In silico analysis is key to understanding bone structure-function relationships in orthopedics and evolutionary biology, but its potential is limited by a lack of standardized, high-quality human bone morphology datasets. This absence hinders research reproducibility and the development of reliable computational models. To overcome this, BoneDat has been developed. It is a comprehensive database containing standardized bone morphology data from 278 clinical lumbopelvic CT scans (pelvis and lower spine). The dataset includes individuals aged 16 to 91, balanced by sex across ten age groups. BoneDat provides curated segmentation masks, normalized bone geometry (volumetric meshes), and reference morphology templates organized by sex and age. By offering standardized reference geometry and enabling shape normalization, BoneDat enhances the repeatability and credibility of computational models. It also allows for integrating other open datasets, supporting the training and benchmarking of deep learning models and accelerating their path to clinical use.

A retrospective analysis. The aim of this study was to identify a robust radiomic signature from deep learning segmentations for intervertebral disc (IVD) degeneration classification. Low back pain (LBP) is the most common musculoskeletal symptom worldwide and IVD degeneration is an important contributing factor. To improve the quantitative phenotyping of IVD degeneration from T2-weighted magnetic resonance imaging (MRI) and better understand its relationship with LBP, multiple shape and intensity features have been investigated. IVD radiomics have been less studied but could reveal sub-visual imaging characteristics of IVD degeneration. We used data from Northern Finland Birth Cohort 1966 members who underwent lumbar spine T2-weighted MRI scans at age 45-47 (n=1397). We used a deep learning model to segment the lumbar spine IVDs and extracted 737 radiomic features, as well as calculating IVD height index and peak signal intensity difference. Intraclass correlation coefficients across image and mask perturbations were calculated to identify robust features. Sparse partial least squares discriminant analysis was used to train a Pfirrmann grade classification model. The radiomics model had balanced accuracy of 76.7% (73.1-80.3%) and Cohen's Kappa of 0.70 (0.67-0.74), compared to 66.0% (62.0-69.9%) and 0.55 (0.51-0.59) for an IVD height index and peak signal intensity model. 2D sphericity and interquartile range emerged as radiomics-based features that were robust and highly correlated to Pfirrmann grade (Spearman's correlation coefficients of -0.72 and -0.77 respectively). Based on our findings these radiomic signatures could serve as alternatives to the conventional indices, representing a significant advance in the automated quantitative phenotyping of IVD degeneration from standard-of-care MRI.

The anterior cruciate ligament (ACL) is a crucial stabilizer of the knee joint, and its injury risk and surgical outcomes are closely linked to femoral and tibial anatomy. This review focuses on current evidence on how skeletal parameters, such as femoral intercondylar notch morphology, tibial slope, and insertion site variations-influence ACL biomechanics. A narrowed or concave femoral notch raises the risk of impingement, while a higher posterior tibial slope makes anterior tibial translation worse, which increases ACL strain. Gender disparities exist, with females exhibiting smaller notch dimensions, and hormonal fluctuations may contribute to ligament laxity. Anatomical changes that come with getting older make clinical management even harder. Adolescent patients have problems with epiphyseal growth, and older patients have to deal with degenerative notch narrowing and lower bone density. Preoperative imaging (MRI, CT, and 3D reconstruction) enables precise assessment of anatomical variations, guiding individualized surgical strategies. Optimal femoral and tibial tunnel placement during reconstruction is vital to replicate native ACL biomechanics and avoid graft failure. Emerging technologies, including AI-driven segmentation and deep learning models, enhance risk prediction and intraoperative precision. Furthermore, synergistic factors, such as meniscal integrity and posterior oblique ligament anatomy, need to be integrated into comprehensive evaluations. Future directions emphasize personalized approaches, combining advanced imaging, neuromuscular training, and artificial intelligence to optimize prevention, diagnosis, and rehabilitation. Addressing age-specific challenges, such as growth plate preservation in pediatric cases and osteoarthritis management in the elderly, will improve long-term outcomes. Ultimately, a nuanced understanding of skeletal anatomy and technological integration holds promise for reducing ACL reinjury rates and enhancing patient recovery.

