Dual-energy X-ray absorptiometry (DXA) is the standard method for assessing areal bone mineral density (aBMD), diagnosing osteoporosis, and predicting fracture risk. However, DXA's availability is limited in resource-poor areas. This study aimed to develop an artificial intelligence (AI) system capable of estimating aBMD from standard radiographs. The study was part of the Vietnam Osteoporosis Study, a prospective population-based research involving 3783 participants aged 18 years and older. A total of 7060 digital radiographs of the frontal pelvis and lateral spine were taken using the FCR Capsula XLII system (Fujifilm Corp., Tokyo, Japan). aBMD at the femoral neck and lumbar spine was measured with DXA (Hologic Horizon, Hologic Corp., Bedford, MA, USA). An ensemble of seven deep-learning models was used to analyze the X-rays and predict bone mineral density, termed "xBMD". The correlation between xBMD and aBMD was evaluated using Pearson's correlation coefficients. The correlation between xBMD and aBMD at the femoral neck was strong ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> = 0.90; 95% CI, 0.88-0.91), and similarly high at the lumbar spine ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> = 0.87; 95% CI, 0.85-0.88). This correlation remained consistent across different age groups and genders. The AI system demonstrated excellent performance in identifying individuals at high risk for hip fractures, with area under the ROC curve (AUC) values of 0.96 (95% CI, 0.95-0.98) at the femoral neck and 0.97 (95% CI, 0.96-0.99) at the lumbar spine. These findings indicate that AI can accurately predict aBMD and identify individuals at high risk of fractures. This AI system could provide an efficient alternative to DXA for osteoporosis screening in settings with limited resources and high patient demand. An AI system developed to predict aBMD from X-rays showed strong correlations with DXA ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> = 0.90 at femoral neck; =  <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> 0.87 at lumbar spine) and high accuracy in identifying individuals at high risk for fractures (AUC = 0.96 at femoral neck; AUC = 0.97 at lumbar spine).

Adolescent idiopathic scoliosis (AIS) affects a significant portion of the adolescent population, leading to severe spinal deformities if untreated. Diagnosis, surgical planning, and assessment of outcomes are determined primarily by the Cobb angle on anteroposterior spinal radiographs. Screening for scoliosis enables early interventions and improved outcomes. However, screenings are often conducted through school entities where a trained radiologist may not be available to accurately interpret the imaging results. In this study, we developed an artificial intelligence tool utilizing a keypoint region-based convolutional neural network (KR-CNN) for automated thoracic Cobb angle (TCA) measurement. The KR-CNN was trained on 609 whole-spine radiographs of AIS patients and validated using our institutional AIS registry, which included 83 patients who underwent posterior spinal fusion with both preoperative and postoperative anteroposterior X-ray images. The KR-CNN model demonstrated superior performance metrics, including a mean absolute error (MAE) of 2.22, mean squared error (MSE) of 9.1, symmetric mean absolute percentage error (SMAPE) of 4.29, and intraclass correlation coefficient (ICC) of 0.98, outperforming existing methods. This method will enable fast and accurate screening for AIS and assessment of postoperative outcomes and provides a development framework for further automation and validation of spinopelvic measurements.

Fractures in children are common in emergency care, and accurate diagnosis is crucial to avoid complications affecting skeletal development. Limited access to pediatric radiology specialists emphasizes the potential of artificial intelligence (AI)-based diagnostic tools. This study evaluates the performance of the AI software BoneView® for detecting fractures of the upper extremity in children aged 2-18 years. A retrospective analysis was conducted using radiographic data from 826 pediatric patients presenting to the university's pediatric emergency department. Independent assessments by two experienced pediatric radiologists served as reference standard. The diagnostic accuracy of the AI tool compared to the reference standard was evaluated and performance parameters, e.g., sensitivity, specificity, positive and negative predictive values were calculated. The AI tool achieved an overall sensitivity of 89% and specificity of 91% for detecting fractures of the upper extremities. Significantly poorer performance compared to the reference standard was observed for the shoulder, elbow, hand, and fingers, while no significant difference was found for the wrist, clavicle, upper arm, and forearm. The software performed best for wrist fractures (sensitivity: 96%; specificity: 94%) and worst for elbow fractures (sensitivity: 87%; specificity: 65%). The software assessed provides diagnostic support in pediatric emergency radiology. While its overall performance is robust, limitations in specific anatomical regions underscore the need for further training of the underlying algorithms. The results suggest that AI can complement clinical expertise but should not replace radiological assessment. Question There is no comprehensive analysis of an AI-based tool for the diagnosis of pediatric fractures focusing on the upper extremities. Findings The AI-based software demonstrated solid overall diagnostic accuracy in the detection of upper limb fractures in children, with performance differing by anatomical region. Clinical relevance AI-based fracture detection can support pediatric emergency radiology, especially where expert interpretation is limited. However, further algorithm training is needed for certain anatomical regions and for detecting associated findings such as joint effusions to maximize clinical benefit.

