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A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography
PURPOSE: The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans. METHODS: A DL framework was developed consisting of a classification model and an...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728496/ https://www.ncbi.nlm.nih.gov/pubmed/36458946 http://dx.doi.org/10.1167/tvst.11.12.3 |
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author | Schwartz, Roy Khalid, Hagar Liakopoulos, Sandra Ouyang, Yanling de Vente, Coen González-Gonzalo, Cristina Lee, Aaron Y. Guymer, Robyn Chew, Emily Y. Egan, Catherine Wu, Zhichao Kumar, Himeesh Farrington, Joseph Müller, Philipp L. Sánchez, Clara I. Tufail, Adnan |
author_facet | Schwartz, Roy Khalid, Hagar Liakopoulos, Sandra Ouyang, Yanling de Vente, Coen González-Gonzalo, Cristina Lee, Aaron Y. Guymer, Robyn Chew, Emily Y. Egan, Catherine Wu, Zhichao Kumar, Himeesh Farrington, Joseph Müller, Philipp L. Sánchez, Clara I. Tufail, Adnan |
author_sort | Schwartz, Roy |
collection | PubMed |
description | PURPOSE: The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans. METHODS: A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), kappa, accuracy, intraclass correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curves. RESULTS: The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification models). The mean ICC for the drusen and RPD area versus graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. CONCLUSIONS: The models achieved high classification and segmentation performance, similar to human performance. TRANSLATIONAL RELEVANCE: Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings. |
format | Online Article Text |
id | pubmed-9728496 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-97284962022-12-08 A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography Schwartz, Roy Khalid, Hagar Liakopoulos, Sandra Ouyang, Yanling de Vente, Coen González-Gonzalo, Cristina Lee, Aaron Y. Guymer, Robyn Chew, Emily Y. Egan, Catherine Wu, Zhichao Kumar, Himeesh Farrington, Joseph Müller, Philipp L. Sánchez, Clara I. Tufail, Adnan Transl Vis Sci Technol Artificial Intelligence PURPOSE: The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans. METHODS: A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), kappa, accuracy, intraclass correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curves. RESULTS: The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification models). The mean ICC for the drusen and RPD area versus graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. CONCLUSIONS: The models achieved high classification and segmentation performance, similar to human performance. TRANSLATIONAL RELEVANCE: Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings. The Association for Research in Vision and Ophthalmology 2022-12-02 /pmc/articles/PMC9728496/ /pubmed/36458946 http://dx.doi.org/10.1167/tvst.11.12.3 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 Schwartz, Roy Khalid, Hagar Liakopoulos, Sandra Ouyang, Yanling de Vente, Coen González-Gonzalo, Cristina Lee, Aaron Y. Guymer, Robyn Chew, Emily Y. Egan, Catherine Wu, Zhichao Kumar, Himeesh Farrington, Joseph Müller, Philipp L. Sánchez, Clara I. Tufail, Adnan A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography |
title | A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography |
title_full | A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography |
title_fullStr | A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography |
title_full_unstemmed | A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography |
title_short | A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography |
title_sort | deep learning framework for the detection and quantification of reticular pseudodrusen and drusen on optical coherence tomography |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728496/ https://www.ncbi.nlm.nih.gov/pubmed/36458946 http://dx.doi.org/10.1167/tvst.11.12.3 |
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