eess.IVMay 24, 2026

Explainable Multi-Task Retinal Imaging Reveals Microvascular Signals for Systemic Risk Stratification in Type 2 Diabetes: A Pilot Study

Authors: Mini Han Wang, Liting Huang, Wei Hong, Boonthawan Wingwon

Organizations: Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, P.R.China · Frontier Science Computing Center, Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, P.R.China · Chinese University of Hong Kong, Hong Kong China · Zhuhai People's Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University), Zhuhai P.R.China · Lampang Inter-Tech College, Lampang Thailand

Abstract

Retinal imaging provides a non-invasive window into systemic microvascular health and has emerged as a potential biomarker for systemic diseases. However, whether retinal features encode biologically meaningful systemic signals that can be reliably interpreted using explainable artificial intelligence (XAI) remains unclear. An explainable multi-task deep learning framework was developed to investigate associations between retinal microvascular features and systemic abnormalities in Type 2 Diabetes Mellitus. A total of 11,011 fundus images from 2,719 individuals were analysed using a shared neural network with task-specific heads for glycaemic status, kidney abnormality, and multi-system involvement. Model interpretability was evaluated using Gradient-weighted Class Activation Mapping (Grad-CAM), anatomical masking, and vessel alignment analysis. The framework demonstrated task-dependent predictive performance, with the best discrimination observed for kidney abnormality (AUC up to 0.63), whereas glycaemic status prediction showed limited performance (AUC = 0.49-0.61). Explainability analyses consistently localized model attention to retinal vessels and peripapillary regions. Masking experiments showed that occlusion of vascular regions caused the greatest performance decline, indicating that retinal vessels were the primary predictive source. Different architectures exhibited heterogeneous attention patterns, suggesting multiple representational pathways for systemic signal encoding. This pilot study demonstrates that retinal microvascular features contain measurable signals associated with systemic abnormalities, particularly microvascular damage. By integrating multi-task learning with quantitative XAI validation, this framework advances retinal imaging toward interpretable digital biomarkers for systemic risk stratification in diabetes.

Explore similar work

CardsList
May 24, 2026cs.LG

Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes

Background: Type 2 diabetes mellitus (T2DM) is increasingly recognised as a systemic disease characterised by coordinated dysfunction across metabolic, renal, lipid, and inflammatory pathways. Existing clinical assessments often fail to capture this multi-dimensional burden. Methods: We conducted a retrospective study of 1,195 patients using routinely collected laboratory biomarkers. System-level abnormality indices were constructed to quantify organ-specific dysfunction, and multi-system involvement was defined as abnormalities in two or more systems. Supervised machine learning models, including logistic regression, random forest, and gradient boosting, were trained to predict multi-system dysregulation. Model interpretability was achieved using SHapley Additive exPlanations (SHAP). Results: The gradient boosting model demonstrated near-perfect discrimination (AUC = 1.000), significantly outperforming logistic regression (AUC = 0.925). Feature attribution analysis revealed that hyperglycaemia, renal impairment, dyslipidaemia, and inflammation were the dominant drivers of multi-system risk. Dose-response relationships observed in partial dependence analyses further supported the biological plausibility of model predictions. Conclusion: This study presents an interpretable, data-driven framework for quantifying systemic disease burden in T2DM. By linking routine biomarkers to multi-organ dysfunction, our approach provides both predictive accuracy and mechanistic insight, offering potential for improved risk stratification and precision medicine in diabetes care. The data and code used in this study are openly available on GitHub at: https://github.com/MiniHanWang/Type-2-Diabetes-1.git
Mini Han Wang, Liting Huang, Wei Hong +1
Interpretable Diabetic Retinopathy GradingHypoglycemia ClassificationBiomarkerClinical PredictionRetinal ImagingModel Interpretability
AskPDF
Jul 6, 2026cs.CV

Causal-RetiGraph: Cross-Cohort Retinal Support and Same-Subject Pathway Analysis for Diabetic Retinopathy

Diabetic retinopathy (DR) is a local retinal lesion process and a visible manifestation of systemic microvascular injury. Modern retinal AI can grade images accurately, but often leaves unanswered how local lesion evidence, retinal vascular structure, and systemic disease pathways are connected. This paper introduces \emph{Causal-RetiGraph}, a compact biomedical informatics framework that links retinal graph phenotypes with NHANES-anchored pathway modelling. The retinal-image fold constructs an interpretable X1234X1234 phenotype from vessel maps, lesion evidence, image embeddings, and AutoMorph biomarkers through spatial X12X_{12} and Jacobian X34X_{34} branches. The NHANES fold models systemic exposures, covariates, a same-subject retinal mediator family R∗R^*, and downstream outcome families. X1234X1234 is used for retinal support and pathway prioritisation, while R∗R^* is used for participant-level pathway summaries. On the retinal fold, X1234X1234 achieves 0.9055 binary DR accuracy and 0.9711 AUROC, with graded DR QWK of 0.8312. The results show that lesion and biomarker streams improve contextual retinal representation under scarce and imbalanced data. In NHANES, HbA1c, urine albumin, pulse pressure, fasting glucose, and systolic blood pressure are the strongest binary DR anchors. Participant-level pathway analysis identifies glycaemic--renal and glycaemic--haemodynamic pathways as the clearest mediator-style signals. These results suggest that retinal graph phenotypes can help prioritise systemic pathways in DR while preserving the distinction between image-derived support and same-subject mediation.
Inam Ullah, Imran Razzak, Shoaib Jameel
Interpretable Diabetic Retinopathy GradingRetinal ImagingBiomarkerPatient Trajectories
AskPDF
Feb 23, 2025cs.CV

Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

Interpretability is crucial for utilizing machine learning models as clinical decision support tools for medical diagnostics. However, most state-of-the-art image classifiers based on neural networks are not interpretable. As a result, clinicians often resort to known biomarkers to guide diagnosis, although biomarker-based classification often suffers from drastic information loss compared to raw medical images. This work proposes a method that preserves the rich imaging information while simultaneously enhancing the interpretability of predictions for diabetic retinopathy staging from optical coherence tomography angiography (OCTA) images. The core contribution of our method is a novel biology-informed heterogeneous graph representation that models retinal vessel segments, intercapillary areas, and the foveal avascular zone (FAZ) in a human-interpretable way. This graph representation allows us to frame diabetic retinopathy staging as a graph-level classification task, which we solve using an established, efficient graph neural network architecture. We compare our method against established methods, including classical biomarker-based classifiers, convolutional neural networks (CNNs), and vision transformers in predicting the clinically assigned DR stage based on color fundus photography images. We find stage agreement rates of our method and alternative vision model based classifiers saturating at AUC-ROC values of 84%. Crucially, we use our biology-informed graph to provide explanations of great detail. Our approach surpasses existing methods in precisely localizing and identifying abnormal vessels and non-perfusion areas. Our approach sets the stage for the interpretable identification of patients who require special attention due to their traceable microvascular changes, only observable using the details of OCTA images.
Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8
Interpretable Diabetic Retinopathy GradingMedical Image ClassificationBiomarkerCoronary AngiographyConvolutional Neural Networks
AskPDF