ArticlePloS one2021
An automatic screening method for strabismus detection based on image processing.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Automated strabismus evaluation: a critical review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Article
- Deep Learning-Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization.Journal of medical Internet research · 2025Article
- High-Accuracy Intermittent Strabismus Screening via Wearable Eye-Tracking and AI-Enhanced Ocular Feature Analysis.Biosensors · 2025Article
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Strabismus Detection in Monocular Eye Images for Telemedicine Applications.Journal of imaging · 2024Article
- A retinal detachment based strabismus detection through FEDCNN.Scientific reports · 2024Article
- Multidimensional quantitative characterization of periocular morphology: distinguishing esotropia from epicanthus by deep learning network.Quantitative imaging in medicine and surgery · 2024Article
- Integrating artificial intelligence in strabismus management: current research landscape and future directions.Experimental biology and medicine (Maywood, N.J.) · 2024Review
- Reliability of a computerized system for strabismus screening.International journal of ophthalmology · 2024Article
- Feasibility study of an automated Strabismus screening Test using Augmented Reality and Eye-tracking (STARE).Eye (London, England) · 2023Article
- Global research of artificial intelligence in strabismus: a bibliometric analysis.Frontiers in medicine · 2023Article
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Advances in artificial intelligence models and algorithms in the field of optometry.Frontiers in cell and developmental biology · 2023Review
- An improved strabismus screening method with combination of meta-learning and image processing under data scarcity.PloS one · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aims to provide an automatic strabismus screening method for people who live in remote areas with poor medical accessibility. MATERIALS AND
methodsThe proposed method first utilizes a pretrained convolutional neural network-based face-detection model and a detector for 68 facial landmarks to extract the eye region for a frontal facial image. Second, Otsu's binarization and the HSV color model are applied to the image to eliminate the influence of eyelashes and canthi. Then, the method samples all of the pixel points on the limbus and applies the least square method to obtain the coordinate of the pupil center. Lastly, we calculated the distances from the pupil center to the medial and lateral canthus to measure the deviation of the positional similarity of two eyes for strabismus screening.
resultWe used a total of 60 frontal facial images (30 strabismus images, 30 normal images) to validate the proposed method. The average value of the iris positional similarity of normal images was smaller than one of the strabismus images via the method (p-value<0.001). The sample mean and sample standard deviation of the positional similarity of the normal and strabismus images were 1.073 ± 0.014 and 0.039, as well as 1.924 ± 0.169 and 0.472, respectively.
conclusionThe experimental results of 60 images show that the proposed method is a promising automatic strabismus screening method for people living in remote areas with poor medical accessibility.
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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.