Enhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images

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dc.contributor.authorVisitsattapongse, Sarinporn
dc.contributor.authorRithcharung, Preeyarat
dc.contributor.authorSantiprabhob, Jeerunda
dc.contributor.authorLertbannaphong, Ornsuda
dc.contributor.authorSermsripong, Wasawat
dc.contributor.authorMontrisuksirikun, Chinnapat
dc.contributor.authorAtchaneeyasakul, La ongsri
dc.contributor.authorPintavirooj, Chuchart
dc.contributor.authorPaing, May Phu
dc.date.accessioned2026-08-06T10:53:29Z
dc.date.available2026-08-06T10:53:29Z
dc.date.issued2026-01-01
dc.description.abstractBackground: Diabetes mellitus (DM) is a chronic metabolic disease characterized by dysregulated blood glucose. Prolonged DM can lead to diabetic retinopathy (DR), in which retinal capillaries are damaged by sustained hyperglycemia. Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality for visualizing retinal microvasculature and can detect early changes in both DM patients with and without DR. However, it requires expert evaluation, making early detection costly and time-consuming. This study aimed to develop a high-performance deep learning framework that can classify OCTA images into three groups of DM, such as normal, good glycemic control, and poor glycemic control. Methods: OCTA datasets of horizontal B-scans and en face scans from 300 participants aged 8–18 years were analyzed, including normal controls, DM patients with good glycemic control, and DM patients with poor control (HbA1c ≥8%). For each participant, a 3 mm × 3 mm foveal-centered en face image of the deep capillary plexus (DCP) and a horizontal B-scan through the foveal center of the right eye were selected. Several convolutional and transformer-based models were evaluated, with ConvNeXt (a ConvNet for the 2020s) chosen as the baseline for its superior performance. To enhance generalization and convergence, progressive resizing and the Lookahead optimization strategy were applied, while class-wise augmentation was used to balance the training set without altering the test distribution. Results: The baseline ConvNeXt achieved F1 scores of 0.7877 (B-scans) and 0.7424 (en face). After doing enhancement using progressive resizing and Lookahead optimization, performance improved to 0.8319 and 0.8567 (Wilcoxon signed-rank tests, P<0.05). Conclusions: Our proposed method for DM classification from OCTA images provided promising results while ensuring resource efficiency and rapid evaluation. Clinically, accurate classification of DM status is valuable for assessing the risk of DR progression. Thus, it can be served as an assistive tool for clinical decision support in DR management.
dc.identifier.citationQuantitative Imaging in Medicine and Surgery, 16(6), 2026
dc.identifier.doi10.21037/qims-2025-aw-2422
dc.identifier.issn22234292
dc.identifier.other2-s2.0-105043369174
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17570
dc.sourceQuantitative Imaging in Medicine and Surgery
dc.subjectDeep learning
dc.subjectdiabetes mellitus (DM)
dc.subjectdiabetic retinopathy (DR)
dc.subjectLookahead optimization
dc.subjectoptical coherence tomography angiography (OCTA)
dc.titleEnhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images
dc.typeArticle

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