Clinical ophthalmology (Auckland, N.Z.) · 2026
Development and Validation of a Novel Deep Learning-Based Model for Detection of Diabetic Kidney Disease from Retinal Imaging Using a Weighted Loss Method.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Prayitnaningsih Seskoati, Syarifuddin Othe Ahmad, Dhani Fauzan Kurniawan, Novita Hera Dwi, Samsu Nur, Sasongko Muhammad Bayu, Dewi Candra, Yudistira Novanto
Аннотация
BACKGROUND: Retinal photographs offer great opportunity to early detect systemic disorders related to diabetes, including Chronic Kidney Disease (CKD).
PURPOSE: To develop and validate a novel deep learning model to detect CKD among diabetic patients.
PATIENTS AND METHODS: We developed an EfficientNet-B2 Deep Learning (DL) model utilizing a weighted cross-entropy loss function to address class imbalance and distinguish retinal images among healthy controls, patients with isolated type 2 diabetes mellitus (T2DM), and patients with CKD stage 3 due to T2DM. The dataset was partitioned using a strict 80/20 patient-level split to evaluate bilateral eyes while strictly preventing data leakage. Model performance was evaluated using sensitivity, specificity, and area under the curve (AUC), alongside Grad-CAM visualizations for clinical interpretability.
RESULTS: The study included 225 participants. Among the evaluated DL architectures, the EfficientNet-B2 model demonstrated the best performance, achieving an overall AUC of 0.96. The model exhibited a sensitivity of 82%, specificity of 94%, precision of 81%, and an F1-score of 0.80. Class-specific AUCs were 0.99 for healthy controls, 0.90 for T2DM without CKD, and 0.90 for T2DM with CKD stage 3. Grad-CAM heatmaps indicated that the model primarily focused on the peripapillary and macular regions to make predictions.
CONCLUSION: This study presents a three-class fundus-based DL model, trained with a weighted-loss strategy, to differentiate controls, isolated T2DM, and T2DM with CKD stage 3. Further external and prospective validation is needed before it can be considered for screening/triage use.
PURPOSE: To develop and validate a novel deep learning model to detect CKD among diabetic patients.
PATIENTS AND METHODS: We developed an EfficientNet-B2 Deep Learning (DL) model utilizing a weighted cross-entropy loss function to address class imbalance and distinguish retinal images among healthy controls, patients with isolated type 2 diabetes mellitus (T2DM), and patients with CKD stage 3 due to T2DM. The dataset was partitioned using a strict 80/20 patient-level split to evaluate bilateral eyes while strictly preventing data leakage. Model performance was evaluated using sensitivity, specificity, and area under the curve (AUC), alongside Grad-CAM visualizations for clinical interpretability.
RESULTS: The study included 225 participants. Among the evaluated DL architectures, the EfficientNet-B2 model demonstrated the best performance, achieving an overall AUC of 0.96. The model exhibited a sensitivity of 82%, specificity of 94%, precision of 81%, and an F1-score of 0.80. Class-specific AUCs were 0.99 for healthy controls, 0.90 for T2DM without CKD, and 0.90 for T2DM with CKD stage 3. Grad-CAM heatmaps indicated that the model primarily focused on the peripapillary and macular regions to make predictions.
CONCLUSION: This study presents a three-class fundus-based DL model, trained with a weighted-loss strategy, to differentiate controls, isolated T2DM, and T2DM with CKD stage 3. Further external and prospective validation is needed before it can be considered for screening/triage use.