We evaluated the diagnostic performance of two AI software programs (BoneView and RBfracture) in assisting non-specialist radiologists (NSRs) in detecting scaphoid fractures using conventional wrist radiographs (X-rays). We retrospectively analyzed 724 radiographs from 264 patients with wrist trauma. Patients were classified into two groups: Group 1 included cases with a definitive diagnosis by a specialist radiologist (SR) based on X-rays (either scaphoid fracture or not), while Group 2 comprised indeterminate cases for the SRs requiring a CT scan for a final diagnosis. Indeterminate cases were defined as negative or doubtful X-rays in patients with persistent clinical symptoms. The X-rays were evaluated by AI and two NSRs, independently and in combination. We compared their diagnostic performances using sensitivity, specificity, area under the curve (AUC), and Cohen's kappa for diagnostic agreement. Group 1 included 174 patients, with 80 cases (45.97%) of scaphoid fractures. Group 2 had 90 patients, of which 44 with uncertain diagnoses and 46 negative cases with persistent symptoms. Scaphoid fractures were identified in 51 patients (56.67%) in Group 2 after further CT imaging. In Group 1, AI performed similarly to NSRs (AUC: BoneView 0.83, RBfracture 0.84, NSR1 0.88, NSR2 0.90), without significant contribution of AI to the performance of NSRs. In Group 2, performances were lower (AUC: BoneView 0.62, RBfracture 0.65, NSR1 0.46, NSR2 0.63), but AI assistance significantly improved NSR performance (NSR2 + BoneView AUC = 0.75, p = 0.003; NSR2 + RBfracture AUC = 0.72, p = 0.030). Diagnostic agreement between NSR1 with AI support and SR was moderate (kappa = 0.576), and substantial for NSR2 (kappa = 0.712). AI tools may effectively assist NSRs, especially in complex scaphoid fracture cases.

Magnetic resonance imaging (MRI) is essential for diagnosing lumbar foraminal stenosis (LFS). However, access remains limited in China due to uneven equipment distribution, high costs, and long waiting times. Therefore, this study developed a lightweight deep learning (DL) model using sagittal CT images to classify LFS severity as a potential clinical alternative where MRI is unavailable. A retrospective study included 868 sagittal CT images from 177 patients (2016-2025). Data were split at the patient level into training (n = 125), validation (n = 31), and test sets (n = 21), with annotations, based on the Lee grading system, provided by two spine surgeons. Two DL models were developed: DL1 (EfficientNet-B0) and DL2 (MobileNetV3-Large-100), both of which incorporated a Faster R-CNN with a ResNet-50-based region-of-interest (ROI) detector. Diagnostic performance was benchmarked against spine surgeons with different levels of clinical experience. DL1 achieved 82.35% diagnostic accuracy (matching the senior spine surgeon's 83.33%), with DL2 at 80.39% (mean 81.37%), both exceeding the junior spine surgeon's 62.75%. DL1 demonstrated near-perfect diagnostic agreement with the senior spine surgeon, as validated by Cohen's kappa analysis (κ = 0.815; 95% CI: 0.723-0.907), whereas DL2 showed substantial consistency (κ = 0.799; 95% CI: 0.703-0.895). Inter-model agreement yielded κ = 0.782 (95% CI: 0.682-0.882). The DL models achieved a mean diagnostic accuracy of 81.37%, comparable to that of the senior spine surgeon (83.33%) in grading LFS severity on sagittal CT. However, given the limited sample size and absence of external validation, their applicability and generalisability to other populations and in multi-centre, large-scale datasets remain uncertain.

