DR-GPT : A large language model for medical report analysis of diabetic retinopathy patients

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openAccess

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2024-10

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en

Pages

14

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PloS one, Volume 19, issue 10 October, pp. 1-14

Abstract

Diabetic retinopathy (DR) is a sight-threatening condition caused by diabetes. Screening programmes for DR include eye examinations, where the patient’s fundi are photographed, and the findings, including DR severity, are recorded in the medical report. However, statistical analyses based on DR severity require structured labels that calls for laborious manual annotation process if the report format is unstructured. In this work, we propose a large language model DR-GPT for classification of the DR severity from unstructured medical reports. On a clinical set of medical reports, DR-GPT reaches 0.975 quadratic weighted Cohen’s kappa using truncated Early Treatment Diabetic Retinopathy Study scale. When DR-GPT annotations for unlabeled data are paired with corresponding fundus images, the additional data improves image classifier performance with statistical significance. Our analysis shows that large language models can be applied for unstructured medical report databases to classify diabetic retinopathy with a variety of applications.

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Publisher Copyright: Copyright: © 2024 Jaskari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Jaskari, J, Sahlsten, J, Summanen, P, Moilanen, J, Lehtola, E, Aho, M, Säpyskä, E, Hietala, K & Kaski, K 2024, ' DR-GPT : A large language model for medical report analysis of diabetic retinopathy patients ', PloS one, vol. 19, no. 10 October, e0297706, pp. 1-14 . https://doi.org/10.1371/journal.pone.0297706