ArticleBMJ open2020
Artificial intelligence method to classify ophthalmic emergency severity based on symptoms: a validation study.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Enhancing access and clinical triage in primary eye care through digital vision testing: validation of the SightConnect mobile application.Frontiers in digital health · 2026Article
- Article
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- The Role of Artificial Intelligence in Triaging Patients in Eye Casualty Departments: A Systematic Review.Cureus · 2025Review
- Corneal thermal burn injuries during long-duration spaceflight: mechanisms, evaluation, and management.Eye (London, England) · 2024Article
- The evaluation and management of corneal penetrating and perforating injuries during long-duration spaceflight.Eye (London, England) · 2024Article
- Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis.Frontiers in pharmacology · 2022Article
- Artificial intelligence approach for recommendation of pupil dilation test using medical interview and basic ophthalmologic examinations.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe investigated the usefulness of machine learning artificial intelligence (AI) in classifying the severity of ophthalmic emergency for timely hospital visits. STUDY
designThis retrospective study analysed the patients who first visited the Armed Forces Daegu Hospital between May and December 2019. General patient information, events and symptoms were input variables. Events, symptoms, diagnoses and treatments were output variables. The output variables were classified into four classes (red, orange, yellow and green, indicating immediate to no emergency cases). About 200 cases of the class-balanced validation data set were randomly selected before all training procedures. An ensemble AI model using combinations of fully connected neural networks with the synthetic minority oversampling technique algorithm was adopted.
participantsA total of 1681 patients were included. MAJOR OUTCOMES: Model performance was evaluated using accuracy, precision, recall and F1 scores.
resultsThe accuracy of the model was 99.05%. The precision of each class (red, orange, yellow and green) was 100%, 98.10%, 92.73% and 100%. The recalls of each class were 100%, 100%, 98.08% and 95.33%. The F1 scores of each class were 100%, 99.04%, 95.33% and 96.00%.
conclusionsWe provided support for an AI method to classify ophthalmic emergency severity based on symptoms.
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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.