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In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility
Published on: April 21, 2022
Precise staging of diabetic retinopathy through machine learning analysis of leakage source characteristics: A
Esmat Ramezanzadeh1, Hoda Zare2, Naser Shoeibi3
1Department of Medical Physics, Faculty of Medicine, North Khorasan University of Medical Sciences, Bojnourd, Iran; Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Eye Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Physics Research Center, Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
Introduction:
Evaluating retinal fundus image for diabetic retinopathy (DR) assessment is used to reduce the risk of blindness among diabetic patients. To do this, DR staging is one of the challenging tasks in screening DR that includes the assessment of disease severity or progression. We introduce a novel Radiomics and machine learning algorithm (MLA) framework that analyzes localized vascular leakage sources in Optical coherence tomography angiography (OCT-A) images, using fundus fluorescein angiography (FFA) as reference, to noninvasively stage DR. By extracting 23 optimized features from leakage-prone regions and employing X-Gradient (XGboost), Adaptive Boost, and Multilayer Perceptron (MLP) classifiers, our approach achieves unprecedented accuracy in differentiating moderate NPDR to early proliferative diabetic retinopathy.
Method:
We developed a novel radiomic and MLA framework to extract features (vascular, image-based, and clinical) from leakage source regions in OCT-A images identified by corresponding FFA images. XGboost, Adaptive Boost, MLP classifier were created to evaluate their diagnostic accuracy for different DR stages. MLA performance was comprehensively evaluated via multiple metrics (accuracy, precision, recall, AUC, sensitivity, specificity) and visualized through confusion matrices (CMs) and receiver operating characteristic (ROC) curves to ensure robust clinical applicability.
Result:
A dataset of 99 patients and 179 images was included. For staging in DR, the highest accuracy (96.6 ± 1.4% [95% CI: 93.9 - 99.3%]) and AUC (98.9 ± 0.5%) were observed for the XGBoost classifier with all image-based features alone. The MLP performed better with vascular and clinical features. (Accuracy: 92.5 ± 3.2%92.5[95% CI: 86.2 - 98.8%], AUC: 95.2%) Entropy, Haralick, GLCM, eccentricity, vessel branch, vessel density, tortuosity (combining geometric analysis and wavelet transform), fractal dimension analysis (combining box counting and wavelet-based), and fast blood sugar emerged as particularly significant discriminative features across DR stages.
Conclusion:
Our approach identifies distinctive vascular and textural biomarkers-including hybrid fractal dimension, vessel tortuosity, and Haralick features-that effectively differentiate DR stages while providing new pathophysiological insights into microvascular remodeling in leakage-prone areas. By bridging non-invasive OCT-A with leakage-specific assessment, this method offers a clinically viable tool for early detection, progression prediction, and personalized DR management. Although limited by macular-centric scans and exclusion of very early/advanced stages, our findings establish a foundation for future multi-center studies with wider fields of view. This work represents a significant advance toward AI-enhanced ophthalmology, paving the way for actionable diagnostic paradigms in diabetic eye disease.
