (SD 2.46°), 1.56° (1.95°), 2.46° (SD 2.55), 1.85° (SD 3.93°), 1.25° (SD 1.83°), .29° (SD .31°) and .67° (SD .77°) for T1 slope, C7 slope, C2-C7, C2-C6, C0-C2, CT, and CCA respectively. As concerns the distances, we found median errors of .55 mm (SD .47 mm) and .47 mm (.62 mm) for SVA and RJD respectively., Conclusions: In this work, we developed a model that was able to accurately predict cervical spine parameters from lateral cervical radiographs. In particular, the performances on the external validation set demonstrate the robustness and the high degree of generalizability of our model on images acquired in a different institution., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Humans should sleep for about a third of their lifetime and the choice of the mattress is very important from a quality-of-life perspective. Therefore, the primary aim of this study was to assess the changes of lumbar angles, evaluated in a supine position using magnetic resonance imaging (MRI), on a mattress versus a rigid surface., Methods: Twenty healthy subjects (10 females, 10 males), aged 32.3 ± 6.5 (mean ± standard deviation), with body mass index 22.4 ± 2.9, completed three evaluations: (i) spine MRI in supine position on a mattress (MAT); (ii) spine MRI in supine position on rigid surface (CON); and (iii) biplanar radiographic imaging in standing position. The following indexes were calculated for both MAT and CON: lumbar lordosis angles L1-L5, L1-S1, L5-S1, and the sacral slope (SS). Further, pelvic incidence (PI) was calculated from the biplanar radiographic images., Results: Main findings were (i) L1-L5 and SS were greater in MAT than CON (L1:L5: +2.9°; SS: +2.0°); (ii) L5-S1 was lower in MAT than CON (-1.6°); (iii) L1-S1 was greater in MAT than CON only for male subjects (+2.0°); (iv) significant and positive correlations between PI and L1-L5, L1-S1 and SS were observed in both CON and MAT., Conclusions: The use of a mattress determined small but statistically significant changes in lumbar angles., Relevance Statement: The use of a mattress determines small but statistically significant changes in radiological angles describing the sagittal alignment of the lumbar spine when lying in the supine position., Key Points: • Lordosis angle L1-L5 was greater in MAT than in CON condition (+2.9°). • Sacral slope was greater in MAT than in CON condition (+2.0°). • Lordosis angle L5-S1 was lower in MAT than in CON condition (-1.6°)., (© 2023. European Society of Radiology (ESR).)

Background: Sacropelvic fixation is frequently combined with thoracolumbar instrumentation for correcting spinal deformities. This study aimed to characterize sacropelvic fixation techniques using novel porous fusion/fixation implants (PFFI)., Methods: Three T10-pelvis finite element models were created: (1) pedicle screws and rods in T10-S1, PFFI bilaterally in S2 alar-iliac (S2AI) trajectory; (2) fixation in T10-S1, PFFI bilaterally in S2AI trajectory, triangular implants bilaterally above the PFFI in a sacro-alar-iliac trajectory (PFFI-IFSAI); and (3) fixation in T10-S1, PFFI bilaterally in S2AI trajectory, PFFI in sacro-alar-iliac trajectory stacked cephalad to those in S2AI position (2-PFFI). Models were loaded with pure moments of 7.5 Nm in flexion-extension, lateral bending, and axial rotation. Outputs were compared against 2 baseline models: (1) pedicle screws and rods in T10-S1 (PED), and (2) pedicle screws and rods in T10-S1, and S2AI screws., Results: PFFI and S2AI resulted in similar L5-S1 motion; adding another PFFI per side (2-PFFI) further reduced this motion. Sacroiliac joint (SIJ) motion was also similar between PFFI and S2AI; PFFI-IFSAI and 2-PFFI demonstrated a further reduction in SIJ motion. Additionally, PFFI reduced max stresses on S1 pedicle screws and on implants in the S2AI position., Conclusion: The study shows that supplementing a long construct with PFFI increases the stability of the L5-S1 and SIJ and reduces stresses on the S1 pedicle screws and implants in the S2AI position., Clinical Relevance: The findings suggest a reduced risk of pseudarthrosis at L5-S1 and screw breakage. Clinical studies may be performed to demonstrate applicability to patient outcomes., Level of Evidence: Not applicable (basic science study)., Competing Interests: Declaration of Conflicting Interests : R.D.C. is an employee of and has stock options/stock in SI-BONE, Inc. DPL is an employee of and has stock options/stock in SI-BONE, Inc., and has 3 pending patents and 6 granted patents. A.M. reports as a paid consultant for SI-BONE, Inc. and is a royalties’ bearer. D.W.P. is a paid consultant for SI-BONE, Inc. and is a royalties’ bearer. S.A.Y. is an employee of and has stock options/stock in SI-BONE, Inc., and has 3 pending patents and 6 granted patents. F.G. received funding support for this study from SI-BONE, Inc., (This manuscript is generously published free of charge by ISASS, the International Society for the Advancement of Spine Surgery. Copyright © 2023 ISASS. To see more or order reprints or permissions, see http://ijssurgery.com.)

