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Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding

(1) Background: We constructed scores for moderate-to-severe and muscle-predominant types of Graves’ orbitopathy (GO) risk prediction based on initial ophthalmic findings. (2) Methods: 400 patients diagnosed with GO and followed up at both endocrinology and ophthalmology clinics with at least 6 mont...

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Autores principales: Lee, Seunghyun, Yu, Jaeyong, Kim, Yuri, Kim, Myungjin, Lew, Helen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10095042/
https://www.ncbi.nlm.nih.gov/pubmed/37048722
http://dx.doi.org/10.3390/jcm12072640
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author Lee, Seunghyun
Yu, Jaeyong
Kim, Yuri
Kim, Myungjin
Lew, Helen
author_facet Lee, Seunghyun
Yu, Jaeyong
Kim, Yuri
Kim, Myungjin
Lew, Helen
author_sort Lee, Seunghyun
collection PubMed
description (1) Background: We constructed scores for moderate-to-severe and muscle-predominant types of Graves’ orbitopathy (GO) risk prediction based on initial ophthalmic findings. (2) Methods: 400 patients diagnosed with GO and followed up at both endocrinology and ophthalmology clinics with at least 6 months of follow-up. The Score for Moderate-to-Severe type of GO risk Prediction (SMSGOP) and the Score for Muscle-predominant type of GO risk Prediction (SMGOP) were constructed using the machine learning-based automatic clinical score generation algorithm. (3) Results: 55.3% were classified as mild type and 44.8% were classified as moderate-to-severe type. In the moderate-to-severe type group, 32.3% and 12.5% were classified as fat-predominant and muscle-predominant type, respectively. SMSGOP included age, central diplopia, thyroid stimulating immunoglobulin, modified NOSPECS classification, clinical activity score and ratio of the inferior rectus muscle cross-sectional area to total orbit in initial examination. SMGOP included age, central diplopia, amount of eye deviation, serum FT4 level and the interval between diagnosis of GD and GO in initial examination. Scores ≥46 and ≥49 had predictive value, respectively. (4) Conclusions: This is the first study to analyze factors in initial findings that can predict the severity of GO and to construct scores for risk prediction for Korean. We set the predictive scores using initial findings.
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spelling pubmed-100950422023-04-13 Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding Lee, Seunghyun Yu, Jaeyong Kim, Yuri Kim, Myungjin Lew, Helen J Clin Med Article (1) Background: We constructed scores for moderate-to-severe and muscle-predominant types of Graves’ orbitopathy (GO) risk prediction based on initial ophthalmic findings. (2) Methods: 400 patients diagnosed with GO and followed up at both endocrinology and ophthalmology clinics with at least 6 months of follow-up. The Score for Moderate-to-Severe type of GO risk Prediction (SMSGOP) and the Score for Muscle-predominant type of GO risk Prediction (SMGOP) were constructed using the machine learning-based automatic clinical score generation algorithm. (3) Results: 55.3% were classified as mild type and 44.8% were classified as moderate-to-severe type. In the moderate-to-severe type group, 32.3% and 12.5% were classified as fat-predominant and muscle-predominant type, respectively. SMSGOP included age, central diplopia, thyroid stimulating immunoglobulin, modified NOSPECS classification, clinical activity score and ratio of the inferior rectus muscle cross-sectional area to total orbit in initial examination. SMGOP included age, central diplopia, amount of eye deviation, serum FT4 level and the interval between diagnosis of GD and GO in initial examination. Scores ≥46 and ≥49 had predictive value, respectively. (4) Conclusions: This is the first study to analyze factors in initial findings that can predict the severity of GO and to construct scores for risk prediction for Korean. We set the predictive scores using initial findings. MDPI 2023-04-01 /pmc/articles/PMC10095042/ /pubmed/37048722 http://dx.doi.org/10.3390/jcm12072640 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Seunghyun
Yu, Jaeyong
Kim, Yuri
Kim, Myungjin
Lew, Helen
Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding
title Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding
title_full Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding
title_fullStr Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding
title_full_unstemmed Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding
title_short Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding
title_sort application of an interpretable machine learning for estimating severity of graves’ orbitopathy based on initial finding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10095042/
https://www.ncbi.nlm.nih.gov/pubmed/37048722
http://dx.doi.org/10.3390/jcm12072640
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