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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background: Recent advancements in deep learning have significantly impacted ophthalmology, especially in glaucoma, a leading cause of irreversible blindness worldwide. In this study, we developed a reliable predictive model for glaucoma detection using deep learning models based on clinical data, social and behavior risk factor, and demographic data from 1652 participants, split evenly between 826 control subjects and 826 glaucoma patients. Methods: We extracted structural data from control and glaucoma patients’ electronic health records (EHR). Three distinct machine learning classifiers, the Random Forest and Gradient Boosting algorithms, as well as the Sequential model from the Keras library of TensorFlow, were employed to conduct predictive analyses across our dataset. Key performance metrics such as accuracy, F1 score, precision, recall, and the area under the receiver operating characteristics curve (AUC) were computed to both train and optimize these models. Results: The Random Forest model achieved an accuracy of 67.5%, with a ROC AUC of 0.67, outperforming the Gradient Boosting and Sequential models, which registered accuracies of 66.3% and 64.5%, respectively. Our results highlighted key predictive factors such as intraocular pressure, family history, and body mass index, substantiating their roles in glaucoma risk assessment. Conclusions: This study demonstrates the potential of utilizing readily available clinical, lifestyle, and demographic data from EHRs for glaucoma detection through deep learning models. While our model, using EHR data alone, has a lower accuracy compared to those incorporating imaging data, it still offers a promising avenue for early glaucoma risk assessment in primary care settings. The observed disparities in model performance and feature significance show the importance of tailoring detection strategies to individual patient characteristics, potentially leading to more effective and personalized glaucoma screening and intervention.

Details

Title
Integrating Deep Learning with Electronic Health Records for Early Glaucoma Detection: A Multi-Dimensional Machine Learning Approach
Author
Karimi, Alireza 1 ; Ansel Stanik 2   VIAFID ORCID Logo  ; Cooper Kozitza 2   VIAFID ORCID Logo  ; Chen, Aiyin 2   VIAFID ORCID Logo 

 Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, OR 97239, USA; [email protected] (A.S.); [email protected] (C.K.); [email protected] (A.C.); Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA 
 Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, OR 97239, USA; [email protected] (A.S.); [email protected] (C.K.); [email protected] (A.C.) 
First page
577
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
23065354
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3072273716
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.