Study Design: Retrospective observational study., Objective: Biomechanical and geometrical descriptors are used to improve global alignment and proportion (GAP) prediction accuracy to detect proximal junctional failure (PJF)., Summary of Background Data: PJF is probably the most important complication after sagittal imbalance surgery. The GAP score has been introduced as an effective predictor for PJF, but it fails in certain situations. In this study, 112 patient records were gathered (57 PJF; 55 controls) with biomechanical and geometrical descriptors measured to stratify control and failure cases., Patients and Methods: Biplanar EOS radiographs were used to build 3-dimensional full-spine models and determine spinopelvic sagittal parameters. The bending moment (BM) was calculated as the upper body mass times, the effective distance to the body center of mass at the adjacent upper instrumented vertebra +1. Other geometrical descriptors such as full balance index (FBI), spino-sacral angle (SSA), C7 plumb line/sacrofemoral distance ratio (C7/SFD ratio), T1-pelvic angle (TPA), and cervical inclination angle (CIA) were also evaluated. The respective abilities of the GAP, FBI, SSA, C7/SFD, TPA, CIA, body weight, body mass index, and BM to discriminate PJF cases were analyzed through receiver operating characteristic curves and corresponding areas under the curve (AUC)., Results: GAP (AUC = 0.8816) and FBI (AUC = 0.8933) were able to discriminate PJF cases but the highest discrimination power (AUC = 0.9371) was achieved with BM at upper instrumented vertebra + 1. Parameter cutoff analyses provided quantitative thresholds to characterize the control and failure groups and led to improved PJF discrimination, with GAP and BM being the most important contributors. SSA (AUC = 0.2857), C7/SFD (AUC = 0.3143), TPA (AUC = 0.5714), CIA (AUC = 0.4571), body weight (AUC = 0.6319), and body mass index (AUC = 0.7716) did not adequately predict PJF., Conclusion: BM reflects the quantitative biomechanical effect of external loads and can improve GAP accuracy. Sagittal alignments and mechanical integrated scores could be used to better prognosticate the risk of PJF., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: The development of specific clinical and neurological symptoms and radiological degeneration affecting the segment adjacent to a spinal arthrodesis comprise the framework of adjacent-level syndrome. Through the analysis of a large surgical series, this study aimed to identify possible demographic, clinical, radiological, and surgical risk factors involved in the development of adjacent-level syndrome., Methods: A single-center retrospective analysis of adult patients undergoing lumbar fusion procedures between January 2014 and December 2018 was performed. Clinical, demographic, radiological, and surgical data were collected. Patients who underwent surgery for adjacent-segment disease (ASD) were classified as the ASD group. All patients were evaluated 1 month after the surgical procedure clinically and radiologically (with lumbar radiographs) and 3 months afterward with CT scans. The last follow-up was performed by telephone interview. The median follow-up for patients included in the analysis was 67.2 months (range 39-98 months)., Results: A total of 902 patients were included in this study. Forty-nine (5.4%) patients required reoperation for ASD. A significantly higher BMI value was observed in the ASD group (p < 0.001). Microdiscectomy and microdecompression procedures performed at the upper or lower level of an arthrodesis without fusion extension have a statistically significant impact on the development of ASD (p = 0.001). Postoperative pelvic tilt in the ASD group was higher than in the non-ASD group. Numeric rating scale, Core Outcome Measures Index, and Oswestry Disability Index scores at the last follow-up were significantly higher in patients in the ASD group and in patients younger than 65 years., Conclusions: Identifying risk factors for the development of adjacent-level syndrome allows the implementation of a prevention strategy in patients undergoing lumbar arthrodesis surgery. Age older than 65 years, high BMI, preexisting disc degeneration at the adjacent level, and high postoperative pelvic tilt are the most relevant factors. In addition, patients older than 65 years achieve higher levels of clinical improvement and postsurgical satisfaction than do younger patients.

