Deep Learning Based Estimation of Blood Glucose Levels from Multidirectional Scleral Blood Vessel Imaging

Abstract

Regular monitoring of glycemic status is essential for diabetes management, yet conventional blood-based testing can be burdensome for frequent assessment. The sclera contains superficial microvasculature that may exhibit diabetes related alterations and is readily visible on the ocular surface. We propose ScleraGluNet, a multiview deep-learning framework for three-class metabolic status classification (normal, controlled diabetes, and high-glucose diabetes) and continuous fasting plasma glucose (FPG) estimation from multidirectional scleral vessel images. The dataset comprised 445 participants (150/140/155) and 2,225 anterior-segment images acquired from five gaze directions per participant. After vascular enhancement, features were extracted using parallel convolutional branches, refined with Manta Ray Foraging Optimization (MRFO), and fused via transformer-based cross-view attention. Performance was evaluated using subject-wise five-fold cross-validation, with all images from each participant assigned to the same fold. ScleraGluNet achieved 93.8% overall accuracy, with one-vs-rest AUCs of 0.971,0.956, and 0.982 for normal, controlled diabetes, and high-glucose diabetes, respectively. For FPG estimation, the model achieved MAE = 6.42 mg/dL and RMSE = 7.91 mg/dL, with strong correlation to laboratory measurements (r = 0.983; R2 = 0.966). Bland Altman analysis showed a mean bias of +1.45 mg/dL with 95% limits of agreement from -8.33 to +11.23$ mg/dL. These results support multidirectional scleral vessel imaging with multiview learning as a promising noninvasive approach for glycemic assessment, warranting multicenter validation before clinical deployment.

Figures (13)

• Figure 1: Overview of the study workflow
• Figure 2: Outlines the protocol for the standardized form of scleral imaging. To check focus and alignment of the images, the first image taken is of a straight gaze position. This is followed by the capture of four other gaze direction images which are necessary for assessing all of the scleral regions. These images are taken in the upward, downward, leftward, and rightward gaze positions, respectively, so that the inferior, superior, temporal, and nasal scleral regions are photographed. By using this protocol, all slices of the scleral vasculature can be photographed for analysis from multiple views.
• Figure 3: Procedure of image pretreatment and enhancement of blood vessels. This image shows the anterior segment with raw data, scleral portion that has been cropped and normalized, an image that has vessels that have been enhanced using CLAHE, and a multi scale Frangi filter, and the related binary image of the blood vessels that has been segmented and that has been used to visualize and control the quality of the image.
• Figure 4: Structural design aspects MRFO architecture ScleraGluNet System based on Scleral images sector gaze. Parallel convolutional branches scleral images. Trained separately convolution branches scleral images gaze processed. Refined MRFO self-attention neural networks dependencies multiple views. Representation diabetes blocks. Continuous glucose monitoring higher-level output. Combination architectures used. Cross-discipline relevance diabetes.
• Figure 5: The Proposed ScleraGluNet and the Previous MRFO-INEYENET model. Despite the fact that MRFO-INEYENET relies on a single glance of ocular pictures and on the MRFO-based feature optimization, ScleraGluNet advances this system by integrating multi directional scleral imaging, and by also adding parallel, branch wise, view specific convolution and transformer based cross view fusion to aid in the simultaneous realization of classification and regression.