ArticleInternational journal of spine surgery2020
Reliability Analysis of Deep Learning Algorithms for Reporting of Routine Lumbar MRI Scans.
Article in International journal of spine surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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8 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Pooled it
- Enhancing Spinal Cord and Canal Segmentation in Degenerative Cervical Myelopathy : The Role of Interactive Learning Models with manual Click.Journal of Korean Neurosurgical Society · 2026Article
- An Evaluation of Lumbar Foraminal Stenosis Radiologic Grading Systems: A Systematic Review.Spine surgery and related research · 2026Review
- Evaluating the reproducibility of a deep learning algorithm for the prediction of retinal age.GeroScience · 2025Article
- Classification of lumbar spine disorders using large language models and MRI segmentation.BMC medical informatics and decision making · 2024Article
- Artificial intelligence: a new cutting-edge tool in spine surgery.Asian spine journal · 2024Article
- A Proposed Personalized Spine Care Protocol (SpineScreen) to Treat Visualized Pain Generators: An Illustrative Study Comparing Clinical Outcomes and Postoperative Reoperations between Targeted Endoscopic Lumbar Decompression Surgery, Minimally Invasive TLIF and Open Laminectomy.Journal of personalized medicine · 2022Article
- A deep learning model for detection of cervical spinal cord compression in MRI scans.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence could provide more accurate magnetic resonance imaging (MRI) predictors of successful clinical outcomes in targeted spine care.
objectiveTo analyze the level of agreement between lumbar MRI reports created by a deep learning neural network (RadBot) and the radiologists' MRI reading.
methodsThe compressive pathology definitions were extracted from the radiologist lumbar MRI reports from 65 patients with a total of 383 levels for the central canal: (0) no disc bulge/protrusion/canal stenosis, (1) disc bulge without canal stenosis, (2) disc bulge resulting in canal stenosis, and (3) disc herniation/protrusion/extrusion resulting in canal stenosis. For both, neural foramina were assessed with either (0) neural foraminal stenosis absent or (1) neural foramina stenosis present. Reporting criteria for the pathologies at each disc level and, when available, the grading of severity were extracted, and the Natural Language Processing model was used to generate a verbal and written report. The RadBot report was analyzed similarly as the MRI report by the radiologist. MRI reports were investigated by dichotomizing the data into 2 categories: normal and stenosis. The quality of the RadBot test was assessed by determining its sensitivity, specificity, and positive and negative predictive value as well as its reliability with the calculation of the Cronbach alpha and Cohen kappa using the radiologist MRI report as a gold standard.
resultsThe authors found a RadBot sensitivity of 73.3%, a specificity of 88.4%, a positive predictive value of 80.3%, and a negative predictive value of 83.7%. The reliability analysis revealed the Cronbach alpha as 0.772. The highest individual values of the Cronbach alpha were 0.629 and 0.681 when compared to the MRI report by the radiologist, rending values of 0.566 and 0.688, respectively. Analysis of interobserver reliability rendered an overall kappa for the RadBot of 0.627. Analysis of receiver operating characteristics (ROC) showed a value of 0.808 for the area under the ROC curve.
conclusionsDeep learning algorithms, when used for routine reporting in lumbar spine MRI, showed excellent quality as a diagnostic test that can distinguish the presence of neural element compression (stenosis) at a statistically significant level ( LEVEL OF EVIDENCE: 3. CLINICAL RELEVANCE: Validity, clinical teaching, and evaluation study.
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