We proudly congratulate Yang Zheng and co-authors on the acceptance of “VIOMA: Video-based Intelligent Ocular Misalignment Assessment” by IEEE Transactions on Automation Science and Engineering (Feb 2025).
- Zheng, Yang; Fu, Hong; Li, Ruimin; Lam, Carly; Liang, Jimin; Guo, Kaitai; Lo, Wai-Lun: “VIOMA: Video-based Intelligent Ocular Misalignment Assessment”, IEEE Transactions on Automation Science and Engineering, Feb 2025
Paper Abstract
The measurement of ocular alignment is critical for the diagnosis of strabismus. Current clinical methods for assessing ocular misalignment are subjective and frequently rely on the expertise of practitioners and the extent of patient cooperation. Computer-aided diagnosis methods in recent years have improved automation and precision of measurement, but still, fall short of the requirement of clinical practice. In this study, a video-based intelligent ocular misalignment assessment (VIOMA) system, was proposed to provide an objective, repeatable, user-friendly and highly-automated alternative modality for clinical ocular misalignment measurement, in which the automatic cover tests were performed under a control and motor unit, simultaneously the eye movements were tracked using a motion-capture module and assessed through video analysis techniques, determining the presence, type, and magnitude of eye deviation. For system evaluation, an automatic cover tests video dataset for strabismus (StrabismusACT-76) was established, which consists of data from 76 participants. The Bland-Altman plot, used to compare the results of the VIOMA system and human expert, showed a mean value of 1.26 prism diopter (PD) and a half-width of the 95% limit of agreement of ±7.17 PD. VIOMA system presented a mean absolute error of 3.04 PD in measuring the deviation magnitude, within a 5 PD error tolerance. Additionally, the system’s measurements were strongly correlated with that of video labeling with the mean value of -0.26 PD, a half-width of the 95% limit of agreement of ±3.56, and the average error of 1.31 PD. The experiment results indicated that the proposed method has the capability to offer accurate and efficient assessment of ocular misalignment.
The research team members
Hong Kong Chu Hai College
- Wai Lun Lo, Full Professor, Director of Quality Assurance, Head, Department of Computer Science
Xidian University
- Yang Zheng – Assistant Professor, School of Electronic Engineering
- Ruimin Li – Lecturer, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education
- Jimin Liang- Full Professor, School of Electronic Engineering
- Kaitai Guo – Assistant Professor, School of Electronic Engineering
The Education University of Hong Kong
- Hong Fu – Associate Professor, Department of Mathematics and Information Technology
The Hong Kong Polytechnic University
Some photos from the paper
