Distal radius fractures (DRFs) represent up to 20% of the fractures in the emergency department. Delays to surgery of more than 14 days are associated with poorer functional outcomes and increased health care utilization/costs. At our institution, the average time to surgery is more than 19 days because of the separation of surgical and nonsurgical care pathways and a lengthy referral process. To address this challenge, we aimed to create a convolutional neural network (CNN) capable of automating DRF x-ray analysis and triaging. We hypothesize that this model will accurately predict whether an acute isolated DRF fracture in a patient under the age of 60 years will be treated surgically or nonsurgically at our institution based on the radiographic input. We included 163 patients under the age of 60 years who presented to the emergency department between 2018 and 2023 with an acute isolated DRF and who were referred for clinical follow-up. Radiographs taken within 4 weeks of injury were collected in posterior-anterior and lateral views and then preprocessed for model training. The surgeons' decision to treat surgically or nonsurgically at our institution was the reference standard for assessing the model prediction accuracy. We included 723 radiographic posterior-anterior and lateral pairs (385 surgical and 338 nonsurgical) for model training. The best-performing model (seven CNN layers, one fully connected layer, an image input size of 256 × 256 pixels, and a 1.5× weighting for volarly displaced fractures) achieved 88% accuracy and 100% sensitivity. Values for true positive (100%), true negative (72.7%), false positive (27.3%), and false negative (0%) were calculated. After training based on institution-specific indications, a CNN-based algorithm can predict with 88% accuracy whether treatment of an acute isolated DRF in a patient under the age of 60 years will be treated surgically or nonsurgically. By promptly identifying patients who would benefit from expedited surgical treatment pathways, this model can reduce times for referral.

A major global health and wellness issue causing major health problems and death, pneumonia underlines the need of quickly and precisely identifying and treating it. Though imaging technology has advanced, radiologists manual reading of chest X-rays still constitutes the basic method for pneumonia detection, which causes delays in both treatment and medical diagnosis. This study proposes a pneumonia detection method to automate the process using deep learning techniques. The concept employs a bespoke convolutional neural network (CNN) trained on different pneumonia-positive and pneumonia-negative cases from several healthcare providers. Various pre-processing steps were done on the chest radiographs to increase integrity and efficiency before teaching the design. Based on the comparison study with VGG19, ResNet50, InceptionV3, DenseNet201, and MobileNetV3, our bespoke CNN model was discovered to be the most efficient in balancing accuracy, recall, and parameter complexity. It shows 96.5% accuracy and 96.6% F1 score. This study contributes to the expansion of an automated, paired with a reliable, pneumonia finding system, which could improve personal outcomes and increase healthcare efficiency. The full project is available at here.

Developmental Dysplasia of the Hip (DDH) stands as one of the preeminent hip disorders prevalent in pediatric orthopedics. Automated diagnostic instruments, driven by artificial intelligence methodologies, are capable of providing substantial assistance to clinicians in the diagnosis of DDH. We have developed a dataset designated as Multitasking DDH (MTDDH), which is composed of two sub-datasets. Dataset 1 encompasses 1,250 pelvic X-ray images, with annotations demarcating four discrete regions for the evaluation of pelvic X-ray quality, in tandem with eight pivotal points serving as support for DDH diagnosis. Dataset 2 contains 906 pelvic X-ray images, and each image has been annotated with eight key points for assisting in the diagnosis of DDH. Notably, MTDDH represents the pioneering dataset engineered for the comprehensive evaluation of pelvic X-ray quality while concurrently offering the most exhaustive set of eight key points to bolster DDH diagnosis, thus fulfilling the exigency for enhanced diagnostic precision. Ultimately, we presented the elaborate process of constructing the MTDDH and furnished a concise introduction regarding its application.

Determining the 3D pose of a patient from a limited set of 2D X-ray images is a critical task in interventional settings. While preoperative volumetric imaging (e.g., CT and MRI) provides precise 3D localization and visualization of anatomical targets, these modalities cannot be acquired during procedures, where fast 2D imaging (X-ray) is used instead. To integrate volumetric guidance into intraoperative procedures, we present PolyPose, a simple and robust method for deformable 2D/3D registration. PolyPose parameterizes complex 3D deformation fields as a composition of rigid transforms, leveraging the biological constraint that individual bones do not bend in typical motion. Unlike existing methods that either assume no inter-joint movement or fail outright in this under-determined setting, our polyrigid formulation enforces anatomically plausible priors that respect the piecewise rigid nature of human movement. This approach eliminates the need for expensive deformation regularizers that require patient- and procedure-specific hyperparameter optimization. Across extensive experiments on diverse datasets from orthopedic surgery and radiotherapy, we show that this strong inductive bias enables PolyPose to successfully align the patient's preoperative volume to as few as two X-ray images, thereby providing crucial 3D guidance in challenging sparse-view and limited-angle settings where current registration methods fail.

