ArticleScientific reports2025
Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 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
- Unsupervised phenotyping of suspect keratoconus based on posterior tomography and epithelial remodeling.BMC ophthalmology · 2026Article
- A deep learning approach for keratoconus detection using spatio-temporal features from corneal imaging.Scientific reports · 2026Article
- Clinical Outcomes and Complications After INTACS Implantation in Keratoconus: A Systematic Review.Journal of clinical medicine · 2026Review
- Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography.International journal of ophthalmology · 2026Article
- Explainable neuro-symbolic artificial intelligence for automated interpretation of corneal topography and early keratoconus detection.Frontiers in artificial intelligence · 2026Article
- The "Three-Tier Regulatory Network" of Orthokeratology in Myopia Control: Evidence Weight of Underlying Mechanisms, Controversies, and New Perspectives for Clinical Translation--A Review.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- An Explainable AI Framework for Corneal Imaging Interpretation and Refractive Surgery Decision Support.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of machine learning models to identify keratoconus based on corneal topography and biomechanical data. We collected a dataset comprising 144 corneal scans from adults aged 18-35, including an equal proportion of keratoconus and normal cases. Various machine learning algorithms were trained and evaluated on datasets containing different parameters obtained using the Pentacam device. The Random Forest algorithm demonstrated the highest reliability, achieving an accuracy of 98% during training and 96% on the test set, while also identifying the most diagnostically relevant measurements. Unlike prior studies, our approach enables detailed comparison between model-selected features and clinically recognized diagnostic parameters. This interpretability provides a clinically meaningful bridge between AI-driven predictions and expert-based decision-making. The results suggest that machine learning models, particularly Random Forest, can effectively aid in the early detection of keratoconus in young individuals, potentially improving patient outcomes through timely intervention.
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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.