Automatic Grading of Disc Herniation, Central Canal Stenosis and Nerve Roots Compression in Lumbar Magnetic Resonance Image Diagnosis

Su, Zhihai; Liu, Jin; Yang, Minsheng; Chen, Ziyang; You, Kun; Shen, Jun; Huang, Chengjie; Zhao, Qinghao; Liu, Enqing; Zhao, Lei; Feng, Qianjin; Pang, Shumao; Li, Shaolin; Lü, Hai

doi:10.3389/fendo.2022.890371

Cited by 14 publications

(14 citation statements)

References 31 publications

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“…Several studies in the current literature have discussed the diagnostic benefits of using each of the aforementioned tests. [12][13][14][15] ChatGPT also accurately presented information on the importance of nerve function tests that were highlighted by the guidelines, including electromyography and nerve conduction studies. These findings by both the guidelines and ChatGPT have been tested across multiple papers in the last few years and have been found to be an accurate diagnostic tool.…”

Section: Chatgpt Vs Guidelines Recommendationsmentioning

confidence: 99%

ChatGPT and its Role in the Decision-Making for the Diagnosis and Treatment of Lumbar Spinal Stenosis: A Comparative Analysis and Narrative Review

Rajjoub

Arroyave

Zaidat

et al. 2023

Global Spine Journal

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Study Design Comparative Analysis and Narrative Review. Objective To assess and compare ChatGPT’s responses to the clinical questions and recommendations proposed by The 2011 North American Spine Society (NASS) Clinical Guideline for the Diagnosis and Treatment of Degenerative Lumbar Spinal Stenosis (LSS). We explore the advantages and disadvantages of ChatGPT’s responses through an updated literature review on spinal stenosis. Methods We prompted ChatGPT with questions from the NASS Evidence-based Clinical Guidelines for LSS and compared its generated responses with the recommendations provided by the guidelines. A review of the literature was performed via PubMed, OVID, and Cochrane on the diagnosis and treatment of lumbar spinal stenosis between January 2012 and April 2023. Results 14 questions proposed by the NASS guidelines for LSS were uploaded into ChatGPT and directly compared to the responses offered by NASS. Three questions were on the definition and history of LSS, one on diagnostic tests, seven on non-surgical interventions and three on surgical interventions. The review process found 40 articles that were selected for inclusion that helped corroborate or contradict the responses that were generated by ChatGPT. Conclusions ChatGPT’s responses were similar to findings in the current literature on LSS. These results demonstrate the potential for implementing ChatGPT into the spine surgeon’s workplace as a means of supporting the decision-making process for LSS diagnosis and treatment. However, our narrative summary only provides a limited literature review and additional research is needed to standardize our findings as means of validating ChatGPT’s use in the clinical space.

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Section: Chatgpt Vs Guidelines Recommendationsmentioning

confidence: 99%

ChatGPT and its Role in the Decision-Making for the Diagnosis and Treatment of Lumbar Spinal Stenosis: A Comparative Analysis and Narrative Review

Rajjoub

Arroyave

Zaidat

et al. 2023

Global Spine Journal

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“…Relying on the MSU classification, we proposed a novel, automated model that utilized axial lumbar MR images to group LDH into four grades; thus, we optimized severity assessment and the selection of suitable operative candidates. The proposed model achieved 87.7% and 74.2% average accuracy for internal and external test datasets, respectively, which indicated its superiority over other automated diagnostic models with multiclass classification (67.1%–81.2% for neural foraminal stenosis; and 70.6%–86.9% for central canal stenosis) 28–30 . We observed 74.2%–84.2% accuracy in LDH grading without automatic detection in our prior research using a ResNet50 framework 30 .…”

Section: Discussionmentioning

confidence: 53%

“…16 Relying on the MSU and 70.6%-86.9% for central canal stenosis). [28][29][30] We observed 74.2%-84.2% accuracy in LDH grading without automatic detection in our prior research using a ResNet50 framework. 30 By comparison, this study further constructed a fully automated detection and classification workflow for grading LDH, and it obtained more optimal accuracy.…”

Section: Discussionmentioning

confidence: 88%

“…[28][29][30] We observed 74.2%-84.2% accuracy in LDH grading without automatic detection in our prior research using a ResNet50 framework. 30 By comparison, this study further constructed a fully automated detection and classification workflow for grading LDH, and it obtained more optimal accuracy. In addition, we found the trained model consumed only 20 ms on average to automatically complete this workflow in test datasets.…”

Section: Discussionmentioning

confidence: 99%

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Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images