Deep learning (DL) identification of vertebral fractures and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment (VFA) images may improve fracture risk assessment in older adults. In 26 299 lateral spine radiographs from 9276 individuals attending a tertiary-level institution (60% train set; 20% validation set; 20% test set; VERTE-X cohort), DL models were developed to detect prevalent vertebral fracture (pVF) and osteoporosis. The pre-trained DL models from lateral spine radiographs were then fine-tuned in 30% of a DXA VFA dataset (KURE cohort), with performance evaluated in the remaining 70% test set. The area under the receiver operating characteristics curve (AUROC) for DL models to detect pVF and osteoporosis was 0.926 (95% CI 0.908-0.955) and 0.848 (95% CI 0.827-0.869) from VERTE-X spine radiographs, respectively, and 0.924 (95% CI 0.905-0.942) and 0.867 (95% CI 0.853-0.881) from KURE DXA VFA images, respectively. A total of 13.3% and 13.6% of individuals sustained an incident fracture during a median follow-up of 5.4 years and 6.4 years in the VERTE-X test set (n = 1852) and KURE test set (n = 2456), respectively. Incident fracture risk was significantly greater among individuals with DL-detected vertebral fracture (hazard ratios [HRs] 3.23 [95% CI 2.51-5.17] and 2.11 [95% CI 1.62-2.74] for the VERTE-X and KURE test sets) or DL-detected osteoporosis (HR 2.62 [95% CI 1.90-3.63] and 2.14 [95% CI 1.72-2.66]), which remained significant after adjustment for clinical risk factors and femoral neck bone mineral density. DL scores improved incident fracture discrimination and net benefit when combined with clinical risk factors. In summary, DL-detected pVF and osteoporosis in lateral spine radiographs and DXA VFA images enhanced fracture risk prediction in older adults.

Artificial Intelligence (AI) is increasingly being integrated into the field of musculoskeletal (MSK) radiology, from research methods to routine clinical practice. Within the field of fracture detection, AI is allowing for precision and speed previously unimaginable. Yet, AI's decision-making processes are sometimes wrought with deficiencies, undermining trust, hindering accountability, and compromising diagnostic precision. To make AI a trusted ally for radiologists, we recommend incorporating clinical history, rationalizing AI decisions by explainable AI (XAI) techniques, increasing the variety and scale of training data to approach the complexity of a clinical situation, and active interactions between clinicians and developers. By bridging these gaps, the true potential of AI can be unlocked, enhancing patient outcomes and fundamentally transforming radiology through a harmonious integration of human expertise and intelligent technology. In this article, we aim to examine the factors contributing to AI inaccuracies and offer recommendations to address these challenges-benefiting both radiologists and developers striving to improve future algorithms.

The verification of chest X-ray images involves several checkpoints, including orientation and reversal. To address the challenges of manual verification, this study developed an artificial intelligence (AI)-based system using a deep convolutional neural network (DCNN) to automatically verify the consistency between the imaging direction and examination orders. The system classified the chest X-ray images into four categories: anteroposterior (AP), posteroanterior (PA), flipped AP, and flipped PA. To evaluate the impact of internal and external datasets on the classification accuracy, the DCNN was trained using multiple publicly available chest X-ray datasets and tested on both internal and external data. The results demonstrated that the DCNN accurately classified the imaging directions and detected image reversal. However, the classification accuracy was strongly influenced by the training dataset. When trained exclusively on NIH data, the network achieved an accuracy of 98.9% on the same dataset; however, this reduced to 87.8% when evaluated with PADChest data. When trained on a mixed dataset, the accuracy improved to 96.4%; however, it decreased to 76.0% when tested on an external COVID-CXNet dataset. Further, using Grad-CAM, we visualized the decision-making process of the network, highlighting the areas of influence, such as the cardiac silhouette and arm positioning, depending on the imaging direction. Thus, this study demonstrated the potential of AI in assisting in automating the verification of imaging direction and positioning in chest X-rays. However, the network must be fine-tuned to local data characteristics to achieve optimal performance.

