A machine learning based quantification system for automated diagnosis of lumbar spondylolisthesis on spinal X-rays

Abstract

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.

Keywords

• Spondylolisthesis
• Machine learning
• Automated diagnosis
• Prime sample attention