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Objective <p>To conduct a preliminary single-center feasibility study of a YOLO-based deep-learning model for automated detection of lumbar disc herniation at the L4–L5 and L5–S1 levels on sagittal MRI.</p> Methods <p>In this retrospective study, 372 anonymized sagittal T2-weighted lumbar MRI slices from adult patients evaluated for low back pain at a single tertiary center were reviewed. Intervertebral discs at the L4–L5 and L5–S1 levels were labeled with bounding boxes into four classes (L4–L5 herniated, L5–S1 herniated, L4–L5 non-herniated, L5–S1 non-herniated) by two clinicians (a radiologist and a PM&amp;R specialist, each with 6 years of post-residency experience), with consensus adjudication of disagreements, using the combined North American Spine Society / ASSR / ASNR nomenclature as reference. The dataset was split into 337 training, 25 validation, and 10 test images. Rotation-based augmentation was applied. A YOLO object-detection architecture was trained and evaluated using precision, recall, F1, and mean average precision (mAP@0.5 and mAP@0.5–0.95). 95% bootstrap confidence intervals were estimated for aggregate metrics.</p> Results <p>The model achieved an overall precision of 0.738, recall of 0.698, mAP@0.5 of 0.744, and mAP@0.5–0.95 of 0.454, with a best overall F1 of 0.69 at a confidence threshold of approximately 0.30. Herniated classes outperformed non-herniated classes, with the highest recall observed for L5–S1 herniated discs (0.963). Bootstrap confidence intervals were wide, consistent with the small test set.</p> Conclusion <p>This preliminary feasibility study suggests that YOLO-based level-specific detection of lumbar disc herniation at L4–L5 and L5–S1 on sagittal MRI is technically feasible. Given the very small test set, single-center design, absence of external validation, and lack of radiologist benchmarking, these results are hypothesis-generating and not yet sufficient to support any clinical or autonomous-diagnostic use. Larger multi-center validation and prospective comparison with radiologists are required.</p>

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Automated detection of lumbar disc herniation at L4–L5 and L5–S1 levels on sagittal MRI using a YOLO-based deep learning model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Zeynep Karakuzu Güngör,
  • Husam Vehbi,
  • Ahmetcan Cansın

摘要

Objective

To conduct a preliminary single-center feasibility study of a YOLO-based deep-learning model for automated detection of lumbar disc herniation at the L4–L5 and L5–S1 levels on sagittal MRI.

Methods

In this retrospective study, 372 anonymized sagittal T2-weighted lumbar MRI slices from adult patients evaluated for low back pain at a single tertiary center were reviewed. Intervertebral discs at the L4–L5 and L5–S1 levels were labeled with bounding boxes into four classes (L4–L5 herniated, L5–S1 herniated, L4–L5 non-herniated, L5–S1 non-herniated) by two clinicians (a radiologist and a PM&R specialist, each with 6 years of post-residency experience), with consensus adjudication of disagreements, using the combined North American Spine Society / ASSR / ASNR nomenclature as reference. The dataset was split into 337 training, 25 validation, and 10 test images. Rotation-based augmentation was applied. A YOLO object-detection architecture was trained and evaluated using precision, recall, F1, and mean average precision (mAP@0.5 and mAP@0.5–0.95). 95% bootstrap confidence intervals were estimated for aggregate metrics.

Results

The model achieved an overall precision of 0.738, recall of 0.698, mAP@0.5 of 0.744, and mAP@0.5–0.95 of 0.454, with a best overall F1 of 0.69 at a confidence threshold of approximately 0.30. Herniated classes outperformed non-herniated classes, with the highest recall observed for L5–S1 herniated discs (0.963). Bootstrap confidence intervals were wide, consistent with the small test set.

Conclusion

This preliminary feasibility study suggests that YOLO-based level-specific detection of lumbar disc herniation at L4–L5 and L5–S1 on sagittal MRI is technically feasible. Given the very small test set, single-center design, absence of external validation, and lack of radiologist benchmarking, these results are hypothesis-generating and not yet sufficient to support any clinical or autonomous-diagnostic use. Larger multi-center validation and prospective comparison with radiologists are required.