A machine learning based quantification system for automated diagnosis of lumbar spondylolisthesis on spinal X-rays
Shanshan Liu,
Chenyi Guo,
Yuting Zhao,
Cheng Zhang,
Lihao Yue,
Ruijie Yao,
Qifeng Lan,
Xingyu Zhou,
Bo Zhao,
Ji Wu,
Weishi Li,
Nanfang Xu
Affiliations
Shanshan Liu
Department of Orthopaedics, Peking University Third Hospital, Beijing, China; Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, China; Beijing Key Laboratory of Spinal Disease Research, Beijing, China
Chenyi Guo
Department of Electronic Engineering, Tsinghua University, Beijing, China
Yuting Zhao
Department of Electronic Engineering, Tsinghua University, Beijing, China
Cheng Zhang
Department of Orthopaedics, Peking University Third Hospital, Beijing, China; Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, China; Beijing Key Laboratory of Spinal Disease Research, Beijing, China
Lihao Yue
Peking University Health Science Center, Beijing, China
Ruijie Yao
Department of Electronic Engineering, Tsinghua University, Beijing, China
Qifeng Lan
Peking University Health Science Center, Beijing, China
Xingyu Zhou
Peking University Health Science Center, Beijing, China
Bo Zhao
Peking University Health Science Center, Beijing, China
Ji Wu
Department of Electronic Engineering, Tsinghua University, Beijing, China; Corresponding author. Tsinghua University, 30 Shuangqing Road, Beijing, 100084, China.
Weishi Li
Department of Orthopaedics, Peking University Third Hospital, Beijing, China; Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, China; Beijing Key Laboratory of Spinal Disease Research, Beijing, China; Corresponding author. Peking University Third Hospital, 49 North Garden Road, Beijing 100191, China.
Nanfang Xu
Department of Orthopaedics, Peking University Third Hospital, Beijing, China; Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, China; Beijing Key Laboratory of Spinal Disease Research, Beijing, China; Corresponding author. Peking University Third Hospital, 49 North Garden Road, Beijing, 100191, China.
The automated diagnosis of lumbar spondylolisthesis lacks standardized criteria and the diagnostic of lumbar spondylolisthesis itself inherently relies on the subjective judgment of experts, resulting in a lack of standardized criteria. The objective of this study is to develop a novel, fully automated diagnostic system for lumbar spondylolisthesis. A two-stage system was developed, consisting of a Mask R-CNN with Prime Sample Attention (PISA) for vertebral segmentation in the first stage and a Light Gradient Boosting Machine (LGBM) for spondylolisthesis diagnosis in the second stage. The training data was developed by a total of 936 X-ray images including neutral, extension, and flexion lateral radiographs retrospectively gathered from 312 patients diagnosed with lumbar spondylolisthesis between January 2021 and March 2022. From a model perspective, there were a total of 4680 vertebrae, of which 1056 (22.6 %) were spondylolisthesis and the rest were normal. The Bbox mAP50, Bbox mAP75, Segm mAP50, and Segm mAP75 of Mask R-CNN with PISA were 0.9852, 0.9179, 0.9741, and 0.8957, respectively. The Accuracy, AUC, Recall, Precision, and F1-score of LGBM were 0.9660, 0.9843, 0.9020, 0.9020, and 0.9020, respectively. This study presents a robust system for the diagnosis of lumbar spondylolisthesis, providing accurate detection, classification, and localization of lumbar spondylolisthesis.