Introduction: A novel classification scheme for endplate lesions, based on T2-weighted images from magnetic resonance imaging (MRI) scan, has been recently introduced and validated. The scheme categorizes intervertebral spaces as "normal," "wavy/irregular," "notched," and "Schmorl's node." These lesions have been associated with spinal pathologies, including disc degeneration and low back pain. The exploitation of an automatic tool for the detection of the lesions would facilitate clinical practice by reducing the workload and the diagnosis time. The present work exploits a deep learning application based on convolutional neural networks to automatically classify the type of lesion., Methods: T2-weighted MRI scans of the sagittal lumbosacral spine of consecutive patients were retrospectively collected. The middle slice of each scan was manually processed to identify the intervertebral spaces from L1L2 to L5S1, and the corresponding lesion type was labeled. A total of 1,559 gradable discs were obtained, with the following types of distribution: "normal" (567 discs), "wavy/irregular" (485), "notched" (362), and "Schmorl's node" (145). The dataset was divided randomly into a training set and a validation set while preserving the original distribution of lesion types in each set. A pretrained network for image classification was utilized, and fine-tuning was performed using the training set. The retrained net was then applied to the validation set to evaluate the overall accuracy and accuracy for each specific lesion type., Results: The overall rate of accuracy was found equal to 88%. The accuracy for the specific lesion type was found as follows: 91% (normal), 82% (wavy/irregular), 93% (notched), and 83% (Schmorl's node)., Discussion: The results indicate that the deep learning approach achieved high accuracy for both overall classification and individual lesion types. In clinical applications, this implementation could be employed as part of an automatic detection tool for pathological conditions characterized by the presence of endplate lesions, such as spinal osteochondrosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor, EG, declared a past collaboration with the author(s)., (© 2023 Bassani, Cina, Galbusera, Sconfienza, Albano, Barcellona, Colombini, Luca and Brayda-Bruno.)

Study Design: Retrospective study., Objectives: Huge amounts of images and medical reports are being generated in radiology departments. While these datasets can potentially be employed to train artificial intelligence tools to detect findings on radiological images, the unstructured nature of the reports limits the accessibility of information. In this study, we tested if natural language processing (NLP) can be useful to generate training data for deep learning models analyzing planar radiographs of the lumbar spine., Methods: NLP classifiers based on the Bidirectional Encoder Representations from Transformers (BERT) model able to extract structured information from radiological reports were developed and used to generate annotations for a large set of radiographic images of the lumbar spine (N = 10 287). Deep learning (ResNet-18) models aimed at detecting radiological findings directly from the images were then trained and tested on a set of 204 human-annotated images., Results: The NLP models had accuracies between 0.88 and 0.98 and specificities between 0.84 and 0.99; 7 out of 12 radiological findings had sensitivity >0.90. The ResNet-18 models showed performances dependent on the specific radiological findings with sensitivities and specificities between 0.53 and 0.93., Conclusions: NLP generates valuable data to train deep learning models able to detect radiological findings in spine images. Despite the noisy nature of reports and NLP predictions, this approach effectively mitigates the difficulties associated with the manual annotation of large quantities of data and opens the way to the era of big data for artificial intelligence in musculoskeletal radiology.

