ArticleThe British journal of ophthalmology2022
Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras.
Article in The British journal of ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
- YOLOv5 Attention Analysis for Anterior Eye Disease Classification: Grad-CAM++ Feature Importance and Cut-and-Paste Validation.Journal of imaging informatics in medicine · 2026Article
- Smartphone-Assisted Placido Ring Imaging for K1 Stratification in Keratoconus: A Deep Learning Study.Diagnostics (Basel, Switzerland) · 2026Article
- AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images.Translational vision science & technology · 2026Article
- Report Generation System for Slit-Lamp Image Interpretation Using Vision-Language Models.Ophthalmology and therapy · 2026Article
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- Applications of Artificial Intelligence in Corneal Nerve Images in Ophthalmology.Diagnostics (Basel, Switzerland) · 2026Review
- The effectiveness of 0.05% cyclosporine A combined with deproteinized calf blood extract eye gel in the treatment of dry eye disease after pterygium surgery.Frontiers in medicine · 2026Article
- Multicentre Pixel-Level Tear Meniscus Segmentation Dataset with Multimodal Imaging for Dry Eye Diagnosis.Scientific data · 2025Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- A Deep Learning Model for Detecting the Eyes Receiving Glaucoma Medications Using Anterior Segment Images.Translational vision science & technology · 2025Article
- Classification of ocular surface diseases: Deep learning for distinguishing ocular surface squamous neoplasia from pterygium.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- A semantic segmentation-based automatic pterygium assessment and grading system.Frontiers in medicine · 2025Article
- SLID: a slit-lamp image dataset for deep learning-based anterior eye anatomical segmentation and multi-lesion detection.Frontiers in digital health · 2025Article
- Slit Lamp Report Generation and Question Answering: Development and Validation of a Multimodal Transformer Model with Large Language Model Integration.Journal of medical Internet research · 2024Article
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- The Use of Artificial Intelligence for Estimating Anterior Chamber Depth from Slit-Lamp Images Developed Using Anterior-Segment Optical Coherence Tomography.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases.The British journal of ophthalmology · 2024Article
- A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images.Diagnostics (Basel, Switzerland) · 2024Article
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
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14 authors.
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Abstract
BACKGROUND/
aimsTo evaluate the performances of deep learning (DL) algorithms for detection of presence and extent pterygium, based on colour anterior segment photographs (ASPs) taken from slit-lamp and hand-held cameras.
methodsReferable pterygium was defined as having extension towards the cornea from the limbus of >2.50 mm or base width at the limbus of >5.00 mm. 2503 images from the Singapore Epidemiology of Eye Diseases (SEED) study were used as the development set. Algorithms were validated on an internal set from the SEED cohort (629 images (55.3% pterygium, 8.4% referable pterygium)), and tested on two external clinic-based sets (set 1 with 2610 images (2.8% pterygium, 0.7% referable pterygium, from slit-lamp ASP); and set 2 with 3701 images, 2.5% pterygium, 0.9% referable pterygium, from hand-held ASP).
resultsThe algorithm's area under the receiver operating characteristic curve (AUROC) for detection of any pterygium was 99.5%(sensitivity=98.6%; specificity=99.0%) in internal test set, 99.1% (sensitivity=95.9%, specificity=98.5%) in external test set 1 and 99.7% (sensitivity=100.0%; specificity=88.3%) in external test set 2. For referable pterygium, the algorithm's AUROC was 98.5% (sensitivity=94.0%; specificity=95.3%) in internal test set, 99.7% (sensitivity=87.2%; specificity=99.4%) in external set 1 and 99.0% (sensitivity=94.3%; specificity=98.0%) in external set 2.
conclusionDL algorithms based on ASPs can detect presence of and referable-level pterygium with optimal sensitivity and specificity. These algorithms, particularly if used with a handheld camera, may potentially be used as a simple screening tool for detection of referable pterygium. Further validation in community setting is warranted. SYNOPSIS/PRECIS: DL algorithms based on ASPs can detect presence of and referable-level pterygium optimally, and may be used as a simple screening tool for the detection of referable pterygium in community screenings.
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