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Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study

PURPOSE: To predict demographic characteristics from anterior segment optical coherence tomography (AS-OCT) images of eyes using a Vision Transformer (ViT) model. METHODS: A total of 2970 AS-OCT images were used to train, validate, and test a ViT to predict age and sex, and 2616 images were used for...

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Autores principales: Lee, Yun Jeong, Choe, Sooyeon, Wy, Seoyoung, Jang, Mirinae, Jeoung, Jin Wook, Choi, Hyuk Jin, Park, Ki Ho, Sun, Sukkyu, Kim, Young Kook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652725/
https://www.ncbi.nlm.nih.gov/pubmed/36355387
http://dx.doi.org/10.1167/tvst.11.11.7
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author Lee, Yun Jeong
Choe, Sooyeon
Wy, Seoyoung
Jang, Mirinae
Jeoung, Jin Wook
Choi, Hyuk Jin
Park, Ki Ho
Sun, Sukkyu
Kim, Young Kook
author_facet Lee, Yun Jeong
Choe, Sooyeon
Wy, Seoyoung
Jang, Mirinae
Jeoung, Jin Wook
Choi, Hyuk Jin
Park, Ki Ho
Sun, Sukkyu
Kim, Young Kook
author_sort Lee, Yun Jeong
collection PubMed
description PURPOSE: To predict demographic characteristics from anterior segment optical coherence tomography (AS-OCT) images of eyes using a Vision Transformer (ViT) model. METHODS: A total of 2970 AS-OCT images were used to train, validate, and test a ViT to predict age and sex, and 2616 images were used for height, weight, and body mass index (BMI). The main outcome measure was the area under the receiver operating characteristic curve (AUC) of the ViT. RESULTS: The ViT achieved the largest AUC (0.910) for differentiating age ≤75 versus >75 years, followed by age ≤60 versus 60–75 versus >75 years (AUC, 0.844), and for discriminating sex (AUC, 0.665). The prediction abilities for the other demographic characteristics were lower: an AUC of 0.521 for classifying height ≤170 versus >170 cm in males and ≤155 versus >155 cm in females; 0.522 for weight <70 versus ≥70 kg in males and 0.503 for <55 versus ≥55 kg in females, and 0.517 for BMI <23 versus 23–25 versus ≥25 kg/m(2). Heatmaps highlighted the area of the iridocorneal angle for its contribution to the prediction of age ≤75 versus >75 years. CONCLUSIONS: Although the ViT demonstrated a good ability to classify age from AS-OCT images, it performed poorly for sex, height, weight, and BMI. The heatmap obtained of the prediction will provide clues to understanding the age-related anterior segment changes in eyes. TRANSLATIONAL RELEVANCE: The ViT can determine age-related anterior segment structural changes using AS-OCT images, which will aid clinicians in the management of ocular diseases.
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spelling pubmed-96527252022-11-15 Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study Lee, Yun Jeong Choe, Sooyeon Wy, Seoyoung Jang, Mirinae Jeoung, Jin Wook Choi, Hyuk Jin Park, Ki Ho Sun, Sukkyu Kim, Young Kook Transl Vis Sci Technol Artificial Intelligence PURPOSE: To predict demographic characteristics from anterior segment optical coherence tomography (AS-OCT) images of eyes using a Vision Transformer (ViT) model. METHODS: A total of 2970 AS-OCT images were used to train, validate, and test a ViT to predict age and sex, and 2616 images were used for height, weight, and body mass index (BMI). The main outcome measure was the area under the receiver operating characteristic curve (AUC) of the ViT. RESULTS: The ViT achieved the largest AUC (0.910) for differentiating age ≤75 versus >75 years, followed by age ≤60 versus 60–75 versus >75 years (AUC, 0.844), and for discriminating sex (AUC, 0.665). The prediction abilities for the other demographic characteristics were lower: an AUC of 0.521 for classifying height ≤170 versus >170 cm in males and ≤155 versus >155 cm in females; 0.522 for weight <70 versus ≥70 kg in males and 0.503 for <55 versus ≥55 kg in females, and 0.517 for BMI <23 versus 23–25 versus ≥25 kg/m(2). Heatmaps highlighted the area of the iridocorneal angle for its contribution to the prediction of age ≤75 versus >75 years. CONCLUSIONS: Although the ViT demonstrated a good ability to classify age from AS-OCT images, it performed poorly for sex, height, weight, and BMI. The heatmap obtained of the prediction will provide clues to understanding the age-related anterior segment changes in eyes. TRANSLATIONAL RELEVANCE: The ViT can determine age-related anterior segment structural changes using AS-OCT images, which will aid clinicians in the management of ocular diseases. The Association for Research in Vision and Ophthalmology 2022-11-10 /pmc/articles/PMC9652725/ /pubmed/36355387 http://dx.doi.org/10.1167/tvst.11.11.7 Text en Copyright 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Artificial Intelligence
Lee, Yun Jeong
Choe, Sooyeon
Wy, Seoyoung
Jang, Mirinae
Jeoung, Jin Wook
Choi, Hyuk Jin
Park, Ki Ho
Sun, Sukkyu
Kim, Young Kook
Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study
title Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study
title_full Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study
title_fullStr Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study
title_full_unstemmed Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study
title_short Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study
title_sort demographics prediction and heatmap generation from oct images of anterior segment of the eye: a vision transformer model study
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652725/
https://www.ncbi.nlm.nih.gov/pubmed/36355387
http://dx.doi.org/10.1167/tvst.11.11.7
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