Laser Powder Bed Fusion (L-PBF) is a well-known Additive Manufacturing (AM) technology with a wide range of industrial applications. Potential occupational exposures to metal nanoparticles (NP) as by-products could occur in these processes, and no cogent occupational exposure limits are available. To contribute to this assessment, a monitoring campaign to measure the NP release pattern in two metal L-PBF facilities was carried out in two academic laboratories adopting L-PBF technology for research purposes. The monitored processes deal with two devices and three feedstock types, namely stainless steel (AISI 316L), aluminium-silicon alloy (A357) and pure copper, which are associated with different levels of industrial maturity. Prolonged environmental and personal real-time monitoring of NP concentration and size were performed, temperature and relative humidity were also measured during environmental monitoring. The measurements reveal a controlled NP release of the monitored processes, resulting in an average reduced exposure of the operators during the whole working shift, in compliance with proposed limit values (20 000 n cm-3 for density >6000 kg m-3 or 40 000 n cm-3 for density <6000 kg m-3). Nonetheless, the monitoring results show release events with an increase in NP concentration and a decrease in NP size corresponding with several actions usually performed during warm-up and cleaning, leading to exposures over 40-50 000 n cm-3 during a considerable time interval, especially during the manufacturing of pure copper powder. The results show that the actions of the operators, boundary conditions (relative humidity) and set-up of the L-PBF device have an impact on the amount of NP released and their size. Several release events (significant increase in NP concentration and decrease in NP size) are identified and associated with specific job tasks of the workers as well as building conditions. These results contribute to the definition of NP release benchmarks in AM processes and provide information to improve the operational conditions of L-PBF processes as well as safety guidelines for operators., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Purpose: Sagittal malalignment is a risk factor for mechanical complications after surgery for adult spinal deformity (ASD). Spinal loads, modulated by sagittal alignment, may explain this relationship. The aims of this study were to investigate the relationships between: (1) postoperative changes in loads at the proximal segment and realignment, and (2) absolute postoperative loads and postoperative alignment measures., Methods: A previously validated musculoskeletal model of the whole spine was applied to study a clinical sample of 205 patients with ASD. Based on clinical and radiographic data, pre-and postoperative patient-specific alignments were simulated to predict loads at the proximal segment adjacent to the spinal fusion., Results: Weak-to-moderate associations were found between pre-to-postop changes in lumbar lordosis, LL (r =  - 0.23, r =  - 0.43; p < 0.001), global tilt, GT (r = 0.26, r = 0.38; p < 0.001) and the Global Alignment and Proportion score, GAP (r = 0.26, r = 0.37; p < 0.001), and changes in compressive and shear forces at the proximal segment. GAP score parameters, thoracic kyphosis measurements and the slope of upper instrumented vertebra were associated with changes in shear. In patients with T10-pelvis fusion, moderate-to-strong associations were found between postoperative sagittal alignment measures and compressive and shear loads, with GT showing the strongest correlations (r = 0.75, r = 0.73, p < 0.001)., Conclusions: Spinal loads were estimated for patient-specific full spinal alignment profiles in a large cohort of patients with ASD pre-and postoperatively. Loads on the proximal segments were greater in association with sagittal malalignment and malorientation of proximal vertebra. Future work should explore whether they provide a causative mechanism explaining the associated risk of proximal junction complications., (© 2022. The Author(s).)

Background: The sacroiliac joint (SIJ) transfers the load of the upper body to the lower extremities while allowing a variable physiological movement among individuals. The axis of rotation (AoR) and center of rotation (CoR) of the SIJ can be evaluated to analyze the stability of the SIJ, including when the sacrum is fixed. The purpose of this study was to determine how load intensity affects the SIJ for the intact model and to characterize how sacropelvic fixation performed with different techniques affects this joint., Methods: Five T10-pelvis models were used: (1) intact model; (2) pedicle screws and rods in T10-S1; (3)pedicle screws and rods in T10-S1, and bilateral S2 alar-iliac screws (S2AI); (4) pedicle screws and rods in T10-S1, bilateral S2AI screws, and triangular implants inserted bilaterally in a sacral alar-iliac trajectory ; and (5) pedicle screws and rods in T10-S1, bilateral S2AI screws, and 2 bilateral triangular implants inserted in a lateral trajectory. Outputs of these models under flexion-extension were compared: AoR and CoR of the SIJ at incremental steps from 0 to 7.5 Nm for the intact model and AoR and CoR of the SIJ for the instrumented models at 7.5 Nm., Results: