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In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper. [ABSTRACT FROM AUTHOR]

Anterior cruciate ligament (ACL) tear is very common in football players, volleyball players, sprinters, runners, etc. It occurs frequently due to extra stretching and sudden movement and causes extreme pain to the patient. Various computer vision-based techniques have been employed for ACL tear detection, but the performance of most of these systems is challenging because of the complex structure of knee ligaments. This paper presents a three-layered compact parallel deep convolutional neural network (CPDCNN) to enhance the feature distinctiveness of the knee MRI images for anterior cruciate ligament (ACL) tear detection in knee MRI images. The performance of the proposed approach is evaluated for the MRNet knee images dataset using accuracy, recall, precision, and the F1 score. The proposed CPDCNN offers an overall accuracy of 96.60%, a recall rate of 0.9668, a precision of 0.9654, and an F1 score of 0.9582, which shows superiority over the existing state-of-the-art methods for knee tear detection. [ABSTRACT FROM AUTHOR]

This study aimed to characterize the Magnetic Resonance Imaging (MRI) features of the Anterolateral Ligament (ALL) in young adults without Anterior Cruciate Ligament (ACL) injury and evaluate its visibility using MRI. In this retrospective analysis, MRI scans of 66 young adults without ACL injuries were assessed by two radiologists. The ALL was examined from its bone-to-bone attachment between the lateral femoral epicondyle and the lateral tibia. The visibility of the ALL was classified as normal, probably normal, abnormal, or non-visualized, based on ligament continuity and thickness relative to the Meniscotibial Ligament (MTL). A continuous structure with thickness equal to or greater than the MTL was considered normal; continuous but wavy and thin features were categorized as probably normal; discontinuity and angulation were deemed abnormal. The proximal attachment of the ALL was categorized as anterior, central, or posterior to the Fibular Collateral Ligament (FCL), while the distal attachment was noted as either at the same location or distal to the MTL. The ALL was identified in 87.9–95.5% of knees and was non-visualized in 4.5–12.1% of cases. Continuous ligamentous structures were observed in 63.7–71.2% of knees (normal in 30.3–37.9%; probably normal in 27.3–40.9%), whereas 19.7–30.3% exhibited abnormal features. Inter-observer agreement was moderate to substantial (κ = 0.66, 0.56), and intra-observer agreement was substantial to excellent (κ = 0.82, 0.66). Among the 58 visible ALLs, proximal attachments were predominantly anterior (63.8%) or central (32.8%) to the FCL, with a minority posterior (1.7%). In total, 4 of the 19 central insertions were incorporated into the FCL mid-substance, and one case was blended into the meniscofemoral ligament. Distal attachments were equally distributed between the same location (50%) and distal to the MTL (50%) (mean 3.7 mm distal). In conclusion, MRI was feasible for detecting the ALL in most young adults without ACL injury, revealing continuous ligament structures in about two-thirds of cases. Approximately 40% of cases exhibited a thickness equal to or greater than the MTL, with the majority of proximal attachments located anterior to the FCL and distal attachments evenly divided between the same insertion and distal to the MTL. [ABSTRACT FROM AUTHOR]

The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach. [ABSTRACT FROM AUTHOR]

Many people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is of great importance to reduce the occupancy rates of health institutions and the treatment time of patients. Many artificial intelligence-based studies have been carried out in detecting and classifying diseases from medical images using imaging applications. The most important goal of these studies was to increase classification performance and model reliability. In this approach, a powerful algorithm based on a new customized deep learning model (ACL model), which trained synchronously with the attention and LSTM model with CNN models, was proposed to classify healthy, COVID-19 and Pneumonia. The important stains and traces in the chest X-ray (CX-R) image were emphasized with the marker-controlled watershed (MCW) segmentation algorithm. The ACL model was trained for different training-test ratios (90–10%, 80–20%, and 70–30%). For 90–10%, 80–20%, and 70–30% training-test ratios, accuracy scores were 100%, 96%, and 96%, respectively. The best performance results were obtained compared to the existing methods. In addition, the contribution of the strategies utilized in the proposed model to classification performance was analyzed in detail. Deep learning-based applications can be used as a useful decision support tool for physicians in the early diagnosis of ILD diseases. However, for the reliability of these applications, it is necessary to undertake verification with many datasets. [ABSTRACT FROM AUTHOR]

