Related Experiment Video For AI
Updated: Jul 12, 2026
Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
Published on: September 28, 2017
Evaluation of AI developed for rib fracture detection in identifying vertebral fractures on abdominal CT
Hyunjung Kim1, Yongfeng Gao2, Jin Woo Kim1
1Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-Ro, Wonju, 26426, Korea.
Purpose:
To evaluate the performance of an AI algorithm originally developed for rib fracture detection in identifying vertebral fractures on abdominal CT scans.
Methods:
A retrospective study was performed on 58 patients with both CT and MRI scans. An AI model and ER clinicians independently assessed CT images for fractures of the vertebral body, transverse processes, and spinous processes. Two spine radiologists reviewed both CT and MRI data and established the reference standard.
Results:
At the patient level, both the AI algorithm and ER clinicians achieved 100% sensitivity, specificity, accuracy, and F1-score. At the vertebral level, the AI algorithm achieved higher sensitivity than ER clinicians (67% vs. 54%, p < 0.05), with similar accuracy (93% vs. 94%, p = 0.73). At the anatomical location level, the AI algorithm achieved a sensitivity of 62%, accuracy of 98%, and an F1-score of 0.69, compared with 46% sensitivity (p < 0.05), 97% accuracy (p = 0.92), and an F1-score of 0.62 (p = 0.50) for ER clinicians. Among 178 confirmed fracture locations, the AI algorithm detected 110 cases, whereas ER clinicians identified 81. Of the 97 fractures missed by ER clinicians, the AI algorithm correctly detected 51.
Conclusions:
The AI software originally trained for rib fracture detection identified vertebral fractures with higher sensitivity compared to those detected by ER clinicians. Its ability to identify otherwise missed fractures highlights its value as a supplementary tool in urgent care.
