Automating the Management of Extra-Spinal Findings in MRI Spine Studies Using a Privacy-Preserving Large Language Model: A Single-Institution Feasibility Study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> MRI spine studies frequently reveal extra-spinal findings (ESFs) that require further evaluation, yet the current process of manually reviewing radiology reports and navigating electronic medical records (EMRs) is time-consuming, labour-intensive and prone to human error. </sec> <sec> <title>OBJECTIVE</title> To address this challenge, we propose using a privacy-preserving large language model (PP-LLM) to automate the identification, classification, and referral assessment of extra-spinal findings (ESFs). </sec> <sec> <title>METHODS</title> A retrospective analysis of 400 consecutive MRI spine reports from the National University Hospital (NUH) database, covering February to June 2024, was conducted. Two independent clinicians reviewed the reports and cross-referenced them with EMRs to identify ESFs from the imaging reports. The CT Extracolonic Findings Reporting and Data System (C-RADS system), was adapted to determine the clinical significance of ESFs and whether specialty referral was required. The PP-LLM was designed to extract these findings, differentiate between new and pre-existing conditions, classify their clinical significance, and generate appropriate referrals. </sec> <sec> <title>RESULTS</title> A retrospective analysis of 400 consecutive MRI spine reports from the National University Hospital (NUH) database, covering February to June 2024, was conducted. Two independent clinicians reviewed the reports and cross-referenced them with EMRs to identify ESFs from the imaging reports. The CT Extracolonic Findings Reporting and Data System (C-RADS system), was adapted to determine the clinical significance of ESFs and whether specialty referral was required. The PP-LLM was designed to extract these findings, differentiate between new and pre-existing conditions, classify their clinical significance, and generate appropriate referrals. </sec> <sec> <title>CONCLUSIONS</title> The PP-LLM demonstrated high accuracy and efficiency in automating the identification and classification of extra-spinal findings in MRI spine reports. By integrating this AI-driven automation into clinical workflows, this technology has the potential to enhance efficiency, reduce clinician administrative burden, ensure timely specialist referrals and improve patient care. </sec> <sec> <title>CLINICALTRIAL</title> NA </sec>

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