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A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach
Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models (DLM) may help identify VF worsening consistently and reproducibly. In this study, we developed and investigated the performance of a DLM on a large p...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852268/ https://www.ncbi.nlm.nih.gov/pubmed/36658309 http://dx.doi.org/10.1038/s41598-023-28003-6 |
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author | Sabharwal, Jasdeep Hou, Kaihua Herbert, Patrick Bradley, Chris Johnson, Chris A. Wall, Michael Ramulu, Pradeep Y. Unberath, Mathias Yohannan, Jithin |
author_facet | Sabharwal, Jasdeep Hou, Kaihua Herbert, Patrick Bradley, Chris Johnson, Chris A. Wall, Michael Ramulu, Pradeep Y. Unberath, Mathias Yohannan, Jithin |
author_sort | Sabharwal, Jasdeep |
collection | PubMed |
description | Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models (DLM) may help identify VF worsening consistently and reproducibly. In this study, we developed and investigated the performance of a DLM on a large population of glaucoma patients. We included 5099 patients (8705 eyes) seen at one institute from June 1990 to June 2020 that had VF testing as well as clinician assessment of VF worsening. Since there is no gold standard to identify VF worsening, we used a consensus of six commonly used algorithmic methods which include global regressions as well as point-wise change in the VFs. We used the consensus decision as a reference standard to train/test the DLM and evaluate clinician performance. 80%, 10%, and 10% of patients were included in training, validation, and test sets, respectively. Of the 873 eyes in the test set, 309 [60.6%] were from females and the median age was 62.4; (IQR 54.8–68.9). The DLM achieved an AUC of 0.94 (95% CI 0.93–0.99). Even after removing the 6 most recent VFs, providing fewer data points to the model, the DLM successfully identified worsening with an AUC of 0.78 (95% CI 0.72–0.84). Clinician assessment of worsening (based on documentation from the health record at the time of the final VF in each eye) had an AUC of 0.64 (95% CI 0.63–0.66). Both the DLM and clinician performed worse when the initial disease was more severe. This data shows that a DLM trained on a consensus of methods to define worsening successfully identified VF worsening and could help guide clinicians during routine clinical care. |
format | Online Article Text |
id | pubmed-9852268 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98522682023-01-21 A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach Sabharwal, Jasdeep Hou, Kaihua Herbert, Patrick Bradley, Chris Johnson, Chris A. Wall, Michael Ramulu, Pradeep Y. Unberath, Mathias Yohannan, Jithin Sci Rep Article Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models (DLM) may help identify VF worsening consistently and reproducibly. In this study, we developed and investigated the performance of a DLM on a large population of glaucoma patients. We included 5099 patients (8705 eyes) seen at one institute from June 1990 to June 2020 that had VF testing as well as clinician assessment of VF worsening. Since there is no gold standard to identify VF worsening, we used a consensus of six commonly used algorithmic methods which include global regressions as well as point-wise change in the VFs. We used the consensus decision as a reference standard to train/test the DLM and evaluate clinician performance. 80%, 10%, and 10% of patients were included in training, validation, and test sets, respectively. Of the 873 eyes in the test set, 309 [60.6%] were from females and the median age was 62.4; (IQR 54.8–68.9). The DLM achieved an AUC of 0.94 (95% CI 0.93–0.99). Even after removing the 6 most recent VFs, providing fewer data points to the model, the DLM successfully identified worsening with an AUC of 0.78 (95% CI 0.72–0.84). Clinician assessment of worsening (based on documentation from the health record at the time of the final VF in each eye) had an AUC of 0.64 (95% CI 0.63–0.66). Both the DLM and clinician performed worse when the initial disease was more severe. This data shows that a DLM trained on a consensus of methods to define worsening successfully identified VF worsening and could help guide clinicians during routine clinical care. Nature Publishing Group UK 2023-01-19 /pmc/articles/PMC9852268/ /pubmed/36658309 http://dx.doi.org/10.1038/s41598-023-28003-6 Text en © The Author(s) 2023 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 Sabharwal, Jasdeep Hou, Kaihua Herbert, Patrick Bradley, Chris Johnson, Chris A. Wall, Michael Ramulu, Pradeep Y. Unberath, Mathias Yohannan, Jithin A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach |
title | A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach |
title_full | A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach |
title_fullStr | A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach |
title_full_unstemmed | A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach |
title_short | A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach |
title_sort | deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852268/ https://www.ncbi.nlm.nih.gov/pubmed/36658309 http://dx.doi.org/10.1038/s41598-023-28003-6 |
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