How do you check a hummingbird for broken bones? Micro-CT scans can reveal hard-to-spot fractures in tiny, injured hummingbirds. Breakthroughs, discoveries, and DIY tips sent six days a week. Like clockwork, ruby-throated hummingbirds () start showing up at wildlife hospitals throughout the eastern United States every spring. The jewel-toned birds are often brought in after crashing into windows or being attacked by domestic cats .

Automating chicken shoulder deboning requires precise 6-DoF cutting through a partially occluded, deformable, multi-material joint, since contact with the bones presents serious health and safety risks. Our work makes both systems-level and algorithmic contributions to train and deploy a reactive force-feedback cutting policy that dynamically adapts a nominal trajectory and enables full 6-DoF knife control to traverse the narrow joint gap while avoiding contact with the bones. First, we introduce an open-source custom-built simulator for multi-material cutting that models coupling, fracture, and cutting forces, and supports reinforcement learning, enabling efficient training and rapid prototyping. Second, we design a reusable physical testbed to emulate the chicken shoulder: two rigid "bone" spheres with controllable pose embedded in a softer block, enabling rigorous and repeatable evaluation while preserving essential multi-material characteristics of the target problem. Third, we train and deploy a residual RL policy, with discretized force observations and domain randomization, enabling robust zero-shot sim-to-real transfer and the first demonstration of a learned policy that debones a real chicken shoulder. Our experiments in our simulator, on our physical testbed, and on real chicken shoulders show that our learned policy reliably navigates the joint gap and reduces undesired bone/cartilage contact, resulting in up to a 4x improvement over existing open-loop cutting baselines in terms of success rate and bone avoidance. Our results also illustrate the necessity of force feedback for safe and effective multi-material cutting. The project website is at https://sites.google.com/view/chickendeboning-2026.

Bone health studies are crucial in medical practice for the early detection and treatment of Osteopenia and Osteoporosis. Clinicians usually make a diagnosis based on densitometry (DEXA scans) and other patient history. The applications of AI in this field are an ongoing research. Most of the successful methods for this task include Deep Learning models that rely on vision alone (DEXA / X-ray imagery) geared towards high prediction accuracy, where ex-plainability is disregarded and largely based on the post hoc assessment of input contributions. W e propose ProtoMedX, a multi-modal model that uses both DEXA scans of the lumbar spine and patient records. ProtoMedX's prototype-based architecture is explainable by design, crucial for medical applications, especially in the context of the upcoming EU AI Act, as it allows explicit analysis of the model's decisions, especially the ones that are incorrect. ProtoMedX demonstrates state-of-the-art performance in bone health classification while also providing explanations that can be visually understood by clinicians. Using our dataset of 4,160 real NHS patients, the proposed ProtoMedX achieves 87.58% accuracy in vision-only tasks and 89.8% in its multi-modal variant, both approaches surpassing existing published methods.

Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. Methods: This retrospective study included radiography series of the clavicle (n=1,170), elbow (n=3,755), and thumb (n=1,978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To assess the impact of label uncertainty, "uncertain" labels of the training and validation sets were automatically reassigned to "true" (inclusive) or "false" (exclusive). Label-image-pairs were used for multi-label classification using ResNet50. Label extraction accuracy was manually verified on internal (clavicle: n=233, elbow: n=745, thumb: n=393) and external test sets (n=300 for each). Performance was assessed using macro-averaged receiver operating characteristic (ROC) area under the curve (AUC), precision recall curves, sensitivity, specificity, and accuracy. AUCs were compared with the DeLong test. Results: Automatic extraction was correct in 98.6% (60,618 of 61,488) of labels in the test sets. Across anatomic regions, label-based model training yielded competitive performance measured by macro-averaged AUC values for inclusive (e.g., elbow: AUC=0.80 [range, 0.62-0.87]) and exclusive models (elbow: AUC=0.80 [range, 0.61-0.88]). Models generalized well on external datasets (elbow [inclusive]: AUC=0.79 [range, 0.61-0.87]; elbow [exclusive]: AUC=0.79 [range, 0.63-0.89]). No significant differences were observed across labeling strategies or datasets (p>=0.15). Conclusion: GPT-4o extracted labels from radiologic reports to train competitive multi-label classification models with high accuracy. Detected uncertainty in the radiologic reports did not influence the performance of these models.

The present research tackles the difficulty of predicting osteoporosis risk via machine learning (ML) approaches, emphasizing the use of explainable artificial intelligence (XAI) to improve model transparency. Osteoporosis is a significant public health concern, sometimes remaining untreated owing to its asymptomatic characteristics, and early identification is essential to avert fractures. The research assesses six machine learning classifiers: Random Forest, Logistic Regression, XGBoost, AdaBoost, LightGBM, and Gradient Boosting and utilizes a dataset based on clinical, demographic, and lifestyle variables. The models are refined using GridSearchCV to calibrate hyperparameters, with the objective of enhancing predictive efficacy. XGBoost had the greatest accuracy (91%) among the evaluated models, surpassing others in precision (0.92), recall (0.91), and F1-score (0.90). The research further integrates XAI approaches, such as SHAP, LIME, and Permutation Feature Importance, to elucidate the decision-making process of the optimal model. The study indicates that age is the primary determinant in forecasting osteoporosis risk, followed by hormonal alterations and familial history. These results corroborate clinical knowledge and affirm the models' therapeutic significance. The research underscores the significance of explainability in machine learning models for healthcare applications, guaranteeing that physicians can rely on the system's predictions. The report ultimately proposes directions for further research, such as validation across varied populations and the integration of supplementary biomarkers for enhanced predictive accuracy.

