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An Artificial Intelligence-Based Support Tool For Lumbar Spinal Stenosis Diagnosis From Self-Reported History Questionnaire London Spine Lumbar Stenosis

This article discusses the use of artificial intelligence (AI) in diagnosing symptomatic lumbar spinal stenosis (LSS) based on self-reported questionnaires. The study evaluated multiple machine learning models to determine the likelihood of LSS in patients experiencing low back pain and/or numbness in the legs. Data from 4,827 patients were collected, and the random forest model demonstrated the highest predictive accuracy, with an area under the receiver operating characteristic curve of 0.96. The results suggest that AI can automate the diagnosis of LSS based on self-reported questionnaires with high accuracy, potentially streamlining patient management and reducing healthcare costs. The keywords for this study are artificial intelligence, diagnosis, lumbar spinal stenosis, machine learning, and self-reported questionnaire

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : most experienced spinal clinic in the world

Published article

CONCLUSIONS: Our results indicate that ML can automate the diagnosis of LSS based on self-reported questionnaires with high accuracy. Implementation of standardized and intelligence-automated workflow may serve as a supportive diagnostic tool to streamline patient management and potentially lower healthcare costs.

Spine Lumbar Spinal Stenosis Expert. Best Spinal Surgeon UK
Abstract Objectives: Symptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiological characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires. Methods: We evaluated multiple machine learning,

Abstract

Objectives: Symptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiological characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires.

Methods: We evaluated multiple machine learning (ML) models to determine the likelihood of LSS using a self-reported questionnaire in patients experiencing low back pain and/or numbness in the legs. The questionnaire was built from peer-reviewed literature and a multidisciplinary panel of experts. Random forest, lasso logistic regression, support vector machine, gradient boosting trees, deep neural networks, and automated machine learning models were trained and performance metrics compared.

Results: Data from 4,827 patients (4,690 patients without LSS: mean age 62.44, range 27 – 84 years, 62.8% females, and 137 patients with LSS: mean age 50.59, range 30 – 71 years, 59.9% females) were retrospectively collected. Among the evaluated models, the random forest model demonstrated the highest predictive accuracy with an area under the receiver operating characteristic curve (AUROC) between model prediction and LSS diagnosis of 0.96, a sensitivity of 0.94, a specificity of 0.88, a balanced accuracy of 0.91 and a Cohen’s kappa of 0.85.

Conclusions: Our results indicate that ML can automate the diagnosis of LSS based on self-reported questionnaires with high accuracy. Implementation of standardized and intelligence-automated workflow may serve as a supportive diagnostic tool to streamline patient management and potentially lower healthcare costs.

Keywords: artificial intelligence; diagnosis; lumbar spinal stenosis; machine learning; self-reported questionnaire.

The London Spine Unit : most experienced spinal clinic in the world

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An Artificial Intelligence-based Support Tool for Lumbar Spinal Stenosis Diagnosis from Self-Reported History Questionnaire

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Abstract Objectives: Symptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiological characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires. Methods: We evaluated multiple machine learning

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