Construction and Application of Rib Fracture Diagnosis Model Based on YOLOv3 Algorithm

doi:10.12116/j.issn.1004-5619.2023.230308

Abstract

Abstract:

Objective The artificial intelligence-aided diagnosis model of rib fractures based on YOLOv3 algorithm was established and applied to practical case to explore the application advantages in rib fracture cases in forensic medicine. Methods DICOM format CT images of 884 cases with rib fractures caused by thoracic trauma were collected, and 801 of them were used as training and validation sets. A rib fracture diagnosis model based on YOLOv3 algorithm and Darknet53 as the backbone network was built. After the model was established, 83 cases were taken as the test set, and the precision rate, recall rate, F1-score and radiology interpretation time were calculated. The model was used to diagnose a practical case and compared with manual diagnosis. Results The established model was used to test 83 cases, the fracture precision rate of this model was 90.5%, the recall rate was 75.4%, F1-score was 0.82, the radiology interpretation time was 4.4 images per second and the identification time of each patient’s data was 21 s, much faster than manual diagnosis. The recognition results of the model was consistent with that of the manual diagnosis. Conclusion The rib fracture diagnosis model in practical case based on YOLOv3 algorithm can quickly and accurately identify fractures, and the model is easy to operate. It can be used as an auxiliary diagnostic technique in forensic clinical identification.

Key words: forensic medicine, artificial intelligence （AI）, rib fracture, computed tomography （CT）, diagnosis, YOLOv3, Darknet53

CLC Number:

Jie BAI, Jing SUN, Xiao-guang CHENG, Fan LIU, Hua LIU, Xu WANG. Construction and Application of Rib Fracture Diagnosis Model Based on YOLOv3 Algorithm[J]. Journal of Forensic Medicine, 2023, 39(4): 343-349.

Figures/Tables 6

Fig. 1 Rib fracture labeling and recognition imagesobtained by the first modeling

Fig. 2 Rib fracture recognition images obtained by the second modeling

Fig. 3 The loss function curve of the second modeling

Fig. 4 Results of the false positive

Fig. 5 Results of the false negative

Fig. 6 Model recognition results of the practicalrib fracture case

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