In recent years, spinal X-ray image segmentation has played a vital role in the computer-aided diagnosis of various adolescent spinal disorders. However, due to the complex morphology of lesions and the fact that most existing methods are tailored to single-disease scenarios, current segmentation networks struggle to balance local detail preservation and global structural understanding across different disease types. As a result, they often suffer from limited accuracy, insufficient robustness, and poor adaptability. To address these challenges, we propose a novel fully automated spinal segmentation network, DCE-UNet, which integrates the local modeling strength of convolutional neural networks (CNNs) with the global contextual awareness of Transformers. The network introduces several architectural and feature fusion innovations. Specifically, a lightweight Transformer module is incorporated in the encoder to model high-level semantic features and enhance global contextual understanding. In the decoder, a Rec-Block module combining residual convolution and channel attention is designed to improve feature reconstruction and multi-scale fusion during the upsampling process. Additionally, the downsampling feature extraction path integrates a novel DC-Block that fuses channel and spatial attention mechanisms, enhancing the network's ability to represent complex lesion structures. Experiments conducted on a self-constructed large-scale multi-disease adolescent spinal X-ray dataset demonstrate that DCE-UNet achieves a Dice score of 91.3%, a mean Intersection over Union (mIoU) of 84.1, and a Hausdorff Distance (HD) of 4.007, outperforming several state-of-the-art comparison networks. Validation on real segmentation tasks further confirms that DCE-UNet delivers consistently superior performance across various lesion regions, highlighting its strong adaptability to multiple pathologies and promising potential for clinical application.

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.

<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.

With the growing complexity of total hip arthroplasty (THA) for high hip dislocation (HHD), artificial intelligence (AI)-assisted three-dimensional (3D) preoperative planning has emerged as a promising tool to enhance surgical accuracy. This study compared clinical outcomes of AI-assisted 3D versus conventional two-dimensional (2D) X-ray preoperative planning in such cases. A retrospective cohort of 92 patients with Crowe type II-IV HHD who underwent THA between May 2020 and January 2023 was analyzed. Patients received either AI-assisted 3D preoperative planning (n = 49) or 2D X-ray preoperative planning (n = 43). The primary outcome was the accuracy of implant size prediction. Secondary outcomes included operative time, blood loss, leg length discrepancy (LLD), implant positioning, functional scores (Harris Hip Score [HHS], WOMAC, VAS), complications, and implant survival at 24 months. At 24 months, both groups demonstrated significant improvements in functional outcomes. Compared to the 2D X-ray group, the AI-3D group showed higher accuracy in implant size prediction (acetabular cup: 59.18% vs. 30.23%; femoral stem: 65.31% vs. 41.86%; both p < 0.05), a greater proportion of cups placed within the Lewinnek and Callanan safe zones (p < 0.05), shorter operative time, reduced intraoperative blood loss, and more effective correction of leg length discrepancy (all p < 0.05). No significant differences were observed in HHS, WOMAC, or VAS scores between groups at 24 months (all p > 0.05). Implant survivorship was also comparable (100% vs. 97.7%; p = 0.283), with one revision noted in the 2D X-ray group. AI-assisted 3D preoperative planning improves prosthesis selection accuracy, implant positioning, and perioperative outcomes in Crowe type II-IV HHD THA, although 2-year functional and survival outcomes were comparable to 2D X-ray preoperative planning. Considering the higher cost, radiation exposure, and workflow complexity, its broader application warrants further investigation, particularly in identifying patients who may benefit most.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content</i>. Chronic low back pain is a global health issue with considerable socioeconomic burdens and is associated with changes in lumbar paraspinal muscles (LPM). In this retrospective study, a deep learning method was trained and externally validated for automated LPM segmentation, muscle volume quantification, and fatty infiltration assessment across multisequence MRIs. A total of 1,302 MRIs from 641 participants across five centers were included. Data from two centers were used for model training and tuning, while data from the remaining three centers were used for external testing. Model segmentation performance was evaluated against manual segmentation using the Dice similarity coefficient (DSC), and measurement accuracy was assessed using two one-sided tests and Intraclass Correlation Coefficients (ICCs). The model achieved global DSC values of 0.98 on the internal test set and 0.93 to 0.97 on external test sets. Statistical equivalence between automated and manual measurements of muscle volume and fat ratio was confirmed in most regions (<i>P</i> < .05). Agreement between automated and manual measurements was high (ICCs > 0.92). In conclusion, the proposed automated method accurately segmented LPM and demonstrated statistical equivalence to manual measurements of muscle volume and fatty infiltration ratio across multisequence, multicenter MRIs. ©RSNA, 2025.