AI Article Synopsis
- The study developed an MRI-based decision support system for diagnosing lumbar disc herniation (LDH) that enhances reproducibility compared to subjective assessments.
- The system was created using machine learning, trained on a dataset of 217 patients and capable of analyzing over 3,000 lumbar discs to classify herniation and provide clinical recommendations.
- Results showed high diagnostic accuracy of 95.83%, substantial agreement with existing grading systems, and improved interpretation efficiency among surgeons, suggesting its potential as a reliable clinical tool.
Article Abstract
Background: Normalized 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.
Methods: The 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.
Results: Eventually, 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.
Conclusion: This system showed considerable accuracy and efficiency, and therefore could serve as an objective reference for the diagnosis and treatment procedure in clinical practice.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11137648 | PMC |
| http://dx.doi.org/10.1002/jsp2.1342 | DOI Listing |
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