AI to predict Graves’ orbitopathy severity

Article

Based on the initial ophthalmic findings, it is difficult but important to predict the patients’ progression to a specific level of disease severity.

Image Credit: © Andriy Blokhin - stock.adobe.com

Using the 2 risk prediction methods, the investigators classified 55.3% of patients as having the mild type of disease and 44.8% as having the moderate-to-severe type. (Photo credit: Adobe Stock / Andriy Blokhin)

Factors identified in the initial finds of patients may predict the severity of Graves’ orbitopathy based on risk prediction scores that the investigators constructed,1 according to Seunghyun Lee, MD, and colleagues. He is from the Department of Ophthalmology, Konyang University, Kim’s Eye Hospital, Myung-Gok Eye Research Institute, Seoul, Republic of Korea.

Having this predictive ability is important because, as they explained, based on the initial ophthalmic findings, it is difficult but important to predict the patients’ progression to a specific level of disease severity.

In this study, the investigators constructed scores for moderate-to-severe and muscle-predominant types of Graves’ orbitopathy risk prediction based on the patients’ initial ophthalmic findings and the treatment they received at the time of the final evaluation retrospectively and constructed predictive scores that can comprehensively predict the moderate-to-severe type of Graves’ orbitopathy for Korean patients.

A total of 400 patients diagnosed with Graves’ orbitopathy at endocrinology and ophthalmology clinics were followed for at least 6 months.

Lee and colleagues constructed the scores for the moderate-to-severe type of Graves’ orbitopathy risk prediction (SMSGOP) and the scores for the muscle-predominant type of Graves’ orbitopathy risk prediction (SMGOP) using the machine learning-based automatic clinical score generation algorithm.

Using the 2 risk prediction methods, the investigators classified 55.3% of patients as having the mild type of disease and 44.8% as having the moderate-to-severe type. Among the latter patients with severe-to-moderate disease, 32.3% and 12.5% were classified as having the fat-predominant type and the muscle-predominant type, respectively.

The SMSGOP method included the patient age, central diplopia, thyroid-stimulating immunoglobulin, modified NOSPECS classification of ocular changes in Graves’ orbitopathy, clinical activity score, and the ratio of the inferior rectus muscle cross-sectional area to the total orbit at the initial examination. The SMGOP included the patient age, central diplopia, amount of eye deviation, the serum FT4 (free thyroxine) level, and the interval between diagnosis of Graves’ disease and Graves’ orbitopathy at the initial examination.

Scores of 46 using and higher the SMSGOP system and 49 and higher using the SMGOP method, had predictive value, they reported. This is the first study to analyze the initial findings that can predict the severity of Graves’ orbitopathy and to construct scores for risk prediction for Korean patients.

Based on their results, they concluded that SMSGOP and SMGOP are two models of artificial intelligence that can predict progression of Graves’ orbitopathy when doctors examine patients withGraves’ disease, based on the initial eye manifestations. The 2 systems can help explain the potential severity to patients and decide follow-up and management plans on a regular basis, they commented.

Reference
1. Lee S, Yu J, Kim Y, Kim M, Lew H. Application of an interpretable machine learning for estimating severity of Graves’ orbitopathy based on initial finding. J Clin Med. 2023;12:2640;https://doi.org/10.3390/jcm12072640

Newsletter

Want more insights like this? Subscribe to Optometry Times and get clinical pearls and practice tips delivered straight to your inbox.

Recent Videos
Matt Jones, OD; Matt Burns, OD; and Joe Sugg, OD; detailed what optometrists can expect to change when HB 1353's regulations are enacted later this year.
Dana Shannon, OD, FAAO, shares pearls on spotting red flags in need of referral and enhancing patient care with follow-up compliance.
Dana Shannon, OD, FAAO, detailed a lecture she gave at the NOA Midwestern Symposium earlier this month.
Melissa Barnett, OD, FAAO, FSLS, FBCLA, gave 2 presentations alongside other ODs and MDs at CRU 2025.
Melissa Tawa, OD, FAAO, provides insights to take glaucoma management from reactive to proactive in presentations given at CRU 2025 in Napa, California.
Rachelle Lin, OD, MS, FAAO, details her presentation on inherited retinal diseases at CRU 2025.
Jennifer Li, MD, details a talk she gave alongside Melissa Barnett, OD, FAAO, FSLS, FBCLA, at CRU 2025 in Napa, California.
Deb Ristvedt, DO, details a handful of presentations on glaucoma she gave during CRU 2025 in Napa, California.
Cecelia Koetting, OD, FAAO, DipABO, weighs in on patient assessments, staining pattern insights, and diagnostic tips for patients who may have dry eye disease.
Melissa Barnett, OD, FAAO, FSLS, FBCLA, discusses keratoconus management, diagnosis, and other key insights at CRU 2025.
© 2025 MJH Life Sciences

All rights reserved.