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European Spine Journal

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Open Access 21-03-2023 | Magnetic Resonance Imaging | Original Article

Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images

Authors: Wei Yi, Jingwei Zhao, Wen Tang, Hongkun Yin, Lifeng Yu, Yaohui Wang, Wei Tian

Published in: European Spine Journal | Issue 11/2023

Abstract

Purpose

To develop and validate a deep learning (DL) model for detecting lumbar degenerative disease in both sagittal and axial views of T2-weighted MRI and evaluate its generalized performance in detecting cervical degenerative disease.

Methods

T2-weighted MRI scans of 804 patients with symptoms of lumbar degenerative disease were retrospectively collected from three hospitals. The training dataset (n = 456) and internal validation dataset (n = 134) were randomly selected from the center I. Two external validation datasets comprising 100 and 114 patients were from center II and center III, respectively. A DL model based on 3D ResNet18 and transformer architecture was proposed to detect lumbar degenerative disease. In addition, a cervical MR image dataset comprising 200 patients from an independent hospital was used to evaluate the generalized performance of the DL model. The diagnostic performance was assessed by the free-response receiver operating characteristic (fROC) curve and precision–recall (PR) curve. Precision, recall, and F1-score were used to measure the DL model.

Results

A total of 2497 three-dimension retrogression annotations were labeled for training (n = 1157) and multicenter validation (n = 1340). The DL model showed excellent detection efficiency in the internal validation dataset, with F1-score achieving 0.971 and 0.903 on the sagittal and axial MR images, respectively. Good performance was also observed in the external validation dataset I (F1-score, 0.768 on sagittal MR images and 0.837 on axial MR images) and external validation dataset II (F1-score, 0.787 on sagittal MR images and 0.770 on axial MR images). Furthermore, the robustness of the DL model was demonstrated via transfer learning and generalized performance evaluation on the external cervical dataset, with the F1-score yielding 0.931 and 0.919 on the sagittal and axial MR images, respectively.

Conclusion

The proposed DL model can automatically detect lumbar and cervical degenerative disease on T2-weighted MR images with good performance, robustness, and feasibility in clinical practice.

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Title: Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images
Authors: Wei Yi
Jingwei Zhao
Wen Tang
Hongkun Yin
Lifeng Yu
Yaohui Wang
Wei Tian
Publication date: 21-03-2023
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Degenerative Disease
Published in: European Spine Journal / Issue 11/2023
Print ISSN: 0940-6719
Electronic ISSN: 1432-0932
DOI: https://doi.org/10.1007/s00586-023-07641-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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