Osteoporosis silently erodes skeletal integrity worldwide; however, early detection through imaging can prevent most fragility fractures. Artificial intelligence (AI) methods now mine routine Dual-energy X-ray Absorptiometry (DXA), X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) scans for subtle, clinically actionable markers, but the literature is fragmented. This survey unifies the field through a tri-axial framework that couples imaging modalities with clinical tasks and AI methodologies (classical machine learning, convolutional neural networks (CNNs), transformers, self-supervised learning, and explainable AI). Following a concise clinical and technical primer, we detail our Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search strategy, introduce the taxonomy via a roadmap figure, and synthesize cross-study insights on data scarcity, external validation, and interpretability. By identifying emerging trends, open challenges, and actionable research directions, this review provides AI scientists, medical imaging researchers, and musculoskeletal clinicians with a clear compass to accelerate rigorous, patient-centered innovation in osteoporosis care. The project page of this survey can also be found on Github.

Fracture detection plays a critical role in medical imaging analysis, traditional fracture diagnosis relies on visual assessment by experienced physicians, however the speed and accuracy of this approach are constrained by the expertise. With the rapid advancements in artificial intelligence, deep learning models based on the YOLO framework have been widely employed for fracture detection, demonstrating significant potential in improving diagnostic efficiency and accuracy. This study proposes an improved YOLO-based model, termed Fracture-YOLO, which integrates novel Critical-Region-Selector Attention (CRSelector) and Scale-Aware (ScA) heads to further enhance detection performance. Specifically, the CRSelector module utilizes global texture information to focus on critical features of fracture regions. Meanwhile, the ScA module dynamically adjusts the weights of features at different scales, enhancing the model's capacity to identify fracture targets at multiple scales. Experimental results demonstrate that, compared to the baseline model, Fracture-YOLO achieves a significant improvement in detection precision, with mAP50 and mAP50-95 increasing by 4 and 3, surpassing the baseline model and achieving state-of-the-art (SOTA) performance.

Background: Accurate diagnosis of wrist and hand fractures using radiographs is essential in emergency care, but manual interpretation is slow and prone to errors. Transformer-based models show promise in improving medical image analysis, but their application to extremity fractures is limited. This study addresses this gap by applying object detection transformers to wrist and hand X-rays. Methods: We fine-tuned the RT-DETR and Co-DETR models, pre-trained on COCO, using over 26,000 annotated X-rays from a proprietary clinical dataset. Each image was labeled for fracture presence with bounding boxes. A ResNet-50 classifier was trained on cropped regions to refine abnormality classification. Supervised contrastive learning was used to enhance embedding quality. Performance was evaluated using AP@50, precision, and recall metrics, with additional testing on real-world X-rays. Results: RT-DETR showed moderate results (AP@50 = 0.39), while Co-DETR outperformed it with an AP@50 of 0.615 and faster convergence. The integrated pipeline achieved 83.1% accuracy, 85.1% precision, and 96.4% recall on real-world X-rays, demonstrating strong generalization across 13 fracture types. Visual inspection confirmed accurate localization. Conclusion: Our Co-DETR-based pipeline demonstrated high accuracy and clinical relevance in wrist and hand fracture detection, offering reliable localization and differentiation of fracture types. It is scalable, efficient, and suitable for real-time deployment in hospital workflows, improving diagnostic speed and reliability in musculoskeletal radiology.

Early and accurate detection of the bone fracture is paramount to initiating treatment as early as possible and avoiding any delay in patient treatment and outcomes. Interpretation of X-ray image is a time consuming and error prone task, especially when resources for such interpretation are limited by lack of radiology expertise. Additionally, deep learning approaches used currently, typically suffer from misclassifications and lack interpretable explanations to clinical use. In order to overcome these challenges, we propose an automated framework of bone fracture detection using a VGG-19 model modified to our needs. It incorporates sophisticated preprocessing techniques that include Contrast Limited Adaptive Histogram Equalization (CLAHE), Otsu's thresholding, and Canny edge detection, among others, to enhance image clarity as well as to facilitate the feature extraction. Therefore, we use Grad-CAM, an Explainable AI method that can generate visual heatmaps of the model's decision making process, as a type of model interpretability, for clinicians to understand the model's decision making process. It encourages trust and helps in further clinical validation. It is deployed in a real time web application, where healthcare professionals can upload X-ray images and get the diagnostic feedback within 0.5 seconds. The performance of our modified VGG-19 model attains 99.78\% classification accuracy and AUC score of 1.00, making it exceptionally good. The framework provides a reliable, fast, and interpretable solution for bone fracture detection that reasons more efficiently for diagnoses and better patient care.

Microearthquakes (MEQs) generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their full spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems (EGS), CO$_2$ sequestration and other geo-engineering applications. We present a transformer-based deep learning model that ingests hydraulic stimulation history and prior MEQ observations to forecast four key quantities: cumulative MEQ count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents ($P_{50}, P_{95}$) of the MEQ cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves $R^2 >0.98$ for the 1-second forecast horizon and $R^2 >0.88$ for the 15-second forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.