Work

Faster R-CNN model for target recognition and diagnosis of scapular fractures

Year: 2025

Type: article

Abstract: • The CNN model improved diagnostic accuracy for scapular fractures to 97.78% with orthopedist input vs. 82.95% without. • CNN model with clinical expertise showed higher predictive value than traditional methods in distinguishing scapular fractures. • The study highlights the CNN model's potential to enhance clinicians' ability to diagnose scapular fractures. This study aims to establish a diagnostic model for scapular fractures using a convolutional neural network (CNN) and to discuss the clinical advantages of this model in diagnosing such complex conditions. Computed tomography (CT) images of 90 patients with scapular fractures were collected. A faster R-CNN-based recognition model was developed and compared with manual diagnosis. External validation was conducted to evaluate the model’s accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The CNN model, when combined with medical expert interpretation, demonstrated significantly higher specificity and positive predictive value compared to orthopedist-independent interpretation and algorithm-independent prediction (P < 0.05). The area under the curve (AUC) value of the combined approach was significantly higher than that of orthopedist-independent interpretation and algorithm-independent prediction groups, with statistically significant differences (P < 0.05). The accuracy of the CNN algorithm model combined with orthopedist interpretation was 97.78 %, significantly higher than orthopedist-independent interpretation (82.95 %) and CNN algorithm-independent prediction (92.05 %) (P < 0.05). The CNN-based recognition model for scapular fractures can assist clinicians in improving their diagnostic accuracy and precision in identifying such fractures on CT images.

Source: Journal of bone oncology

DOI: 10.1016/j.jbo.2025.100664

Authors: Qiong Fang, Anhong Jiang, Meimei Liu, Sen Zhao

Institutions: Anhui Medical University, Second Affiliated Hospital of Anhui Medical University

Language: English

Topic: Medical Imaging and Analysis

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,990