Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs

Osteoporotic vertebral fracture (OVF) is a risk factor formorbidity andmortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep-learning-based vertebral fracture diagnostic system using area loss ratio, which assisted amultitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participantswere recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X-ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH modelwere 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity formoderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. (c) 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Automated summary

Osteoporotic vertebral fracture (OVF) is a significant risk for morbidity and mortality in the elderly population, and accurate diagnosis is crucial for improving treatment outcomes. Deep learning methods applied to plain radiographs can potentially address the high rates of misdiagnosis and underdiagnosis in OVF diagnosis. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection, segmentation, and fracture identification, making it an efficient tool to assist radiologists and clinicians in diagnosing vertebral fractures.