ArticleInternational journal of ophthalmology2023
Artificial intelligence assisted pterygium diagnosis: current status and perspectives.
Article in International journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Flipped classroom integrated with team-based learning enhances surgical skills in ophthalmology residency training: a randomized controlled trial.Frontiers in medicine · 2026Article
- Changes in endothelial morphology and ocular aberrations in pterygium.International journal of ophthalmology · 2026Article
- Case Report: Antiviral treatment after pterygium surgery based on corneal confocal microscopy.Frontiers in medicine · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 2025Review
- Diagnostic accuracy of a deep learning model for pterygium detection in Barcelos, Brazilian Amazon.Arquivos brasileiros de oftalmologia · 2025Article
- Intermittent sliding-lock-knot suture for limbal conjunctival autograft fixation in pterygium surgery: a technique note.International journal of ophthalmology · 2024Article
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- An ensemble deep learning diagnostic system for determining Clinical Activity Scores in thyroid-associated ophthalmopathy: integrating multi-view multimodal images from anterior segment slit-lamp photographs and facial images.Frontiers in endocrinology · 2024Article
- Diabetic retinopathy identification based on multi-source-free domain adaptation.International journal of ophthalmology · 2024Article
- DBPF-net: dual-branch structural feature extraction reinforcement network for ocular surface disease image classification.Frontiers in medicine · 2023Article
Corrections and comments
- Erratum issuedCORRIGENDUM.2023
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.
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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.