This review evaluates advances made in deep learning (DL) applications to automatic spinopelvic parameter estimation, comparing their accuracy to manual measurements performed by surgeons. The PubMed database was queried for studies on DL measurement of adult spinopelvic parameters between 2014 and 2024. Studies were excluded if they focused on pediatric patients, non-deformity-related conditions, non-human subjects, or if they lacked sufficient quantitative data comparing DL models to human measurements. Included studies were assessed based on model architecture, patient demographics, training, validation, testing methods, and sample sizes, as well as performance compared to manual methods. Of 442 screened articles, 16 were included, with sample sizes ranging from 15 to 9,832 radiograph images and reporting interclass correlation coefficients (ICCs) of 0.56 to 1.00. Measurements of pelvic tilt, pelvic incidence, T4-T12 kyphosis, L1-L4 lordosis, and SVA showed consistently high ICCs (>0.80) and low mean absolute deviations (MADs <6°), with substantial number of studies reporting pelvic tilt achieving an excellent ICC of 0.90 or greater. In contrast, T1-T12 kyphosis and L4-S1 lordosis exhibited lower ICCs and higher measurement errors. Overall, most DL models demonstrated strong correlations (>0.80) with clinician measurements and minimal differences compared to manual references, except for T1-T12 kyphosis (average Pearson correlation: 0.68), L1-L4 lordosis (average Pearson correlation: 0.75), and L4-S1 lordosis (average Pearson correlation: 0.65). Novel computer vision algorithms show promising accuracy in measuring spinopelvic parameters, comparable to manual surgeon measurements. Future research should focus on external validation, additional imaging modalities, and the feasibility of integration in clinical settings to assess model reliability and predictive capacity.

Accurate lung volume determination is crucial for reliable dark-field imaging. We compared different approaches for the determination of lung volume in mean dark-field coefficient calculation. In this retrospective analysis of data prospectively acquired between October 2018 and October 2020, patients at least 18 years of age who underwent chest computed tomography (CT) were screened for study participation. Inclusion criteria were the ability to consent and to stand upright without help. Exclusion criteria were pregnancy, lung cancer, pleural effusion, atelectasis, air space disease, ground-glass opacities, and pneumothorax. Lung volume was calculated using four methods: conventional radiography (CR) using shape information; a convolutional neural network (CNN) trained for CR; CT-based volume estimation; and results from pulmonary function testing (PFT). Results were compared using a Student t-test and Spearman ρ correlation statistics. We studied 81 participants (51 men, 30 women), aged 64 ± 12 years (mean ± standard deviation). All lung volumes derived from the various methods were different from each other: CR, 7.27 ± 1.64 L; CNN, 4.91 ± 1.05 L; CT, 5.25 ± 1.36 L; PFT, 6.54 L ± 1.52 L; p < 0.001 for all comparisons. A high positive correlation was found for all combinations (p < 0.001 for all), the highest one being between CT and CR (ρ = 0.88) and the lowest one between PFT and CNN (ρ = 0.78). Lung volume and therefore mean dark-field coefficient calculation is highly dependent on the method used, taking into consideration different positioning and inhalation depths. This study underscores the impact of the method used for lung volume determination. In the context of mean dark-field coefficient calculation, CR-based methods are more desirable because both dark-field images and conventional images are acquired at the same breathing state, and therefore, biases due to differences in inhalation depth are eliminated. Lung volume measurements vary significantly between different determination methods. Mean dark-field coefficient calculations require the same method to ensure comparability. Radiography-based methods simplify workflows and minimize biases, making them most suitable.

When spine fractures involve posterior wall damage, they pose a heightened risk of instability, consequently influencing treatment strategies. To enhance early diagnosis and refine treatment planning for these fractures, we implemented a radiomics analysis using deep learning techniques, based on both anteroposterior and lateral plain X-ray images. Retrospective data were collected for 130 patients with spine fractures who underwent anteroposterior and lateral imaging at two centers (Center 1, training cohort; Center 2, validation cohort) between January 2010 and June 2024. The Vision Transformer (ViT) technique was employed to extract imaging features. The features selected through multiple methods were then used to construct a machine learning model using NaiveBayes and Support Vector Machine (SVM). The model's performance was evaluated using the area under the curve (AUC) metric. 12 features were selected to form the deep learning features. The SVM model using a combination of anteroposterior and lateral plain images showed good performance in both centers with a high AUC for predicting spine fractures with posterior wall injury (Center 1, AUC: 0.909, 95% CI: 0.763-1.000; Center 2, AUC: 0.837, 95% CI: 0.678-0.996). The SVM model based on the combined images outperformed both the individual position images and a spine surgeon with 3 years of clinical experience in classification performance. Our study demonstrates that a radiomic model created by integrating anteroposterior and lateral plain X-ray images of the spine can more effectively predict spine fractures with posterior wall injury, aiding clinicians in making accurate diagnoses and treatment decisions.