Transl Vis Sci Technol. 2025 Aug 1;14(8):25. doi: 10.1167/tvst.14.8.25.
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.
PMID:40828527 | DOI:10.1167/tvst.14.8.25