Towards the goal of obtaining non-invasive biomarkers reflecting the anterior cruciate ligament's (ACL) loading capacity, this project aimed to develop a magnetic resonance imaging (MRI)-based method facilitating the measurement of ACL elongations during the execution of knee stress tests. An MRI-compatible, computer-controlled, and pneumatically driven knee loading device was designed to perform Lachman-like tests and induce ACL strain. A human cadaveric leg was used for test purposes. During the execution of the stress tests, a triggered real-time cine MRI sequence with a temporal resolution of 10 Hz was acquired in a parasagittal plane to capture the resultant ACL elongations. To test the accuracy of these measurements, the results were compared to in situ data of ACL elongation that were acquired by measuring the length changes of a surgical wire directly sutured to the ACL's anteromedial bundle. The MRI-based ACL elongations ranged between 0.7 and 1.7 mm and agreed very well with in situ data (root mean square errors, RMSEs ≤ 0.25 mm), although peak elongation rates were underestimated by the MRI (RMSEs 0.19–0.36 mm/s). The high accuracy of elongation measurements underlines the potential of the technique to yield an imaging-based biomarker of the ACL's loading capacity. [ABSTRACT FROM AUTHOR]

Background: Soleus injury is one of the most common soft tissue tears during sport activities. Current classifications of muscle tears are based on symptoms and tear size and they do not contribute suitable evidence-based treatment protocols. The objective of this study was to analyze the most frequent echotexture findings of patients with soleus muscle injury, located in the central intramuscular tendon (IMT), and healthy people to determine whether they behave differently and to propose an ultrasound (US)-based classification. Methods: eighty-four athletes, who played in sport activities comprising lower limbs. Echotexture characteristics of soleus muscle were reviewed for 84 subjects. They were divided based on the muscle echogenicity in three groups (Injury Type 1 group, Injury type 2 group and healthy group). Echointensity (EI) and Echovariation (EV) were taken in all groups like quantitative US variable. Results. The Injury Type 1 group was identified by a hypoechoic area and characterized by a higher EV; and Injury Type 2 group was identified by a fibrotic area and characterized by a lower EV. The echogenic pattern of healthy people obtained an intermediate value of EV between both injured soleus types. Conclusions. EV may be useful to classify different types of soleus muscle pathology according to the echogenicity pattern. An innovative proposed US-based classification system for soleus tears may be used to guide treatment decisions for patients with central tendon injury of soleus muscle. [ABSTRACT FROM AUTHOR]

Morquio syndrome (mucopolysaccharidosis IV/MPS IV) is a genetic disorder leading to skeletal abnormalities and gait deviations. Research on the gait patterns and lower extremity physical characteristics associated with skeletal dysplasia in children with MPS IV is currently limited. This research aimed to provide baseline gait patterns and lower limb skeletal alignment of children with MPS IV utilizing three-dimensional instrumented gait analysis. This Institutional Review Board-approved retrospective study evaluates the kinematics of the lower extremities of children with MPS IV during gait, comparing them with an age-matched group of typically developing children. Thirty-three children with MPS IV were included (8.6 ± 4.0 years old). Children with MPS IV walk with increased anterior pelvic tilt, knee valgus, knee flexion, external tibial torsion, and reduced walking speed and stride length (p < 0.001). Multiplanar abnormal alignment results in abnormal knee moments (p < 0.001). Limited correlations exist (r = 0.69–0.28) between dynamic three-dimensional measurements of knee varus/valgus and rotational alignment and traditional static two-dimensional measures (physical examination or radiographs) suggesting the possibility of knee instability during gait and the benefits of dynamic assessment. [ABSTRACT FROM AUTHOR]