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Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy

Although the clinical activity score (CAS) is a validated scoring system for identifying disease activity of thyroid-associated orbitopathy (TAO), it may produce differing results depending on the evaluator, and an experienced ophthalmologist is required for accurate evaluation. In this study, we de...

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Autores principales: Moon, Jae Hoon, Shin, Kyubo, Lee, Gyeong Min, Park, Jaemin, Lee, Min Joung, Choung, Hokyung, Kim, Namju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772205/
https://www.ncbi.nlm.nih.gov/pubmed/36543834
http://dx.doi.org/10.1038/s41598-022-25887-8
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author Moon, Jae Hoon
Shin, Kyubo
Lee, Gyeong Min
Park, Jaemin
Lee, Min Joung
Choung, Hokyung
Kim, Namju
author_facet Moon, Jae Hoon
Shin, Kyubo
Lee, Gyeong Min
Park, Jaemin
Lee, Min Joung
Choung, Hokyung
Kim, Namju
author_sort Moon, Jae Hoon
collection PubMed
description Although the clinical activity score (CAS) is a validated scoring system for identifying disease activity of thyroid-associated orbitopathy (TAO), it may produce differing results depending on the evaluator, and an experienced ophthalmologist is required for accurate evaluation. In this study, we developed a machine learning (ML)-assisted system to mimic an expert’s CAS assessment using digital facial images and evaluated its accuracy for predicting the CAS and diagnosing active TAO (CAS ≥ 3). An ML-assisted system was designed to assess five CAS components related to inflammatory signs (redness of the eyelids, redness of the conjunctiva, swelling of the eyelids, inflammation of the caruncle and/or plica, and conjunctival edema) in patients’ facial images and to predict the CAS by considering two components of subjective symptoms (spontaneous retrobulbar pain and pain on gaze). To train and test the system, 3,060 cropped images from 1020 digital facial images of TAO patients were used. The reference CAS for each image was scored by three ophthalmologists, each with > 15 years of clinical experience. We repeated the experiments for 30 randomly split training and test sets at a ratio of 8:2. The sensitivity and specificity of the ML-assisted system for diagnosing active TAO were 72.7% and 83.2% in the test set constructed from the entire dataset. For the test set constructed from the dataset with consistent results for the three ophthalmologists, the sensitivity and specificity for diagnosing active TAO were 88.1% and 86.9%. In the test sets from the entire dataset and from the dataset with consistent results, 40.0% and 49.9% of the predicted CAS values were the same as the reference CAS, respectively. The system predicted the CAS within 1 point of the reference CAS in 84.6% and 89.0% of cases when tested using the entire dataset and in the dataset with consistent results, respectively. An ML-assisted system estimated the clinical activity of TAO and detect inflammatory active TAO with reasonable accuracy. The accuracy could be improved further by obtaining more data. This ML-assisted system can help evaluate the disease activity consistently as well as accurately and enable the early diagnosis and timely treatment of active TAO.
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spelling pubmed-97722052022-12-23 Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy Moon, Jae Hoon Shin, Kyubo Lee, Gyeong Min Park, Jaemin Lee, Min Joung Choung, Hokyung Kim, Namju Sci Rep Article Although the clinical activity score (CAS) is a validated scoring system for identifying disease activity of thyroid-associated orbitopathy (TAO), it may produce differing results depending on the evaluator, and an experienced ophthalmologist is required for accurate evaluation. In this study, we developed a machine learning (ML)-assisted system to mimic an expert’s CAS assessment using digital facial images and evaluated its accuracy for predicting the CAS and diagnosing active TAO (CAS ≥ 3). An ML-assisted system was designed to assess five CAS components related to inflammatory signs (redness of the eyelids, redness of the conjunctiva, swelling of the eyelids, inflammation of the caruncle and/or plica, and conjunctival edema) in patients’ facial images and to predict the CAS by considering two components of subjective symptoms (spontaneous retrobulbar pain and pain on gaze). To train and test the system, 3,060 cropped images from 1020 digital facial images of TAO patients were used. The reference CAS for each image was scored by three ophthalmologists, each with > 15 years of clinical experience. We repeated the experiments for 30 randomly split training and test sets at a ratio of 8:2. The sensitivity and specificity of the ML-assisted system for diagnosing active TAO were 72.7% and 83.2% in the test set constructed from the entire dataset. For the test set constructed from the dataset with consistent results for the three ophthalmologists, the sensitivity and specificity for diagnosing active TAO were 88.1% and 86.9%. In the test sets from the entire dataset and from the dataset with consistent results, 40.0% and 49.9% of the predicted CAS values were the same as the reference CAS, respectively. The system predicted the CAS within 1 point of the reference CAS in 84.6% and 89.0% of cases when tested using the entire dataset and in the dataset with consistent results, respectively. An ML-assisted system estimated the clinical activity of TAO and detect inflammatory active TAO with reasonable accuracy. The accuracy could be improved further by obtaining more data. This ML-assisted system can help evaluate the disease activity consistently as well as accurately and enable the early diagnosis and timely treatment of active TAO. Nature Publishing Group UK 2022-12-21 /pmc/articles/PMC9772205/ /pubmed/36543834 http://dx.doi.org/10.1038/s41598-022-25887-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Moon, Jae Hoon
Shin, Kyubo
Lee, Gyeong Min
Park, Jaemin
Lee, Min Joung
Choung, Hokyung
Kim, Namju
Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy
title Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy
title_full Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy
title_fullStr Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy
title_full_unstemmed Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy
title_short Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy
title_sort machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772205/
https://www.ncbi.nlm.nih.gov/pubmed/36543834
http://dx.doi.org/10.1038/s41598-022-25887-8
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