Vertebral compression fractures (VCFs) are the most prevalent skeletal manifestations of osteoporosis in cancer patients. Yet, they are frequently missed or not reported in routine clinical radiology, adversely impacting patient outcomes and quality of life. This study evaluates the diagnostic performance of a deep-learning (DL)-based application and its potential to reduce the miss rate of incidental VCFs in a high-risk cancer population. We retrospectively analysed thoraco-abdomino-pelvic (TAP) CT scans from 1556 patients with stage IV cancer collected consecutively over a 4-month period (September-December 2023) in a tertiary cancer center. A DL-based application flagged cases positive for VCFs, which were subsequently reviewed by two expert radiologists for validation. Additionally, grade 3 fractures identified by the application were independently assessed by two expert interventional radiologists to determine their eligibility for vertebroplasty. Of the 1556 cases, 501 were flagged as positive for VCF by the application, with 436 confirmed as true positives by expert review, yielding a positive predictive value (PPV) of 87%. Common causes of false positives included sclerotic vertebral metastases, scoliosis, and vertebrae misidentification. Notably, 83.5% (364/436) of true positive VCFs were absent from radiology reports, indicating a substantial non-report rate in routine practice. Ten grade 3 fractures were overlooked or not reported by radiologists. Among them, 9 were deemed suitable for vertebroplasty by expert interventional radiologists. This study underscores the potential of DL-based applications to improve the detection of VCFs. The analyzed tool can assist radiologists in detecting more incidental vertebral fractures in adult cancer patients, optimising timely treatment and reducing associated morbidity and economic burden. Moreover, it might enhance patient access to interventional treatments such as vertebroplasty. These findings highlight the transformative role that DL can play in optimising clinical management and outcomes for osteoporosis-related VCFs in cancer patients.

Distal myopathy represents a genetically heterogeneous group of skeletal muscle disorders with broad clinical manifestations, posing diagnostic challenges in radiology. To address this, we propose a novel multimodal attention-aware fusion architecture that combines features extracted from two distinct deep learning models, one capturing global contextual information and the other focusing on local details, representing complementary aspects of the input data. Uniquely, our approach integrates these features through an attention gate mechanism, enhancing both predictive performance and interpretability. Our method achieves a high classification accuracy on the BUSI benchmark and a proprietary distal myopathy dataset, while also generating clinically relevant saliency maps that support transparent decision-making in medical diagnosis. We rigorously evaluated interpretability through (1) functionally grounded metrics, coherence scoring against reference masks and incremental deletion analysis, and (2) application-grounded validation with seven expert radiologists. While our fusion strategy boosts predictive performance relative to single-stream and alternative fusion strategies, both quantitative and qualitative evaluations reveal persistent gaps in anatomical specificity and clinical usefulness of the interpretability. These findings highlight the need for richer, context-aware interpretability methods and human-in-the-loop feedback to meet clinicians' expectations in real-world diagnostic settings.

Osteoporosis (OP), a systemic skeletal disease characterized by compromised bone strength and elevated fracture susceptibility, represents a growing global health challenge that necessitates early detection and accurate risk stratification. With the exponential growth of multidimensional biomedical data in OP research, feature selection has become an indispensable machine learning paradigm that improves model generalizability. At the same time, it preserves clinical interpretability and enhances predictive accuracy. This perspective article systematically reviews the transformative role of feature selection methodologies across three critical domains of OP investigation: 1) multi-omics biomarker identification, 2) diagnostic pattern recognition, and 3) fracture risk prognostication. In biomarker discovery, advanced feature selection algorithms systematically refine high-dimensional multi-omics datasets (genomic, proteomic, metabolomic) to isolate key molecular signatures correlated with bone mineral density (BMD) trajectories and microarchitectural deterioration. For clinical diagnostics, these techniques enable efficient extraction of discriminative pattern from multimodal imaging data, including dual-energy X-ray absorptiometry (DXA), quantitative computed tomography (CT), and emerging dental radiographic biomarkers. In prognostic modeling, strategic variable selection optimizes prognostic accuracy by integrating demographic, biochemical, and biomechanical predictors while migrating overfitting in heterogeneous patient cohorts. Current challenges include heterogeneity in dataset quality and dimensionality, translational gaps between algorithmic outputs and clinical decision parameters, and limited reproducibility across diverse populations. Future directions should prioritize the development of adaptive feature selection frameworks capable of dynamic multi-omics data integration, coupled with hybrid intelligence systems that synergize machine-derived biomarkers with clinician expertise. Addressing these challenges requires coordinated interdisciplinary efforts to establish standardized validation protocols and create clinician-friendly decision support interfaces, ultimately bridging the gap between computational OP research and personalized patient care.

