Abstract Details

Title Stroke Etiology Prediction Utilizing GPT-4

Topic Cerebrovascular Disease and Interventional Neurology

Presentation(s) P9 - Poster Session 9 (11:45 AM-12:45 PM)

Poster/Presentation
Number 14-002

Objective Based on our previous study to localize stroke lesions, we tested GPT-4 to predict stroke etiology using several key elements: brain imaging, brain vessel imaging, neck vessel imaging, transthoracic echocardiogram (TTE), and history of atrial flutter/fibrillation (AFL/AF).

Background

Determining stroke etiology requires several factors to be weighed and taken into consideration.

Design/Methods Key elements used to predict stroke etiology included brain and vessel imaging, echocardiography, a history of atrial flutter/fibrillation (AFL/AF), patient history, and laboratory data. These factors were obtained from 14 case reports and processed across five trials using GPT-4. Prompt engineering involved text classification, applying the TOAST classification system. Performance metrics, including specificity, sensitivity, precision, and F1 score, were calculated by comparing GPT-4’s predictions with the etiologies described in the case reports.

Results

The overall performance metrics demonstrated a specificity of 0.99, sensitivity of 0.97, precision of 0.96, and an F1 score of 0.97. Cardioembolic and small vessel occlusion etiologies achieved 100% sensitivity, specificity, precision, and F1 scores. Although sensitivity remained high at 0.91 for large vessel atherosclerosis, precision decreased to 0.83, resulting in an F1 score of 0.87. The "other determined" category also performed consistently well, with a specificity of 0.97, sensitivity of 0.97, precision of 0.97, and an F1 score of 0.97. This result demonstrates GPT-4's accuracy in predicting stroke etiology, with variability performance observed only in large vessel atherosclerosis cases.

Conclusions GPT-4 demonstrated high accuracy in predicting stroke etiology which suggests GPT-4’s potential utility in assisting to determine stroke etiology. However, further research with larger datasets and actual clinical data is required to test its validity.

Authors/Disclosures
Sujith Vasireddy, MBBS (SUNY Downstate) PRESENTER	Dr. Vasireddy has nothing to disclose.
Jung-Hyun Lee, MD (SUNY Downstate Medical Center)	Dr. Lee has nothing to disclose.
Sergio L. Angulo Castro, MD, PhD	Dr. Angulo Castro has nothing to disclose.
Robert McDougal	An immediate family member of Robert McDougal has received personal compensation for serving as an employee of Farnam Real Estate. The institution of Robert McDougal has received research support from NIH. Robert McDougal has a non-compensated relationship as a Webmaster with Organiztion for Computational Neuroscience that is relevant to AAN interests or activities.
William W. Lytton, MD (SUNY Downstate)	Dr. Lytton has nothing to disclose.
Steven Levine, MD, FAHA (SUNY Downstate Medical Center)	Dr. Levine has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for MEDLINK. Dr. Levine has received personal compensation in the range of $50,000-$99,999 for serving as an Expert Witness for Law Firms. The institution of Dr. Levine has received research support from NIH.

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