The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma

• Published in: BMC medical imaging 2023-04, Vol.23 (1), p.55-55, Article 55
• Main Authors: Tan, Hui, Xu, Hui, Yu, Nan, Yu, Yong, Duan, Haifeng, Fan, Qiuju, Zhanyu, Tian
• Format: Article
• Language: English
• Subjects: CAI; Chest; Classification; Computed tomography; Computer aided diagnostic; Computer assisted instruction; Computers; Deep Learning; Diagnostic performance; Diagnostic systems; Equipment and supplies; Fractures; Humans; Medical diagnosis; Medical imaging; Medical imaging equipment; Neural networks; Patients; Readers; Reading time; Retrospective Studies; Rib; Rib fracture; Rib Fractures - diagnostic imaging; Ribs; Sensitivity; Sensitivity and Specificity; Teaching; Thoracic Injuries; Thorax; Tomography, X-Ray Computed - methods; Trauma; Wounds, Nonpenetrating - diagnostic imaging
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-023-01012-7

To evaluate the value of a deep learning-based computer-aided diagnostic system (DL-CAD) in improving the diagnostic performance of acute rib fractures in patients with chest trauma. CT images of 214 patients with acute blunt chest trauma were retrospectively analyzed by two interns and two attending radiologists independently firstly and then with the assistance of a DL-CAD one month later, in a blinded and randomized manner. The consensusdiagnosis of fib fracture by another two senior thoracic radiologists was regarded as reference standard. The rib fracture diagnostic sensitivity, specificity, positive predictive value, diagnostic confidence and mean reading time with and without DL-CAD were calculated and compared. There were 680 rib fracture lesions confirmed as reference standard among all patients. The diagnostic sensitivity and positive predictive value of interns weresignificantly improved from (68.82%, 84.50%) to (91.76%, 93.17%) with the assistance of DL-CAD, respectively. Diagnostic sensitivity and positive predictive value of attendings aided by DL-CAD (94.56%, 95.67%) or not aided (86.47%, 93.83%), respectively. In addition, when radiologists were assisted by DL-CAD, the mean reading time was significantly reduced, and diagnostic confidence was significantly enhanced. DL-CAD improves the diagnostic performance of acute rib fracture in chest trauma patients, which increases the diagnostic confidence, sensitivity, and positive predictive value for radiologists. DL-CAD can advance the diagnostic consistency of radiologists with different experiences.