ArticleThe British journal of ophthalmology2024
Accurate detection and grading of pterygium through smartphone by a fusion training model.
Article in The British journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 27 citations in OpenAlex.
- Smartphone-based lightweight AI system for real-time multiple anterior segment disease screening: development and real-world validation.BMC medicine · 2026Article
- Report Generation System for Slit-Lamp Image Interpretation Using Vision-Language Models.Ophthalmology and therapy · 2026Article
- Smartphone-Based Portable Slit Lamp in Anterior Segment Diseases: A Narrative Review of Clinical Assessment and Integration with Artificial Intelligence.Ophthalmology and therapy · 2026Review
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses.NPJ digital medicine · 2025Article
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 2025Review
- A semantic segmentation-based automatic pterygium assessment and grading system.Frontiers in medicine · 2025Article
- Large language model-based multimodal system for detecting and grading ocular surface diseases from smartphone images.Frontiers in cell and developmental biology · 2025Article
- Diagnostic accuracy of a deep learning model for pterygium detection in Barcelos, Brazilian Amazon.Arquivos brasileiros de oftalmologia · 2025Article
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images.Diagnostics (Basel, Switzerland) · 2024Article
- Evaluation of a new portable corneal topography system for self-measurement using smartphones: a pilot study.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- A Beginner's Guide to Artificial Intelligence for Ophthalmologists.Ophthalmology and therapy · 2024Review
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- Telemedicine for Cornea and External Disease: A Scoping Review of Imaging Devices.Ophthalmology and therapy · 2023Article
- Artificial intelligence assisted pterygium diagnosis: current status and perspectives.International journal of ophthalmology · 2023Article
- Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review.Advances in ophthalmology practice and researchReview
Corrections and comments
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Authors and funding
17 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimsTo improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data.
methodTwo datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea.
resultsThe detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F
conclusionOur fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands.
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