Pes planus is a common postural deformity related to the medial longitudinal arch of the foot. Radiographic examinations are important for reproducibility and objectivity; the most commonly used methods are the calcaneal inclusion angle and Mery angle. However, there may be variations in radiographic measurements due to human error and inexperience. In this study, a deep learning (DL)-based solution is proposed to solve this problem. Lateral radiographs of the right and left foot of 289 patients were taken and saved. The study population is a homogeneous group in terms of age and gender, and does not provide sufficient heterogeneity to represent the general population. These radiography (X-ray) images were measured by 2 different experts and the measurements were recorded. According to these measurements, each X-ray image is labeled as pes planus or non-pes planus. These images were then filtered and resized using Gaussian blurring and median filtering methods. As a result of these processes, 2 separate data sets were created. Generally accepted DL models (AlexNet, GoogleNet, SqueezeNet) were reconstructed to classify these images. The 2-category (pes planus/no pes planus) data in the 2 preprocessed and resized datasets were classified by fine-tuning these reconstructed transfer learning networks. The GoogleNet and SqueezeNet models achieved 100% accuracy, while AlexNet achieved 92.98% accuracy. These results show that the predictions of the models and the measurements of expert radiologists overlap to a large extent. DL-based diagnostic methods can be used as a decision support system in the diagnosis of pes planus. DL algorithms enhance the consistency of the diagnostic process by reducing measurement variations between different observers. DL systems accelerate diagnosis by automatically performing angle measurements from X-ray images, which is particularly beneficial in busy clinical settings by saving time. DL models integrated with smartphone cameras can facilitate the diagnosis of pes planus and serve as a screening tool, especially in regions with limited access to healthcare.

Meniscal ramp lesions can impact knee stability, particularly when associated with anterior cruciate ligament (ACL) injuries. Although magnetic resonance imaging (MRI) is the primary diagnostic tool, its diagnostic accuracy remains suboptimal. We aimed to determine whether deep learning technology could enhance MRI-based ramp lesion detection. We reviewed the records of 236 patients who underwent arthroscopic procedures documenting ACL injuries and the status of the medial meniscal posterior horn. A deep learning model was developed using MRI data for ramp lesion detection. Ramp lesion risk factors among patients who underwent ACL reconstruction were analyzed using logistic regression, extreme gradient boosting (XGBoost), and random forest models and were integrated into a final prediction model using Swin Transformer Large architecture. The deep learning model using MRI data demonstrated superior overall diagnostic performance to the clinicians' assessment (accuracy of 73.3% compared with 68.1%, specificity of 78.0% compared with 62.9%, and sensitivity of 64.7% compared with 76.4%). Incorporating risk factors (age, posteromedial tibial bone marrow edema, and lateral meniscal tears) improved the model's accuracy to 80.7%, with a sensitivity of 81.8% and a specificity of 80.9%. Integrating deep learning with MRI data and risk factors significantly enhanced diagnostic accuracy for ramp lesions, surpassing that of the model using MRI alone and that of clinicians. This study highlights the potential of artificial intelligence to provide clinicians with more accurate diagnostic tools for detecting ramp lesions, potentially enhancing treatment and patient outcomes. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Sports injuries are a significant concern for athletes at all levels of competition, ranging from acute traumas to chronic conditions. Prompt diagnosis and effective treatment are crucial for an athlete's recovery and quality of life. Traditionally, sports injury diagnosis has relied on clinical assessments, patient history, and basic imaging techniques such as X-rays, ultrasound, and magnetic resonance imaging (MRI). However, recent technological advancements have revolutionized the field of sports medicine, offering more accurate diagnoses and targeted treatment strategies. High-resolution MRI and CT scans provide detailed images of deep tissue injuries, while advanced ultrasound technology enables on-field diagnostics. Wearable sensor devices and machine learning algorithms allow real-time monitoring of an athlete's movements and physical loads, facilitating early intervention and injury risk prediction. Regenerative medicine, including stem cell therapy and tissue engineering, has emerged as a transformative approach to healing damaged tissues and reducing treatment time. Despite the challenges of high costs, lack of skilled personnel, and ethical considerations, the integration of artificial intelligence and machine learning into sports medicine holds immense potential for revolutionizing injury prevention and management. As these advancements continue to evolve, they are expected to extend athletes' careers and enhance their overall quality of life. This review summarizes conventional methods to diagnose and manage injuries and provides insights into the recent advancements in the field of sports science and medicine. It also states future outlook on the diagnosis and treatment of sports injuries.