The intact model was validated against an in vivo study by comparing range of motion and displacement of the sacrum. Increasing the load intensity for the intact model led to an increase of the rotation of the sacrum but did not change the CoR. Comparison among the instrumented models showed that sacropelvic fixation techniques reduced the rotation of the sacrum and stabilized the SIJ, in particular with triangular implants., Conclusion: The study outcomes suggest that increasing load intensity increases the rotation of the sacrum but does not influence the CoR, and use of sacropelvic fixation increases the stability of the SIJ, especially when triangular implants are employed., Clinical Relevance: The choice of the instrumentation strategy for sacropelvic fixation affects the stability of the construct in terms of both range of motion and axes of rotation, with direct consequences on the risk of failure and mobilization. Clinical studies should be performed to confirm these biomechanical findings., Competing Interests: Declaration of Conflicting Interests: D.P.L. is an employee of SI-BONE, has stock options/stock in SI-BONE, and has pending patents EP3782586, JP2021180928, and US20220117640 and granted patents EP3193752, JP6542362, JP6932741, US9662157, US10194962, and US11071573. S.A.Y. is an employee of SI-BONE, has stock options/stock in SI-BONE and has pending patents EP3782586, JP2021180928, and US20220117640 and granted patents EP3193752, JP6542362, JP6932741, US9662157, US10194962, and US11071573. R.D.C. is an employee of SI-BONE and has stock options/stock in SI-BONE. D.W.P. is a paid consultant of SI-BONE and royalties’ bearer., (This manuscript is generously published free of charge by ISASS, the International Society for the Advancement of Spine Surgery. Copyright © 2023 ISASS. To see more or order reprints or permissions, see http://ijssurgery.com.)

Aim: To compare three different posterior mono-segmental instrumented models with a Lateral Lumbar Interbody Fusion (LLIF) cage in L4-L5 based on finite element (FE) analysis., Material and Methods: Three different configurations of posterior instrumentation were created: 1. Bilateral posterior screws with 2 rods: Bilateral (B); 2. Left posterior rod and left pedicle screws in L4-L5: Unilateral (U); 3. Oblique posterior rod, left pedicle screw in L4, and right pedicle screw in L5: Oblique (O). The models were compared regarding the range of motion (ROM), stresses in the L4 and L5 pedicle screws, and posterior rods., Results: The Oblique and Unilateral models showed a lower decrease in ROM than the Bilateral model (O vs U vs B; 92% vs 95% vs 96%). In the L4 screw, a higher stress level was identified in the O than in the B model. Still, lower if compared to U. In the L5 screw, the highest stress values were observed with the O model in extension and flexion and the U model in lateral bending and axial rotation. The highest stress values for the rods were observed for the O model in extension, flexion, and axial rotation and the U model in lateral bending., Conclusion: The FE analysis showed that the three configurations significantly reduced the ROM. The stress analysis identified a substantially higher value for the rod and pedicle screws in oblique or unilateral configuration systems compared to the standard bilateral one. In particular, the oblique configuration has stress properties similar to the unilateral in lateral bending and axial rotation but is significantly higher in flexion-extension.

Modic changes (MC) and endplate abnormalities (EA) have been shown to impact preoperative symptoms and outcomes following spinal surgery. However, little is known about how these phenotypes impact cervical alignment. This study aimed to evaluate the impact that these phenotypes have on preoperative, postoperative, and changes in cervical alignment in patients undergoing anterior cervical discectomy and fusion (ACDF). We performed a retrospective study of prospectively collected data of ACDF patients at a single institution. Preoperative magnetic resonance imagings (MRIs) were used to assess for the MC and EA. Patients were subdivided into four groups: MC-only, EA-only, the combined Modic-Endplate-Complex (MEC), and patients without either phenotype. Pre and postoperative MRIs were used to assess alignment parameters. Associations with imaging phenotypes and alignment parameters were assessed, and statistical significance was set at p < 0.5. A total of 512 patients were included, with 84 MC-only patients, 166 EA-only patients, and 71 patients with MEC. Preoperative MC (p = 0.031) and the MEC (p = 0.039) had significantly lower preoperative T1 slope compared to controls. Lower preoperative T1 slope was a risk factor for MC (p = 0.020) and MEC (p = 0.029) and presence of MC (Type II) and the MEC (Type III) was predictive of lower preoperative T1 slope. There were no differences in postoperative alignment measures or patient reported outcome measures. MC and endplate pathologies such as the MEC appear to be associated with worse cervical alignment at baseline relative to patients without these phenotypes. Poor alignment may be an adaptive response to these degenerative findings or may be a risk factor for their development., (© 2022 Orthopaedic Research Society.)