Neonatal pneumoperitoneum is a life-threatening condition requiring prompt diagnosis, yet its subtle radiographic signs pose diagnostic challenges, especially in emergency settings. To develop and validate a deep multi-task learning model for diagnosing neonatal pneumoperitoneum on radiographs and to assess its clinical utility across clinicians of varying experience levels. Retrospective diagnostic study using internal and external datasets. Internal data were collected between January 1995 and August 2018, while external data were sourced from 11 neonatal intensive care units. Tertiary hospital and multicenter validation settings. Internal dataset: 204 neonates (546 radiographs), external dataset: 378 radiographs (125 pneumoperitoneum cases, 214 non-pneumoperitoneum cases). Radiographs were reviewed by two pediatric radiologists. A reader study involved 4 physicians with varying experience levels. A deep multi-task learning model combining classification and segmentation tasks for pneumoperitoneum detection. The primary outcomes included diagnostic accuracy, area under the receiver operating characteristic curve (AUC), and inter-reader agreement. AI-assisted and unassisted reader performance metrics were compared. The AI model achieved an AUC of 0.98 (95 % CI, 0.94-1.00) and accuracy of 94 % (95 % CI, 85.1-99.6) in internal validation, and AUC of 0.89 (95 % CI, 0.85-0.92) with accuracy of 84.1 % (95 % CI, 80.4-87.8) in external validation. AI assistance improved reader accuracy from 82.5 % to 86.6 % (p < .001) and inter-reader agreement (kappa increased from 0.33 to 0.71 to 0.54-0.86). The multi-task learning model demonstrated excellent diagnostic performance and improved clinicians' diagnostic accuracy and agreement, suggesting its potential to enhance care in neonatal intensive care settings. All code is available at https://github.com/brody9512/NEC_MTL.

Advancements in artificial intelligence (AI) have driven substantial breakthroughs in computer-aided detection (CAD) for chest x-ray (CXR) imaging. The National Taiwan University Hospital research team previously developed an AI-based emergency CXR system (Capstone project), which led to the creation of a CXR module. This CXR module has an established model supported by extensive research and is ready for application in clinical trials without requiring additional model training. This study will use 3 submodules of the system: detection of misplaced endotracheal tubes, detection of misplaced nasogastric tubes, and identification of pneumothorax. This study aims to apply a real-time CXR CAD system in emergency and critical care settings to evaluate its clinical and economic benefits without requiring additional CXR examinations or altering standard care and procedures. The study will evaluate the impact of CAD system on mortality reduction, postintubation complications, hospital stay duration, workload, and interpretation time, as wells as conduct a cost-effectiveness comparison with standard care. This study adopts a pilot trial and cluster randomized controlled trial design, with random assignment conducted at the ward level. In the intervention group, units are granted access to AI diagnostic results, while the control group continues standard care practices. Consent will be obtained from attending physicians, residents, and advanced practice nurses in each participating ward. Once consent is secured, these health care providers in the intervention group will be authorized to use the CAD system. Intervention units will have access to AI-generated interpretations, whereas control units will maintain routine medical procedures without access to the AI diagnostic outputs. The study was funded in September 2024. Data collection is expected to last from January 2026 to December 2027. This study anticipates that the real-time CXR CAD system will automate the identification and detection of misplaced endotracheal and nasogastric tubes on CXRs, as well as assist clinicians in diagnosing pneumothorax. By reducing the workload of physicians, the system is expected to shorten the time required to detect tube misplacement and pneumothorax, decrease patient mortality and hospital stays, and ultimately lower health care costs. PRR1-10.2196/72928.

