Deep Learning Ensemble for Predicting Diabetic Macular Edema Onset Using Ultra-Wide Field Color Fundus Image

• URL: http://arxiv.org/abs/2410.06483v1
• Date: Wed, 9 Oct 2024 02:16:29 GMT
• Title: Deep Learning Ensemble for Predicting Diabetic Macular Edema Onset Using Ultra-Wide Field Color Fundus Image
• Authors: Pengyao Qin, Arun J. Thirunavukarasu, Le Zhang,
• Abstract summary: Diabetic macular edema (DME) is a severe complication of diabetes, characterized by thickening of the central portion of the retina due to accumulation of fluid. We propose an ensemble method to predict ci-DME onset within a year using ultra-wide-field color fundus photography images.
• Score: 3.271278111396875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diabetic macular edema (DME) is a severe complication of diabetes, characterized by thickening of the central portion of the retina due to accumulation of fluid. DME is a significant and common cause of visual impairment in diabetic patients. Center-involved DME (ci-DME) is the highest risk form of disease as fluid extends close to the fovea which is responsible for sharp central vision. Earlier diagnosis or prediction of ci-DME may improve treatment outcomes. Here, we propose an ensemble method to predict ci-DME onset within a year using ultra-wide-field color fundus photography (UWF-CFP) images provided by the DIAMOND Challenge. We adopted a variety of baseline state-of-the-art classification networks including ResNet, DenseNet, EfficientNet, and VGG with the aim of enhancing model robustness. The best performing models were Densenet 121, Resnet 152 and EfficientNet b7, and these were assembled into a definitive predictive model. The final ensemble model demonstrates a strong performance with an Area Under Curve (AUC) of 0.7017, an F1 score of 0.6512, and an Expected Calibration Error (ECE) of 0.2057 when deployed on a synthetic dataset. The performance of this ensemble model is comparable to previous studies despite training and testing in a more realistic setting, indicating the potential of UWF-CFP combined with a deep learning classification system to facilitate earlier diagnosis, better treatment decisions, and improved prognostication in ci-DME.

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