This study was conducted to evaluate whether thin-slice 2D fat-saturated proton density-weighted images of the shoulder joint in three imaging planes combined with parallel imaging, partial Fourier technique, and denoising approach with deep learning-based reconstruction (dDLR) are more useful than 3D fat-saturated proton density multi-planar voxel images. Eighteen patients who underwent MRI of the shoulder joint at 3T were enrolled. The denoising effect of dDLR in 2D was evaluated using coefficient of variation (CV). Qualitative evaluation of anatomical structures, noise, and artifacts in 2D after dDLR and 3D was performed by two radiologists using a five-point Likert scale. All were analyzed statistically. Gwet's agreement coefficients were also calculated. The CV of 2D after dDLR was significantly lower than that before dDLR (P < 0.05). Both radiologists rated 2D higher than 3D for all anatomical structures and noise (P < 0.05), except for artifacts. Both Gwet's agreement coefficients of anatomical structures, noise, and artifacts in 2D and 3D produced nearly perfect agreement between the two radiologists. The evaluation of 2D tended to be more reproducible than 3D. 2D with parallel imaging, partial Fourier technique, and dDLR was proved to be superior to 3D for depicting shoulder joint structures with lower noise.

Creating a 3D lumbar model and planning a personalized puncture trajectory has an advantage in establishing the working channel for percutaneous endoscopic lumbar discectomy (PELD). However, existing 3D lumbar models, which seldom include lumbar nerves and dural sac reconstructions, primarily depend on CT images for preoperative trajectory planning. Therefore, our study aims to further investigate the relationship between different virtual working channels and the 3D lumbar model, which includes automated MR image segmentation of lumbar bone, nerves, and dural sac at the L4/L5 level. Preoperative lumbar MR images of 50 patients with L4/L5 lumbar disc herniation were collected from a teaching hospital between March 2020 and July 2020. Automated MR image segmentation was initially used to create a 3D model of the lumbar spine, including the L4 vertebrae, L5 vertebrae, intervertebral disc, L4 nerves, dural sac, and skin. Thirty were then randomly chosen from the segmentation results to clarify the relationship between various virtual working channels and the lumbar 3D model. A bivariate Spearman's rank correlation analysis was used in this study. Preoperative MR images of 50 patients (34 males, mean age 45.6 ± 6 years) were used to train and validate the automated segmentation model, which had mean Dice scores of 0.906, 0.891, 0.896, 0.695, 0.892, and 0.892 for the L4 vertebrae, L5 vertebrae, intervertebral disc, L4 nerves, dural sac, and skin, respectively. With an increase in the coronal plane angle (CPA), there was a reduction in the intersection volume involving the L4 nerves and atypical structures. Conversely, the intersection volume encompassing the dural sac, L4 inferior articular process, and L5 superior articular process increased; the total intersection volume showed a fluctuating pattern: it initially decreased, followed by an increase, and then decreased once more. As the cross-section angle (CSA) increased, there was a rise in the intersection volume of both the L4 nerves and the dural sac; the intersection volume involving the L4 inferior articular process grew while that of the L5 superior articular process diminished; the overall intersection volume and the intersection volume of atypical structures initially decreased, followed by an increase. In terms of regularity, the optimal angles for L4/L5 PELD are a CSA of 15° and a CPA of 15°-20°, minimizing harm to the vertebral bones, facet joint, spinal nerves, and dural sac. Additionally, our 3D preoperative planning method could enhance puncture trajectories for individual patients, potentially advancing surgical navigation, robots, and artificial intelligence in PELD procedures.