<b>Objective:</b> To investigate the application effectiveness of the artificial intelligence(AI) based Generative Pre-treatment tool of Skeletal Pathology (GPTSP) in measuring functional lumbar radiographic examinations. <b>Methods:</b> This is a retrospective case series study,reviewing the clinical and imaging data of 34 patients who underwent lumbar dynamic X-ray radiography at Department of Orthopedics, the Second Hospital of Shandong University from September 2021 to June 2023. Among the patients, 13 were male and 21 were female, with an age of (68.0±8.0) years (range:55 to 88 years). The AI model of the GPTSP system was built upon a multi-dimensional constrained loss function constructed based on the YOLOv8 model, incorporating Kullback-Leibler divergence to quantify the anatomical distribution deviation of lumbar intervertebral space detection boxes, along with the introduction of a global dynamic attention mechanism. It can identify lumbar vertebral body edge points and measure lumbar intervertebral space. Furthermore, spondylolisthesis index, lumbar index, and lumbar intervertebral angles were measured using three methods: manual measurement by doctors, predefined annotated measurement, and AI-assisted measurement. The consistency between the doctors and the AI model was analyzed through intra-class correlation coefficient (ICC) and Kappa coefficient. <b>Results:</b> AI-assisted physician measurement time was (1.5±0.1) seconds (range: 1.3 to 1.7 seconds), which was shorter than the manual measurement time ((2 064.4±108.2) seconds,range: 1 768.3 to 2 217.6 seconds) and the pre-defined annotation measurement time ((602.0±48.9) seconds,range: 503.9 to 694.4 seconds). Kappa values between physicians' diagnoses and AI model's diagnoses (based on GPTSP platform) for the lumbar slip index, lumbar index, and intervertebral angles measured by three methods were 0.95, 0.92, and 0.82 (all <i>P</i><0.01), with ICC values consistently exceeding 0.90, indicating high consistency. Based on the doctor's manual measurement, compared with the predefined label measurement, altering AI assistance, doctors measurement with average annotation errors reduced from 2.52 mm (range: 0.01 to 6.78 mm) to 1.47 mm(range: 0 to 5.03 mm). <b>Conclusions:</b> The GPTSP system enhanced efficiency in functional lumbar analysis. AI model demonstrated high consistency in annotation and measurement results, showing strong potential to serve as a reliable clinical auxiliary tool.

An enchondroma is a benign neoplasm of mature hyaline cartilage that proliferates from the medullary cavity toward the cortical bone. This results in the formation of a significant endogenous mass within the medullary cavity. Although enchondromas are predominantly asymptomatic, they may exhibit various clinical manifestations contingent on the size of the lesion, its localization, and the characteristics observed on radiological imaging. This study aimed to identify and present cases of bone tissue enchondromas to field specialists as preliminary data. In this study, authentic X-ray radiographs of patients were obtained following ethical approval and subjected to preprocessing. The images were then annotated by orthopedic oncology specialists using advanced, state-of-the-art object detection algorithms trained with diverse architectural frameworks. All processes, from preprocessing to identifying pathological regions using object detection systems, underwent rigorous cross-validation and oversight by the research team. After performing various operations and procedural steps, including modifying deep learning architectures and optimizing hyperparameters, enchondroma formation in bone tissue was successfully identified. This achieved an average precision of 0.97 and an accuracy rate of 0.98, corroborated by medical professionals. A comprehensive study incorporating 1055 authentic patient data from multiple healthcare centers will be a pioneering investigation that introduces innovative approaches for delivering preliminary insights to specialists concerning bone radiography.

Fracture risk is commonly assessed by FRAX, a tool that estimates 10-year risk for major osteoporotic fracture (MOF) and hip fracture. FRAX scores are often refined by additionally including femoral neck (FN) bone mineral density (BMD) measured by dual-energy x-ray absorptiometry (DXA) as an input. Rho™, a novel AI-powered software, estimates FN BMD T-Scores from conventional x-rays, even when FN is not in the image. Whether a FRAX score using this estimate (FRAX-Rho) can improve a FRAX score without a T-Score input (FRAX-NoT) has not been studied. We conducted a retrospective analysis of Canadian Multicentre Osteoporosis Study participants who had x-rays of the lumbar and/or thoracic spine, FRAX risk factors, and DXA T-Scores acquired at the same time point, and follow-up fracture outcomes over 9 years. In 1361 participants with lumbar x-rays, FRAX-Rho and FRAX with DXA FN T-Scores (FRAX-DXA) had very good agreement in categorizing participants by MOF risk (Cohen's weighted kappa κ=0.80 [0.77-0.82]), which tended to be better than that between FRAX-NoT and FRAX-DXA (0.76 [0.73-0.79]). Agreement in categorizing participants by hip fracture risk was significantly greater between FRAX-Rho and FRAX-DXA (0.67 [0.63-0.71]) than FRAX-NoT and FRAX-DXA (0.52 [0.48-0.56]). In predicting true incident MOF, FRAX-Rho and FRAX-DXA did not differ in their discriminative power (c-index) (0.76 and 0.77; p=0.36) and both were significantly greater than that of FRAX-NoT (0.73; p<0.004). The accuracy of FRAX-Rho for predicting MOF (Brier Score) was better than FRAX-NoT (p<0.05) but not as good as FRAX-DXA. Similar results were observed in participants with thoracic x-rays. In conclusion, FN T-Scores estimated by Rho from lumbar and thoracic x-rays add value to FRAX-NoT estimates and may be useful for risk assessment when DXA is not available.