A recently developed method enables automated measurement of the hallux valgus angle (HVA) and the first intermetatarsal angle (IMA) from weight-bearing foot radiographs. This approach employs bone segmentation to identify anatomical landmarks and provides standardized angle measurements based on established guidelines. While effective for HVA and IMA, preoperative radiograph analysis remains complex and requires additional measurements, such as the hallux interphalangeal angle (IPA), which has received limited research attention. To expand the previous method, which measured HVA and IMA, by incorporating the automatic measurement of IPA, evaluating its accuracy and clinical relevance. A preexisting database of manually labeled foot radiographs was used to train a U-Net neural network for segmenting bones and identifying landmarks necessary for IPA measurement. Of the 265 radiographs in the dataset, 161 were selected for training and 20 for validation. The U-Net neural network achieves a high mean Sørensen-Dice index (> 0.97). The remaining 84 radiographs were used to assess the reliability of automated IPA measurements against those taken manually by two orthopedic surgeons (O_A and O_B) using computer-based tools. Each measurement was repeated to assess intraobserver (O_A1 and O_A2) and interobserver (O_A2 and O_B) reliability. Agreement between automated and manual methods was evaluated using the Intraclass Correlation Coefficient (ICC), and Bland-Altman analysis identified systematic differences. Standard error of measurement (SEM) and Pearson correlation coefficients quantified precision and linearity, and measurement times were recorded to evaluate efficiency. The artificial intelligence (AI)-based system demonstrated excellent reliability, with ICC3.1 values of 0.92 (AI <i>vs</i> O_A2) and 0.88 (AI <i>vs</i> O_B), both statistically significant (<i>P</i> < 0.001). For manual measurements, ICC values were 0.95 (O_A2 <i>vs</i> O_A1) and 0.95 (O_A2 <i>vs</i> O_B), supporting both intraobserver and interobserver reliability. Bland-Altman analysis revealed minimal biases of: (1) 1.61° (AI <i>vs</i> O_A2); and (2) 2.54° (AI <i>vs</i> O_B), with clinically acceptable limits of agreement. The AI system also showed high precision, as evidenced by low SEM values: (1) 1.22° (O_A2 <i>vs</i> O_B); (2) 1.77° (AI <i>vs</i> O_A2); and (3) 2.09° (AI <i>vs</i> O_B). Furthermore, Pearson correlation coefficients confirmed strong linear relationships between automated and manual measurements, with <i>r</i> = 0.85 (AI <i>vs</i> O_A2) and <i>r</i> = 0.90 (AI <i>vs</i> O_B). The AI method significantly improved efficiency, completing all 84 measurements 8 times faster than manual methods, reducing the time required from an average 36 minutes to just 4.5 minutes. The proposed AI-assisted IPA measurement method shows strong clinical potential, effectively corresponding with manual measurements. Integrating IPA with HVA and IMA assessments provides a comprehensive tool for automated forefoot deformity analysis, supporting hallux valgus severity classification and preoperative planning, while offering substantial time savings in high-volume clinical settings.

Automated grading of Knee Osteoarthritis (KOA) from radiographs is challenged by significant inter-observer variability and the limited robustness of deep learning models, particularly near critical decision boundaries. To address these limitations, this paper proposes a novel framework, Diffusion-based Counterfactual Augmentation (DCA), which enhances model robustness and interpretability by generating targeted counterfactual examples. The method navigates the latent space of a diffusion model using a Stochastic Differential Equation (SDE), governed by balancing a classifier-informed boundary drive with a manifold constraint. The resulting counterfactuals are then used within a self-corrective learning strategy to improve the classifier by focusing on its specific areas of uncertainty. Extensive experiments on the public Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) datasets demonstrate that this approach significantly improves classification accuracy across multiple model architectures. Furthermore, the method provides interpretability by visualizing minimal pathological changes and revealing that the learned latent space topology aligns with clinical knowledge of KOA progression. The DCA framework effectively converts model uncertainty into a robust training signal, offering a promising pathway to developing more accurate and trustworthy automated diagnostic systems. Our code is available at https://github.com/ZWang78/DCA.

Peripheral Nerves (PNs) are traditionally evaluated using US or MRI, allowing radiologists to identify and classify them as normal or pathological based on imaging findings, symptoms, and electrophysiological tests. However, the anatomical complexity of PNs, coupled with their proximity to surrounding structures like vessels and muscles, presents significant challenges. Advanced imaging techniques, including MR-neurography and Diffusion-Weighted Imaging (DWI) neurography, have shown promise but are hindered by steep learning curves, operator dependency, and limited accessibility. Discrepancies between imaging findings and patient symptoms further complicate the evaluation of PNs, particularly in cases where imaging appears normal despite clinical indications of pathology. Additionally, demographic and clinical factors such as age, sex, comorbidities, and physical activity influence PN health but remain unquantifiable with current imaging methods. Artificial Intelligence (AI) solutions have emerged as a transformative tool in PN evaluation. AI-based algorithms offer the potential to transition from qualitative to quantitative assessments, enabling precise segmentation, characterization, and threshold determination to distinguish healthy from pathological nerves. These advances could improve diagnostic accuracy and treatment monitoring. This review highlights the latest advances in AI applications for PN imaging, discussing their potential to overcome the current limitations and opportunities to improve their integration into routine radiological practice.

