Cargando…

Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris

PURPOSE: The purpose of this study was to evaluate the diagnostic performance of deep learning (DL) anterior segment optical coherence tomography (AS-OCT) as a plateau iris prediction model. DESIGN: We used a cross-sectional study of the development and validation of the DL system. METHODS: We condu...

Descripción completa

Detalles Bibliográficos
Autores principales: Wanichwecharungruang, Boonsong, Kaothanthong, Natsuda, Pattanapongpaiboon, Warisara, Chantangphol, Pantid, Seresirikachorn, Kasem, Srisuwanporn, Chaniya, Parivisutt, Nucharee, Grzybowski, Andrzej, Theeramunkong, Thanaruk, Ruamviboonsuk, Paisan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794268/
https://www.ncbi.nlm.nih.gov/pubmed/33505774
http://dx.doi.org/10.1167/tvst.10.1.7
_version_ 1783634168292311040
author Wanichwecharungruang, Boonsong
Kaothanthong, Natsuda
Pattanapongpaiboon, Warisara
Chantangphol, Pantid
Seresirikachorn, Kasem
Srisuwanporn, Chaniya
Parivisutt, Nucharee
Grzybowski, Andrzej
Theeramunkong, Thanaruk
Ruamviboonsuk, Paisan
author_facet Wanichwecharungruang, Boonsong
Kaothanthong, Natsuda
Pattanapongpaiboon, Warisara
Chantangphol, Pantid
Seresirikachorn, Kasem
Srisuwanporn, Chaniya
Parivisutt, Nucharee
Grzybowski, Andrzej
Theeramunkong, Thanaruk
Ruamviboonsuk, Paisan
author_sort Wanichwecharungruang, Boonsong
collection PubMed
description PURPOSE: The purpose of this study was to evaluate the diagnostic performance of deep learning (DL) anterior segment optical coherence tomography (AS-OCT) as a plateau iris prediction model. DESIGN: We used a cross-sectional study of the development and validation of the DL system. METHODS: We conducted a collaboration between a referral eye center and an informative technology department. The study enrolled 179 eyes from 142 patients with primary angle closure disease (PACD). All patients had remaining appositional angle after iridotomy. Each eye was scanned in four quadrants for both AS-OCT and ultrasound biomicroscopy (UBM). A DL algorithm for plateau iris prediction of AS-OCT was developed from training datasets and was validated in test sets. Sensitivity, specificity, and area under the receiver operating characteristics curve (AUC-ROC) of the DL for predicting plateau iris were evaluated, using UBM as a reference standard. RESULTS: Total paired images of AS-OCT and UBM were from 716 quadrants. Plateau iris was observed with UBM in 276 (38.5%) quadrants. Trainings dataset with data augmentation were used to develop an algorithm from 2500 images, and the test set was validated from 160 images. AUC-ROC was 0.95 (95% confidence interval [CI] = 0.91 to 0.99), sensitivity was 87.9%, and specificity was 97.6%. CONCLUSIONS: DL revealed a high performance in predicting plateau iris on the noncontact AS-OCT images. TRANSLATIONAL RELEVANCE: This work could potentially assist clinicians in more practically detecting this nonpupillary block mechanism of PACD.
format Online
Article
Text
id pubmed-7794268
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher The Association for Research in Vision and Ophthalmology
record_format MEDLINE/PubMed
spelling pubmed-77942682021-01-26 Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris Wanichwecharungruang, Boonsong Kaothanthong, Natsuda Pattanapongpaiboon, Warisara Chantangphol, Pantid Seresirikachorn, Kasem Srisuwanporn, Chaniya Parivisutt, Nucharee Grzybowski, Andrzej Theeramunkong, Thanaruk Ruamviboonsuk, Paisan Transl Vis Sci Technol Article PURPOSE: The purpose of this study was to evaluate the diagnostic performance of deep learning (DL) anterior segment optical coherence tomography (AS-OCT) as a plateau iris prediction model. DESIGN: We used a cross-sectional study of the development and validation of the DL system. METHODS: We conducted a collaboration between a referral eye center and an informative technology department. The study enrolled 179 eyes from 142 patients with primary angle closure disease (PACD). All patients had remaining appositional angle after iridotomy. Each eye was scanned in four quadrants for both AS-OCT and ultrasound biomicroscopy (UBM). A DL algorithm for plateau iris prediction of AS-OCT was developed from training datasets and was validated in test sets. Sensitivity, specificity, and area under the receiver operating characteristics curve (AUC-ROC) of the DL for predicting plateau iris were evaluated, using UBM as a reference standard. RESULTS: Total paired images of AS-OCT and UBM were from 716 quadrants. Plateau iris was observed with UBM in 276 (38.5%) quadrants. Trainings dataset with data augmentation were used to develop an algorithm from 2500 images, and the test set was validated from 160 images. AUC-ROC was 0.95 (95% confidence interval [CI] = 0.91 to 0.99), sensitivity was 87.9%, and specificity was 97.6%. CONCLUSIONS: DL revealed a high performance in predicting plateau iris on the noncontact AS-OCT images. TRANSLATIONAL RELEVANCE: This work could potentially assist clinicians in more practically detecting this nonpupillary block mechanism of PACD. The Association for Research in Vision and Ophthalmology 2021-01-06 /pmc/articles/PMC7794268/ /pubmed/33505774 http://dx.doi.org/10.1167/tvst.10.1.7 Text en Copyright 2021 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Article
Wanichwecharungruang, Boonsong
Kaothanthong, Natsuda
Pattanapongpaiboon, Warisara
Chantangphol, Pantid
Seresirikachorn, Kasem
Srisuwanporn, Chaniya
Parivisutt, Nucharee
Grzybowski, Andrzej
Theeramunkong, Thanaruk
Ruamviboonsuk, Paisan
Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris
title Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris
title_full Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris
title_fullStr Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris
title_full_unstemmed Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris
title_short Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris
title_sort deep learning for anterior segment optical coherence tomography to predict the presence of plateau iris
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794268/
https://www.ncbi.nlm.nih.gov/pubmed/33505774
http://dx.doi.org/10.1167/tvst.10.1.7
work_keys_str_mv AT wanichwecharungruangboonsong deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris
AT kaothanthongnatsuda deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris
AT pattanapongpaiboonwarisara deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris
AT chantangpholpantid deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris
AT seresirikachornkasem deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris
AT srisuwanpornchaniya deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris
AT parivisuttnucharee deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris
AT grzybowskiandrzej deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris
AT theeramunkongthanaruk deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris
AT ruamviboonsukpaisan deeplearningforanteriorsegmentopticalcoherencetomographytopredictthepresenceofplateauiris