scholarly journals Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

10.2196/23472 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e23472
Author(s):  
Eugene Yu-Chuan Kang ◽  
Yi-Ting Hsieh ◽  
Chien-Hung Li ◽  
Yi-Jin Huang ◽  
Chang-Fu Kuo ◽  
...  
Keyword(s):  
Renal Function ◽  
Deep Learning ◽  
Learning Model ◽  
Fundus Images ◽  
Renal Function Impairment ◽  
Function Impairment ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Deep Learning Model ◽  
Hba1c Levels

Background Retinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning–based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning–based method using retinal images for detecting early renal function impairment has not yet been well studied. Objective This study aimed to develop and evaluate a deep learning model for detecting early renal function impairment using retinal fundus images. Methods This retrospective study enrolled patients who underwent renal function tests with color fundus images captured at any time between January 1, 2001, and August 31, 2019. A deep learning model was constructed to detect impaired renal function from the images. Early renal function impairment was defined as estimated glomerular filtration rate <90 mL/min/1.73 m2. Model performance was evaluated with respect to the receiver operating characteristic curve and area under the curve (AUC). Results In total, 25,706 retinal fundus images were obtained from 6212 patients for the study period. The images were divided at an 8:1:1 ratio. The training, validation, and testing data sets respectively contained 20,787, 2189, and 2730 images from 4970, 621, and 621 patients. There were 10,686 and 15,020 images determined to indicate normal and impaired renal function, respectively. The AUC of the model was 0.81 in the overall population. In subgroups stratified by serum hemoglobin A1c (HbA1c) level, the AUCs were 0.81, 0.84, 0.85, and 0.87 for the HbA1c levels of ≤6.5%, >6.5%, >7.5%, and >10%, respectively. Conclusions The deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA1c levels.

scholarly journals Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Eugene Yu-Chuan Kang ◽  
Yi-Ting Hsieh ◽  
Chien-Hung Li ◽  
Yi-Jin Huang ◽  
Chang-Fu Kuo ◽  
...  
Keyword(s):  
Renal Function ◽  
Deep Learning ◽  
Impaired Renal Function ◽  
Learning Model ◽  
Fundus Images ◽  
Renal Function Impairment ◽  
Function Impairment ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Deep Learning Model

BACKGROUND Retinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning–based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning–based method using retinal images for detecting early renal function impairment has not yet been well studied. OBJECTIVE This study aimed to develop and evaluate a deep learning model for detecting early renal function impairment using retinal fundus images. METHODS This retrospective study enrolled patients who underwent renal function tests with color fundus images captured at any time between January 1, 2001, and August 31, 2019. A deep learning model was constructed to detect impaired renal function from the images. Early renal function impairment was defined as estimated glomerular filtration rate &lt;90 mL/min/1.73 m<sup>2</sup>. Model performance was evaluated with respect to the receiver operating characteristic curve and area under the curve (AUC). RESULTS In total, 25,706 retinal fundus images were obtained from 6212 patients for the study period. The images were divided at an 8:1:1 ratio. The training, validation, and testing data sets respectively contained 20,787, 2189, and 2730 images from 4970, 621, and 621 patients. There were 10,686 and 15,020 images determined to indicate normal and impaired renal function, respectively. The AUC of the model was 0.81 in the overall population. In subgroups stratified by serum hemoglobin A<sub>1c</sub> (HbA<sub>1c</sub>) level, the AUCs were 0.81, 0.84, 0.85, and 0.87 for the HbA<sub>1c</sub> levels of ≤6.5%, &gt;6.5%, &gt;7.5%, and &gt;10%, respectively. CONCLUSIONS The deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA<sub>1c</sub> levels. CLINICALTRIAL

An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-24
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Final Diagnosis ◽  
Learning Rate ◽  
Ensemble Model ◽  
Fundus Images ◽  
Robust Detection ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) is one of the most common causes of vision loss in people who have diabetes for a prolonged period. Convolutional neural networks (CNNs) have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.

Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images

2020 ◽  
Vol 217 ◽  
pp. 121-130 ◽  
Author(s):  
Jooyoung Chang ◽  
Ahryoung Ko ◽  
Sang Min Park ◽  
Seulggie Choi ◽  
Kyuwoong Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiovascular Mortality ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals Detection of anaemia from retinal fundus images via deep learning

2019 ◽  
Vol 4 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Akinori Mitani ◽  
Abigail Huang ◽  
Subhashini Venugopalan ◽  
Greg S. Corrado ◽  
Lily Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study

10.2196/28868 ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. e28868
Author(s):  
Eugene Yu-Chuan Kang ◽  
Ling Yeung ◽  
Yi-Lun Lee ◽  
Cheng-Hsiu Wu ◽  
Shu-Yen Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Multimodal Imaging ◽  
Vascular Diseases ◽  
Learning Model ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Retinal Diseases ◽  
Ophthalmic Imaging ◽  
Retinal Fundus ◽  
Deep Learning Model

Background Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. Objective The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. Methods This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. Results A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. Conclusions Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.

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