To assess the methodological quality of radiomics-based studies on bone chondrosarcoma using METhodological RadiomICs Score (METRICS) and Radiomics Quality Score (RQS). A literature search was conducted on EMBASE and PubMed databases for research papers published up to July 2024 and focused on radiomics in bone chondrosarcoma, with no restrictions regarding the study aim. Three readers independently evaluated the study quality using METRICS and RQS. Baseline study characteristics were extracted. Inter-reader reliability was calculated using intraclass correlation coefficient (ICC). Out of 68 identified papers, 18 were finally included in the analysis. Radiomics research was aimed at lesion classification (n = 15), outcome prediction (n = 2) or both (n = 1). Study design was retrospective in all papers. Most studies employed MRI (n = 12), CT (n = 3) or both (n = 1). METRICS and RQS adherence rates ranged between 37.3-94.8% and 2.8-44.4%, respectively. Excellent inter-reader reliability was found for both METRICS (ICC = 0.961) and RQS (ICC = 0.975). Among the limitations of the evaluated studies, the absence of prospective studies and deep learning-based analyses was highlighted, along with the limited adherence to radiomics guidelines, use of external testing datasets and open science data. METRICS and RQS are reproducible quality assessment tools, with the former showing higher adherence rates in studies on chondrosarcoma. METRICS is better suited for assessing papers with retrospective design, which is often chosen in musculoskeletal oncology due to the low prevalence of bone sarcomas. Employing quality scoring systems should be promoted in radiomics-based studies to improve methodological quality and facilitate clinical translation. Employing reproducible quality scoring systems, especially METRICS (which shows higher adherence rates than RQS and is better suited for assessing retrospective investigations), is highly recommended to design radiomics-based studies on chondrosarcoma, improve methodological quality and facilitate clinical translation. The low scientific and reporting quality of radiomics studies on chondrosarcoma is the main reason preventing clinical translation. Quality appraisal using METRICS and RQS showed 37.3-94.8% and 2.8-44.4% adherence rates, respectively. Room for improvement was noted in study design, deep learning methods, external testing and open science. Employing reproducible quality scoring systems is recommended to design radiomics studies on bone chondrosarcoma and facilitate clinical translation.

Popliteal sciatic nerve block is a widely used technique for lower limb anesthesia. However, despite ultrasound guidance, the complex anatomical structures of the popliteal fossa can present challenges, potentially leading to complications. To accurately identify the bifurcation of the sciatic nerve for nerve blockade, we propose MDEANet, a deep learning-based segmentation network designed for the precise localization of nerves, muscles, and arteries in ultrasound images of the popliteal region. MDEANet incorporates Cascaded Multi-scale Atrous Convolutions (CMAC) to enhance multi-scale feature extraction, Enhanced Spatial Attention Mechanism (ESAM) to focus on key anatomical regions, and Cross-level Feature Fusion (CLFF) to improve contextual representation. This integration markedly improves segmentation of nerves, muscles, and arteries. Experimental results demonstrate that MDEANet achieves an average Intersection over Union (IoU) of 88.60% and a Dice coefficient of 93.95% across all target structures, outperforming state-of-the-art models by 1.68% in IoU and 1.66% in Dice coefficient. Specifically, for nerve segmentation, the Dice coefficient reaches 93.31%, underscoring the effectiveness of our approach. MDEANet has the potential to provide decision-support assistance for anesthesiologists, thereby enhancing the accuracy and efficiency of ultrasound-guided nerve blockade procedures.

Medical imaging plays a crucial role in assessing knee osteoarthritis (OA) risk by enabling early detection and disease monitoring. Recent machine learning methods have improved risk estimation (i.e., predicting the likelihood of disease progression) and predictive modelling (i.e., the forecasting of future outcomes based on current data) using medical images, but clinical adoption remains limited due to their lack of interpretability. Existing approaches that generate future images for risk estimation are complex and impractical. Additionally, previous methods fail to localize anatomical knee landmarks, limiting interpretability. We address these gaps with a new interpretable machine learning method to estimate the risk of knee OA progression via multi-task predictive modelling that classifies future knee OA severity and predicts anatomical knee landmarks from efficiently generated high-quality future images. Such image generation is achieved by leveraging a diffusion model in a class-conditioned latent space to forecast disease progression, offering a visual representation of how particular health conditions may evolve. Applied to the Osteoarthritis Initiative dataset, our approach improves the state-of-the-art (SOTA) by 2\%, achieving an AUC of 0.71 in predicting knee OA progression while offering ~9% faster inference time.

To apply machine learning (ML) to measure the Norberg angle (NA) on canine ventrodorsal hip-extended pelvic radiographs. In this observational study, an NA-AI model was trained on real and synthetic radiographs. Additional radiographs were used for validation and testing. Each NA was predicted using a hybrid architecture derived from 2 ML vision models. The NAs were measured by 4 authors, and the model all were compared to each other. The time taken to correct the NAs predicted by the model was compared to unassisted human measurements. The NA-AI model was trained on 733 real and 1,474 synthetic radiographs; 105 real radiographs were used for validation and 128 for testing. The mean absolute error between each human measurement ranged from 3° to 10° ± SD = 3° to 10° with an intraclass correlation between humans of 0.38 to 0.92. The mean absolute error between the NA-AI model prediction and the human measurements was 5° to 6° ± SD = 5° (intraclass correlation, 0.39 to 0.94). Bland-Altman plots showed good agreement between human and AI measurements when the NAs were greater than 80°. The time taken to check the accuracy of the NA measurement compared to unassisted measurements was reduced by 45% to 80%. The NA-AI model proved more accurate than the original model except when the hip dysplasia was severe, and its assistance decreased the time needed to analyze radiographs. The assistance of the NA-AI model reduces the time taken for radiographic hip analysis for clinical applications. However, it is less reliable in cases involving severe osteoarthritic change, requiring manual review for such cases.