ReviewJournal of Taibah University Medical Sciences2024
Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis.
Review in Journal of Taibah University Medical Sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Early Detection of Keratoconus: Diagnostic Advances and Their Impact on Visual Outcomes: A Systematic Review.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Article
- A Novel Chaos-inspired Artificial Intelligence-based Model for Keratoconus Prediction.Journal of ophthalmic & vision research · 2026Article
- Utilization of an automated machine learning approach for the detection of granular corneal dystrophy via slit lamp photographs.BMC ophthalmology · 2025Article
- Development and Validation of the Relational Tissue Altered (RTA) Index: Applied Artificial Intelligence for the Assessment of Structural Impact from Laser Vision Correction.Ophthalmology and therapy · 2025Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Strategies for Early Keratoconus Diagnosis: A Narrative Review of Evaluating Affordable and Effective Detection Techniques.Journal of clinical medicine · 2025Review
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Prevention and early intervention screening for inherited ocular diseases in Saudi Arabia: a national perspective.Frontiers in ophthalmology · 2025Review
- Multi-modal imaging for the detection of early keratoconus: a narrative review.Eye and vision (London, England) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The challenges in diagnosing keratoconus (KC) have led researchers to explore the use of artificial intelligence (AI) as a diagnostic tool. AI has emerged as a new way to improve the efficiency of KC diagnosis. This study analyzed the use of AI as a diagnostic modality for KC. Methods: This study used a systematic review and meta-analysis following the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched selected databases using a combination of search terms: "((Artificial Intelligence) OR (Diagnostic Modality)) AND (Keratoconus)" from PubMed, Medline, and ScienceDirect within the last 5 years (2018-2023). Following a systematic review protocol, we selected 11 articles and 6 articles were eligible for final analysis. The relevant data were analyzed with Review Manager 5.4 software and the final output was presented in a forest plot. Results: This research found neural networks as the most used AI model in diagnosing KC. Neural networks and naïve bayes showed the highest accuracy of AI in diagnosing KC with a sensitivity of 1.00, while random forests were >0.90. All studies in each group have proven high sensitivity and specificity over 0.90. Conclusions: AI potentially makes a better diagnosis of the KC with its high performance, particularly on sensitivity and specificity, which can help clinicians make medical decisions about an individual patient.
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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.