Affiliation:
1. Department of Orthopaedics Tianjin Medical University General Hospital Tianjin People's Republic of China
2. Beijing Longwood Valley Company Beijing People's Republic of China
3. School of Control Science and Engineering, Shandong University Jinan People's Republic of China
Abstract
AbstractBackgroundNormalized decision support system for lumbar disc herniation (LDH) will improve reproducibility compared with subjective clinical diagnosis and treatment. Magnetic resonance imaging (MRI) plays an essential role in the evaluation of LDH. This study aimed to develop an MRI‐based decision support system for LDH, which evaluates lumbar discs in a reproducible, consistent, and reliable manner.MethodsThe research team proposed a system based on machine learning that was trained and tested by a large, manually labeled data set comprising 217 patients' MRI scans (3255 lumbar discs). The system analyzes the radiological features of identified discs to diagnose herniation and classifies discs by Pfirrmann grade and MSU classification. Based on the assessment, the system provides clinical advice.ResultsEventually, the accuracy of the diagnosis process reached 95.83%. An 83.5% agreement was observed between the system's prediction and the ground‐truth in the Pfirrmann grade. In the case of MSU classification, 95.0% precision was achieved. With the assistance of this system, the accuracy, interpretation efficiency and interrater agreement among surgeons were improved substantially.ConclusionThis system showed considerable accuracy and efficiency, and therefore could serve as an objective reference for the diagnosis and treatment procedure in clinical practice.