X-ray digital subtraction angiography (DSA) is frequently used when evaluating minimally invasive medical interventions. DSA predominantly visualizes vessels, and soft tissue anatomy is less visible or invisible in DSA. Visualization of cerebral anatomy could aid physicians during treatment. This study aimed to develop and evaluate a deep learning model to predict vascular territories that are not explicitly visible in DSA imaging acquired during ischemic stroke treatment. We trained an nnUNet model with manually segmented intracranial carotid artery and middle cerebral artery vessel territories on minimal intensity projection DSA acquired during ischemic stroke treatment. We compared the model to a traditional atlas registration model using the Dice similarity coefficient (DSC) and average surface distance (ASD). Additionally, we qualitatively assessed the success rate in both models using an external test. The segmentation model was trained on 1224 acquisitions from 361 patients with ischemic stroke. The segmentation model had a significantly higher DSC (0.96 vs 0.82, p<0.001) and lower ASD compared to the atlas model (13.8 vs 47.3, p<0.001). The success rate of the segmentation model (85%) was higher compared to the atlas registration model (66%) in the external test set. A deep learning method for the segmentation of vascular territories without explicit borders on cerebral DSA demonstrated superior accuracy and quality compared to the traditional atlas-based method. This approach has the potential to be applied to other anatomical structures for enhanced visualization during X-ray guided medical procedures. The code is publicly available at https://github.com/RuishengSu/autoTICI.

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.

Patient-specific bone models are essential for designing surgical guides and preoperative planning, as they enable the visualization of intricate anatomical structures. However, traditional CT-based approaches for creating bone models are limited to preoperative use due to the low flexibility and high radiation exposure of CT and time-consuming manual delineation. Here, we introduce Semi-Supervised Reconstruction with Knowledge Distillation (SSR-KD), a fast and accurate AI framework to reconstruct high-quality bone models from biplanar X-rays in 30 seconds, with an average error under 1.0 mm, eliminating the dependence on CT and manual work. Additionally, high tibial osteotomy simulation was performed by experts on reconstructed bone models, demonstrating that bone models reconstructed from biplanar X-rays have comparable clinical applicability to those annotated from CT. Overall, our approach accelerates the process, reduces radiation exposure, enables intraoperative guidance, and significantly improves the practicality of bone models, offering transformative applications in orthopedics.

Early diagnosis of atrial septal defects (ASDs) from chest X-ray (CXR) images with high accuracy is vital. This study created a dataset from chest X-ray images obtained from different adult subjects. To diagnose atrial septal defects with very high accuracy, which we call state-of-the-art technology, the method known as the Origami paper folding technique, which was used for the first time in the literature on our dataset, was used for data augmentation. Two different augmented data sets were obtained using the Origami technique. The mean, standard deviation, median, variance, and skewness statistical values were obtained column-wise on the images in these data sets. These features were classified with a Support vector machine (SVM). The results obtained using the support vector machine were evaluated according to the k-nearest neighbors (k-NN) and decision tree classifiers for comparison. The results obtained from the classification of the data sets augmented with the Origami technique with the support vector machine (SVM) are state-of-the-art (99.69 %). Our study has provided a clear superiority over deep learning-based artificial intelligence methods.

Large language models (LLMs) have shown remarkable capabilities in natural language processing and multi-modal understanding. However, their high computational cost, limited accessibility, and data privacy concerns hinder their adoption in resource-constrained healthcare environments. This study investigates the performance of small language models (SLMs) in a medical imaging classification task, comparing different models and prompt designs to identify the optimal combination for accuracy and usability. Using the NIH Chest X-ray dataset, we evaluate multiple SLMs on the task of classifying chest X-ray positions (anteroposterior [AP] vs. posteroanterior [PA]) under three prompt strategies: baseline instruction, incremental summary prompts, and correction-based reflective prompts. Our results show that certain SLMs achieve competitive accuracy with well-crafted prompts, suggesting that prompt engineering can substantially enhance SLM performance in healthcare applications without requiring deep AI expertise from end users.