ArticleTranslational vision science & technology2025
A Novel System for Measuring Eyeball Rotation Angle Based on Color Fundus Photographs in Natural Head Position.
Article in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Quantitative analysis of the effect of ocular rotation on postoperative residual astigmatism in small incision lenticule extraction for myopia correction.Frontiers in cell and developmental biology · 2025Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study presents an artificial intelligence (AI)-based system for measuring eyeball rotation angles, which is a key symptom in assessing eye disease severity. The system aims to accurately segment the optic disc and macula, and compute the eyeball rotation angle based on these features. Methods: The system consists of three modules: optic disc segmentation, macular segmentation, and measurement. The optic disc segmentation module utilizes the Efficient-UNet3+ network to address sample imbalance and irregular edge detection of the optic disc. The macular segmentation module uses the Efficient-UNet based on Dual Attention network (DA-EUNet) to enhance macular recognition and boundary feature detection while suppressing irrelevant background interference. The measurement module calculates the eyeball rotation angle by locating the centers of the optic disc and macula and determining the angle between the line connecting these centers and the horizontal vector. Results: The proposed method demonstrated high accuracy, with a correlation coefficient of 0.94 compared to expert measurements. Statistical analysis revealed no significant difference between the AI-based measurements and expert assessments (P = 0.26). Conclusions: This system achieves high accuracy and reliability in clinical diagnostics. The segmentation techniques used significantly improve feature recognition and segmentation performance, enabling accurate measurements of eyeball rotation. Translational Relevance: This AI-based system bridges the gap between basic research in medical image processing and clinical care. It provides an automated and reliable tool for ophthalmologists to assess eyeball rotation, which is crucial for diagnosing eye diseases. Eyeball rotation can occur in many eye diseases or systemic diseases, and measuring the eyeball rotation angle has been a challenging issue in clinical practice. By automating this process, the system reduces the clinicians' workload and enhances diagnostic consistency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.