Related Experiment Video
Updated: Jan 24, 2026
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
Published on: December 15, 2023
Diagnostic Accuracy of Artificial Intelligence-Enabled Retinal Biomarkers for Detecting Type 2 Diabetes and
Soujanya Kaup1,2, Sudeshna Sil Kar3, Rodrigo M Carrillo-Larco4
1Department of Ophthalmology, Yenepoya (Deemed to be) University, Mangalore, India.
Purpose:
To identify retinal microvascular features that distinguish normoglycemia from diabetes mellitus (DM)[Study-1] and prediabetes (PreDM)[Study-2] among Asian Indians using artificial intelligence (AI); evaluate their diagnostic accuracy; and examine independent associations of oculomics scores [Oculomic Diabetes Score (ODiS) and Prediabetes Score (OPreS)] with glycemic status.
Methods:
We analyzed 273 retinal images from 139 participants (19 = normoglycemia, 100 = DM, 20 = PreDM; mean age: 49.4 ± 12.9 yrs; male: 71.2%) with dataset randomly split (50:50) into training and test sets. We extracted 226 quantitative vessel tortuosity features separately for arteries and veins using machine vision-based approaches. Top six discriminating features were separately selected (using Wilcoxon Rank-Sum test and Linear Discriminant Analysis) on training sets and validated on independent blinded test sets. Model performances were evaluated using area under precision-recall curve (AUPRC) for DM and area under the receiver operating characteristic curve (AUC) for PreDM. Independent association of ODiS and OPreS (adjusting for age, sex, height, weight, blood pressure, serum cholesterol, serum creatinine) was assessed by multivariable logistic regression.
Results:
Specific oculomics-based retinal vascular features distinguished DM/PreDM from normoglycemia. Vein-only model achieved AUPRC = 0.96 (95% confidence interval [95% CI]: 0.9-1.00) (sensitivity = 95%, specificity = 72.2%, precision = 95%) on Sv1 for DM and an AUC = 0.80 (95% CI: 0.63-0.94) (sensitivity = 70%, specificity = 80%, precision = 82.35%) on Sv2 for PreDM. Oculomics scores were independently associated with DM(ODiS) [Adjusted Odds ratio (AdjOR): 2.00 (95% CI: 1.56-2.57, P < 0.001)] but not with PreDM(OPreS) [AdjOR: 1.32 (95% CI: 0.65-2.71, P = 0.48)].
Conclusions:
In this proof-of-concept study, AI-informed retinal venous features on routine fundus images, with further prospective and multisite validation, could potentially serve as noninvasive DM detection using AI models among Asian Indians.
Related Concept Videos
01:22Diabetes Mellitus: Type 2 and Gestational
01:22Diabetes Mellitus: Overview and Type I Subtype
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
02:07Improving Translational Accuracy
01:29Appendicitis-II: Diagnostic Studies and Management
Diagnosing Appendicitis
It requires a multifaceted approach, starting with a detailed physical examination to pinpoint the location and nature of the pain and identify any associated symptoms. Laboratory tests play a crucial role. A complete Blood Count (CBC) typically reveals leukocytosis (an increased number of...
01:20Blood Studies for Cardiovascular System I: Cardiac Biomarkers
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
03:37Uncertainty in Measurement: Accuracy and Precision