Knee injuries frequently require Magnetic Resonance Imaging (MRI) evaluation, increasing radiologists' workload. This study evaluates the impact of a Knee AI assistant on radiologists' diagnostic accuracy and efficiency in detecting anterior cruciate ligament (ACL), meniscus, cartilage, and medial collateral ligament (MCL) lesions on knee MRI exams. This retrospective reader study was conducted from January 2024 to April 2024. Knee MRI studies were evaluated with and without AI assistance by six radiologists with between 2 and 10 years of experience in musculoskeletal imaging in two sessions, 1 month apart. The AI algorithm was trained on 23,074 MRI studies separate from the study dataset and tested on various knee structures, including ACL, MCL, menisci, and cartilage. The reference standard was established by the consensus of three expert MSK radiologists. Statistical analysis included sensitivity, specificity, accuracy, and Fleiss' Kappa. The study dataset involved 165 knee MRIs (89 males, 76 females; mean age, 42.3 ± 15.7 years). AI assistance improved sensitivity from 81% (134/165, 95% CI = [79.7, 83.3]) to 86%(142/165, 95% CI = [84.2, 87.5]) (p < 0.001), accuracy from 86% (142/165, 95% CI = [85.4, 86.9]) to 91%(150/165, 95% CI = [90.7, 92.1]) (p < 0.001), and specificity from 88% (145/165, 95% CI = [87.1, 88.5]) to 93% (153/165, 95% CI = [92.7, 93.8]) (p < 0.001). Sensitivity and accuracy improvements were observed across all knee structures with varied statistical significance ranging from < 0.001 to 0.28. The Fleiss' Kappa values among readers increased from 54% (95% CI = [53.0, 55.3]) to 78% (95% CI = [76.6, 79.0]) (p < 0.001) post-AI integration. The integration of AI improved diagnostic accuracy, efficiency, and inter-reader agreement in knee MRI interpretation, highlighting the value of this approach in clinical practice. Question Can artificial intelligence (AI) assistance improve the diagnostic accuracy and efficiency of radiologists in detecting main lesions anterior cruciate ligament, meniscus, cartilage, and medial collateral ligament lesions in knee MRI? Findings AI assistance in knee MRI interpretation increased radiologists' sensitivity from 81 to 86% and accuracy from 86 to 91% for detecting knee lesions while improving inter-reader agreement (p < 0.001). Clinical relevance AI-assisted knee MRI interpretation enhances diagnostic precision and consistency among radiologists, potentially leading to more accurate injury detection, improved patient outcomes, and reduced diagnostic variability in musculoskeletal imaging.

The Radiological Society of North America has actively promoted artificial intelligence (AI) challenges since 2017. Algorithms emerging from the recent RSNA 2022 Cervical Spine Fracture Detection Challenge demonstrated state-of-the-art performance in the competition's data set, surpassing results from prior publications. However, their performance in real-world clinical practice is not known. As an initial step toward the goal of assessing feasibility of these models in clinical practice, we conducted a generalizability test by using one of the leading algorithms of the competition. The deep learning algorithm was selected due to its performance, portability, and ease of use, and installed locally. One hundred examinations (50 consecutive cervical spine CT scans with at least 1 fracture present and 50 consecutive negative CT scans) from a level 1 trauma center not represented in the competition data set were processed at 6.4 seconds per examination. Ground truth was established based on the radiology report with retrospective confirmation of positive fracture cases. Sensitivity, specificity, F1 score, and area under the curve were calculated. The external validation data set comprised older patients in comparison to the competition set (53.5 ± 21.8 years versus 58 ± 22.0, respectively; <i>P</i> < .05). Sensitivity and specificity were 86% and 70% in the external validation group and 85% and 94% in the competition group, respectively. Fractures misclassified by the convolutional neural networks frequently had features of advanced degenerative disease, subtle nondisplaced fractures not easily identified on the axial plane, and malalignment. The model performed with a similar sensitivity on the test and external data set, suggesting that such a tool could be potentially generalizable as a triage tool in the emergency setting. Discordant factors such as age-associated comorbidities may affect accuracy and specificity of AI models when used in certain populations. Further research should be encouraged to help elucidate the potential contributions and pitfalls of these algorithms in supporting clinical care.