Deep learning system assisted detection and localization of lumbar spondylolisthesis

Frontiers in Bioengineering and Biotechnology

Bioengineering and Biotechnology

Liangli Cheng¹  Heng Lin²  Lin Lu³  Ying Fang⁴  Mingdi Xue⁴  Hong Zhou⁴  Pengran Liu⁴  Mao Xie⁴  Honglin Wang⁴  Jiayao Zhang⁴  Jiaming Yang⁴  Yi Xie⁴  Songxiang Liu⁴  Zhewei Ye⁴  Tongtong Huo⁵ 

[1] Department of Orthopedics, Daye People’s Hospital, Daye, China;Department of Orthopedics, Nanzhang People’s Hospital, Nanzhang, China;Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, China;Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;

关键词: artificial intelligence;    deep learning;    lumbar spondylolisthesis;    diagnosis;    assisted diagnosis;   

DOI  :  10.3389/fbioe.2023.1194009

 received in 2023-03-26, accepted in 2023-07-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

Objective: Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors’ evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis.Methods: Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set (n = 1,117), a validation set (n = 240), and a test set (n = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals’ evaluation.Results: A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s.Conclusion: The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.

【 授权许可】

Unknown   
Copyright © 2023 Zhang, Lin, Wang, Xue, Fang, Liu, Huo, Zhou, Yang, Xie, Xie, Cheng, Lu, Liu and Ye.