Deep Learning Model for Grading and Localization of Lumbar Disc Herniation on Magnetic Resonance Imaging

Abstract

BackgroundMethods for grading and localization of lumbar disc herniation (LDH) on MRI are complex, time‐consuming, and subjective. Utilizing deep learning (DL) models as assistance would mitigate such complexities.PurposeTo develop an interpretable DL model capable of grading and localizing LDH.Study TypeRetrospective.Subjects1496 patients (M/F: 783/713) were evaluated, and randomly divided into training (70%), validation (10%), and test (20%) sets.Field Strength/Sequence1.5T MRI for axial T2‐weighted sequences (spin echo).AssessmentThe training set was annotated by three spinal surgeons using the Michigan State University classification to train the DL model. The test set was annotated by a spinal surgery expert (as ground truth labels), and two spinal surgeons (comparison with the trained model). An external test set was employed to evaluate the generalizability of the DL model.Statistical TestsCalculated intersection over union (IoU) for detection consistency, utilized Gwet's AC1 to assess interobserver agreement, and evaluated model performance based on sensitivity and specificity, with statistical significance set at P < 0.05.ResultsThe DL model achieved high detection consistency in both the internal test dataset (grading: mean IoU 0.84, recall 99.6%; localization: IoU 0.82, recall 99.5%) and external test dataset (grading: 0.72, 98.0%; localization: 0.71, 97.6%). For internal testing, the DL model (grading: 0.81; localization: 0.76), Rater 1 (0.88; 0.82), and Rater 2 (0.86; 0.83) demonstrated results highly consistent with the ground truth labels. The overall sensitivity of the DL model was 87.0% for grading and 84.0% for localization, while the specificity was 95.5% and 94.4%. For external testing, the DL model showed an appreciable decrease in consistency (grading: 0.69; localization: 0.66), sensitivity (77.2%; 76.7%), and specificity (92.3%; 91.8%).Data ConclusionThe classification capabilities of the DL model closely resemble those of spinal surgeons. For future improvement, enriching the diversity of cases could enhance the model's generalization.Level of Evidence4.Technical EfficacyStage 2.