Prospective Study Protocol for Developing an Artificial Intelligence Model to Diagnose Lumbar Intervertebral Disc Prolapse

Abstract

Background: Lumbar prolapsed intervertebral disc (PIVD) is a common musculoskeletal disorder that is routinely diagnosed clinically and confirmed by magnetic resonance imaging (MRI). Although there have been various artificial intelligence (AI) models studied as diagnostic options, they are mostly nonspecific for lumbar PIVD and do not incorporate clinical and physical examination parameters, which may contribute to misdiagnosis. This planned study aims to develop an AI-driven diagnosis tool using clinical and physical parameters and to compare its findings with MRI-based diagnoses.
Methods: The research involves two concurrent phases. Phase one includes finding applicable clinical and physical diagnostic parameters from interviews with patients, literature, and expert questionnaires. These parameters are to be used to construct a dataset from chosen participants for developing the AI model. In phase two, there will be a prospective cross-sectional study comparing the diagnostic output of the AI tool with MRI results. The performance of the AI model will be measured using standard metrics and iteratively optimized according to the results.
Results: Phase one started in October 2023 to determine diagnostic parameters. AI development data collection started in February 2024 and took 12–14 months. The future cross-sectional validation phase will follow the development of the AI model. Final study findings are expected by mid-2025.
Conclusion: Upon the development of an efficient AI tool, the model can serve as a diagnostic substitute for MRI in diagnosing lumbar PIVD, thereby improving accessibility and reducing diagnostic delays, particularly in areas with limited imaging capacity.