YOLO26x-based automated fracture detection on radiographs and its impact on radiologist performance: A multi-reader...

Missed fractures are among the most common diagnostic errors in emergency radiology and may lead to delayed treatment and adverse outcomes. Deep learning-based approaches have shown promise for automated fracture detection; however, many existing models are restricted to specific anatomical regions, operate on downsampled images, or focus primarily on image classification rather than robust localization. This study aimed to develop and validate a YOLO26x-based deep learning model trained using high-resolution input (1280 × 1280 pixels) for detection of appendicular fractures on radiographs and to evaluate its impact on radiologist diagnostic performance, reading time, and diagnostic confidence in a multi-reader multi-case (MRMC) study.

A total of 8,690 appendicular radiographs from an institutional picture archiving and communication system (PACS) and an open-source repository were used to train a YOLO26x-based object detection model using transfer learning. High-resolution input (1280 × 1280 pixels) was used to preserve fine fracture detail. The test set comprised 500 images with a balanced design (250 fracture-positive, 250 fracture-negative; 250 institutional, 250 open-source), ensuring equal representation of both data sources and providing a prevalence-controlled evaluation of diagnostic performance. Model performance was assessed at both image and object levels. A multi-reader multi-case (MRMC) study involving three radiologists (3, 6, and 21 years of experience) evaluated the effect of model assistance on diagnostic accuracy, efficiency, and self-reported confidence using a sequential unassisted-then-assisted design with a 3-week washout interval between sessions. Statistical comparisons included McNemar's test with Holm-Bonferroni correction, Wilcoxon signed-rank tests, Cohen's and Fleiss' κ statistics, and source-stratified analyses.

The model achieved an image-level AUC-ROC of 0.847 (95 % CI: 0.811-0.881) with an F1-score of 0.799 (95 % CI: 0.760-0.834) at the primary operating threshold. Source-stratified analysis demonstrated comparable performance across institutional (AUC 0.843) and independent open-source (AUC 0.852) subsets. Model-assisted reading significantly improved accuracy for two of three readers (RAD1: 76.2 % → 83.4 %, p = 0.001; RAD3: 75.6 % → 83.4 %, p < 0.001; Holm-corrected), with sensitivity gains of 2.4-16.4 percentage points. Inter-reader agreement improved substantially (Fleiss' κ: 0.432 → 0.642). Median interpretation time decreased by 33.2 % overall (12.9 → 8.6 s; p < 0.001), and self-reported confidence increased significantly across all readers (all p < 0.001). Decision-change analysis demonstrated a net positive effect, with beneficial changes substantially outnumbering detrimental ones (197 vs 104 across 1,500 reader-case pairs).

The proposed YOLO26x model trained using high-resolution input demonstrated robust image-level fracture detection with consistent performance across institutional and independent open-source data sources. Model assistance improved diagnostic accuracy, efficiency, inter-reader agreement, and diagnostic confidence. These findings support the potential of high-resolution deep learning-based systems as clinically practical decision-support tools in emergency radiology, while prospective multicenter validation and workflow integration studies remain warranted prior to routine clinical implementation.

AI-assisted fracture detection using high-resolution radiograph input may enhance diagnostic consistency, improve interpretation efficiency, and help reduce missed-injury rates in high-volume emergency settings. With additional validation and workflow integration, such systems may support triage prioritization and clinical decision-making in emergency radiology.