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Diabetic Retinopathy

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

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A Deep Learning Prognostic Model for Diabetes Patients Using Bilateral Fundus Imaging.

Yen-Ming Lai¹, I-Fang Chung¹, Shang-Hsuan Huang¹

¹Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei.

Journal of Diabetes Science and Technology

|March 29, 2026

Summary

This summary is machine-generated.

Diabetic patients at high risk of mortality can be identified years earlier using routine eye scans. This deep learning model predicts survival, enabling proactive interventions to extend life expectancy.

Keywords:

color fundus diabetes mortality end-to-end survival learning prognostic model

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Published on: October 23, 2020

Area of Science:

Ophthalmology
Diabetology
Artificial Intelligence

Background:

Diabetes mellitus significantly reduces life expectancy due to vascular complications.
Predicting outpatient mortality is challenging, hindering proactive care.
Routine fundus photography offers a potential avenue for early risk stratification.

Purpose of the Study:

To develop a prognostic model using deep learning and fundus photography for early identification of high-mortality-risk diabetic patients.
To enable timely interventions and improve survival rates in aging populations with diabetes.

Main Methods:

Analysis of 19,029 individuals with diabetes undergoing routine color fundus examinations.
Adaptation of deep learning architectures to Cox proportional hazards modeling for mortality prediction.
Comparison with a regulatory-approved commercial ophthalmic system.

Main Results:

The developed image-trained mortality model stratified patients into quartiles with escalating hazard ratios (HRs) up to 7.19.
The highest-risk quartile showed a five-year survival rate below 80%.
The model demonstrated superior prognostic discrimination compared to a commercial system, with HRs up to 6.89.

Conclusions:

A survival-aware, image-based model was successfully developed using routine fundus photography.
The model accurately predicts mortality risk in diabetic patients.
This prognostic tool can significantly aid in diabetes management and proactive care.