Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images
To develop a multimodal model to automate glaucoma detection Development of a machine-learning glaucoma detection model We selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combi...
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| Published in | American journal of ophthalmology Vol. 231; pp. 154 - 169 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.11.2021
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0002-9394 1879-1891 1879-1891 |
| DOI | 10.1016/j.ajo.2021.04.021 |
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| Abstract | To develop a multimodal model to automate glaucoma detection
Development of a machine-learning glaucoma detection model
We selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]).
Results show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma—age and pulmonary function.
The accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease. |
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| AbstractList | To develop a multimodal model to automate glaucoma detection DESIGN: Development of a machine-learning glaucoma detection model METHODS: We selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]).PURPOSETo develop a multimodal model to automate glaucoma detection DESIGN: Development of a machine-learning glaucoma detection model METHODS: We selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]).Results show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma-age and pulmonary function.RESULTSResults show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma-age and pulmonary function.The accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease.CONCLUSIONSThe accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease. PurposeTo develop a multimodal model to automate glaucoma detectionDesignDevelopment of a machine-learning glaucoma detection modelMethodsWe selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]).ResultsResults show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma—age and pulmonary function.ConclusionsThe accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease. To develop a multimodal model to automate glaucoma detection Development of a machine-learning glaucoma detection model We selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]). Results show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma—age and pulmonary function. The accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease. To develop a multimodal model to automate glaucoma detection DESIGN: Development of a machine-learning glaucoma detection model METHODS: We selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]). Results show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma-age and pulmonary function. The accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease. |
| Author | Lee, Aaron Y. Chen, Philip P. Bojikian, Karine D. Petersen, Christine A. Wen, Joanne C. Lee, Su-In Balazinska, Magdalena Mehta, Parmita Rokem, Ariel Banitt, Michael R. Egan, Catherine |
| Author_xml | – sequence: 1 givenname: Parmita surname: Mehta fullname: Mehta, Parmita organization: From the Paul G. Allen School of Computer Science and Engineering, Seattle, Washington, USA (PM, S-IL, MB) – sequence: 2 givenname: Christine A. orcidid: 0000-0002-8400-3721 surname: Petersen fullname: Petersen, Christine A. organization: Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL) – sequence: 3 givenname: Joanne C. surname: Wen fullname: Wen, Joanne C. organization: Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL) – sequence: 4 givenname: Michael R. surname: Banitt fullname: Banitt, Michael R. organization: Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL) – sequence: 5 givenname: Philip P. surname: Chen fullname: Chen, Philip P. organization: Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL) – sequence: 6 givenname: Karine D. surname: Bojikian fullname: Bojikian, Karine D. organization: Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL) – sequence: 7 givenname: Catherine surname: Egan fullname: Egan, Catherine organization: Moorfields Eye Hospital, NHS Trust, UK (C.E.) – sequence: 8 givenname: Su-In surname: Lee fullname: Lee, Su-In organization: From the Paul G. Allen School of Computer Science and Engineering, Seattle, Washington, USA (PM, S-IL, MB) – sequence: 9 givenname: Magdalena surname: Balazinska fullname: Balazinska, Magdalena organization: From the Paul G. Allen School of Computer Science and Engineering, Seattle, Washington, USA (PM, S-IL, MB) – sequence: 10 givenname: Aaron Y. surname: Lee fullname: Lee, Aaron Y. organization: Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL) – sequence: 11 givenname: Ariel orcidid: 0000-0003-0679-1985 surname: Rokem fullname: Rokem, Ariel email: arokem@uw.edu organization: eScience Institute, Seattle, Washington, USA (MB, AR) |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33945818$$D View this record in MEDLINE/PubMed |
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Development of a machine-learning glaucoma detection model
We selected a study cohort from the UK... To develop a multimodal model to automate glaucoma detection DESIGN: Development of a machine-learning glaucoma detection model METHODS: We selected a study... PurposeTo develop a multimodal model to automate glaucoma detectionDesignDevelopment of a machine-learning glaucoma detection modelMethodsWe selected a study... |
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| SubjectTerms | Asymptomatic Biobanks Cataracts Datasets Diabetes Diabetic retinopathy Glaucoma Glaucoma - diagnosis Human subjects Humans Intraocular Pressure Machine Learning Macular degeneration Optic Disk Questionnaires Tomography, Optical Coherence |
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| Title | Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images |
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