Zhang,

Chen,

et al. 2023

JOR Spine

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BackgroundThe severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time‐consuming and requires repetitive work. This study aims to develop and evaluate a deep learning‐based diagnostic model for automated LDH detection and classification on lumbar axial T2‐weighted MR images.MethodsA total of 1115 patients were analyzed in this retrospective study; both a development dataset (1015 patients, 15 249 images) and an external test dataset (100 patients, 1273 images) were utilized. According to the Michigan State University (MSU) classification criterion, experts labeled all images with consensus, and the final labeled results were regarded as the reference standard. The automated diagnostic model comprised Faster R‐CNN and ResNeXt101 as the detection and classification network, respectively. The deep learning‐based diagnostic performance was evaluated by calculating mean intersection over union (IoU), accuracy, precision, sensitivity, specificity, F1 score, the area under the receiver operating characteristics curve (AUC), and intraclass correlation coefficient (ICC) with 95% confidence intervals (CIs).ResultsHigh detection consistency was obtained in the internal test dataset (mean IoU = 0.82, precision = 98.4%, sensitivity = 99.4%) and external test dataset (mean IoU = 0.70, precision = 96.3%, sensitivity = 97.8%). Overall accuracy for LDH classification was 87.70% (95% CI: 86.59%–88.86%) and 74.23% (95% CI: 71.83%–76.75%) in the internal and external test datasets, respectively. For internal testing, the proposed model achieved a high agreement in classification (ICC = 0.87, 95% CI: 0.86–0.88, P < 0.001), which was higher than that of external testing (ICC = 0.79, 95% CI: 0.76–0.81, P < 0.001). The AUC for model classification was 0.965 (95% CI: 0.962–0.968) and 0.916 (95% CI: 0.908–0.925) in the internal and external test datasets, respectively.ConclusionsThe automated diagnostic model achieved high performance in detecting and classifying LDH and exhibited considerable consistency with experts' classification.

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“…Running in parallel with the segmentation model, the binary multilabel stenosis classification model adopts the RegNet architecture [18], specifically implementing the…”

Section: Binary Multilabel Stenosis Classification Modelmentioning

confidence: 99%

A neural network model for detection and classification of lumbar spinal stenosis on MRI

Tumko,

Kim,

Uspenskaia

et al. 2023

Eur Spine J

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Objectives To develop a three-stage convolutional neural network (CNN) approach to segment anatomical structures, classify the presence of lumbar spinal stenosis (LSS) for all 3 stenosis types: central, lateral recess and foraminal and assess its severity on spine MRI and to demonstrate its efficacy as an accurate and consistent diagnostic tool. Methods The three-stage model was trained on 1635 annotated lumbar spine MRI studies consisting of T2-weighted sagittal and axial planes at each vertebral level. Accuracy of the model was evaluated on an external validation set of 150 MRI studies graded on a scale of absent, mild, moderate or severe by a panel of 7 radiologists. The reference standard for all types was determined by majority voting and in case of disagreement, adjudicated by an external radiologist. The radiologists’ diagnoses were then compared to the diagnoses of the model. Results The model showed comparable performance to the radiologist average both in terms of the determination of presence/absence of LSS as well as severity classification, for all 3 stenosis types. In the case of central canal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.971, 0.864, 0.963) for binary (presence/absence) classification compared to the radiologist average of (0.786, 0.899, 0.842). For lateral recess stenosis, the sensitivity, specificity and AUROC of the CNN were (0.853, 0.787, 0.907) compared to the radiologist average of (0.713, 0.898, 805). For foraminal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.942, 0.844, 0.950) compared to the radiologist average of (0.879, 0.877, 0.878). Multi-class severity classifications showed similarly comparable statistics. Conclusions The CNN showed comparable performance to radiologist subspecialists for the detection and classification of LSS. The integration of neural network models in the detection of LSS could bring higher accuracy, efficiency, consistency, and post-hoc interpretability in diagnostic practices.

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Automatic Grading of Disc Herniation, Central Canal Stenosis and Nerve Roots Compression in Lumbar Magnetic Resonance Image Diagnosis

Cited by 14 publications

References 31 publications

ChatGPT and its Role in the Decision-Making for the Diagnosis and Treatment of Lumbar Spinal Stenosis: A Comparative Analysis and Narrative Review

ChatGPT and its Role in the Decision-Making for the Diagnosis and Treatment of Lumbar Spinal Stenosis: A Comparative Analysis and Narrative Review

Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images

A neural network model for detection and classification of lumbar spinal stenosis on MRI

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