ArticleDiagnostics (Basel, Switzerland)2021
Keratoconus Diagnostic and Treatment Algorithms Based on Machine-Learning Methods.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Artificial intelligence for detecting keratoconus.The Cochrane database of systematic reviews · 2023Pooled it
- Paediatric Keratoconus: An ERN-EYE Clinical Consensus Statement on Diagnosis, Treatment and Follow-Up Care.Ophthalmology and therapy · 2026Review
- Combination of tissue-derived and shape-based parameters for subclinical keratoconus detection.Biomedical optics express · 2026Article
- Multicentre Pixel-Level Tear Meniscus Segmentation Dataset with Multimodal Imaging for Dry Eye Diagnosis.Scientific data · 2025Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Highly efficient stacking ensemble learning model for automated keratoconus screening.Eye and vision (London, England) · 2025Article
- Advances in machine learning for keratoconus diagnosis.International ophthalmology · 2025Review
- Keratoconus.Nature reviews. Disease primers · 2024Article
- Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices.Eye and vision (London, England) · 2024Article
- Keratoconus: imaging modalities and management.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Review
- Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Machine learning analysis with the comprehensive index of corneal tomographic and biomechanical parameters in detecting pediatric subclinical keratoconus.Frontiers in bioengineering and biotechnology · 2023Article
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
- Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate diagnosis of keratoconus, especially in its early stages of development, allows one to utilise timely and proper treatment strategies for slowing the progression of the disease and provide visual rehabilitation. Various keratometry indices and classifications for quantifying the severity of keratoconus have been developed. Today, many of them involve the use of the latest methods of computer processing and data analysis. The main purpose of this work was to develop a machine-learning-based algorithm to precisely determine the stage of keratoconus, allowing optimal management of patients with this disease. A multicentre retrospective study was carried out to obtain a database of patients with keratoconus and to use machine-learning techniques such as principal component analysis and clustering. The created program allows for us to distinguish between a normal state; preclinical keratoconus; and stages 1, 2, 3 and 4 of the disease, with an accuracy in terms of the AUC of 0.95 to 1.00 based on keratotopographer readings, relative to the adapted Amsler-Krumeich algorithm. The predicted stage and additional diagnostic criteria were then used to create a standardised keratoconus management algorithm. We also developed a web-based interface for the algorithm, providing us the opportunity to use the software in a clinical environment.
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