ArticleJournal of clinical medicine2021
Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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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
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Pooled 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
- Deep learning approach for analyzing regional scheimpflug corneal images to assess keratoconus progression.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- A deep learning approach for keratoconus detection using spatio-temporal features from corneal imaging.Scientific reports · 2026Article
- Enhancing the diagnostic precision of subclinical keratoconus by combining indices from Scheimpflug tomography, corneal biomechanics, and anterior segment optical coherence tomography.International journal of ophthalmology · 2026Article
- Artificial Intelligence-Derived Biomechanical Index for Early Corneal Ectasia Detection: Advancing Beyond Tomography.Ophthalmology science · 2026Article
- 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
- Early diagnosis of keratoconus using corneal biomechanics and OCT derived technologies.Eye and vision (London, England) · 2025Review
- Advances in machine learning for keratoconus diagnosis.International ophthalmology · 2025Review
- Strategies for Early Keratoconus Diagnosis: A Narrative Review of Evaluating Affordable and Effective Detection Techniques.Journal of clinical medicine · 2025Review
- Diagnosis of Forme Fruste Keratoconus Using Corvis ST Sequences with Digital Image Correlation and Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Systemic and Ocular Associations of Keratoconus.Expert review of ophthalmology · 2024Article
- The Level of Awareness of Keratoconus Among the General Population in Hail Region, Saudi Arabia.Cureus · 2023Article
- Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- Efficacy of custom-made soft keratoconus lenses on corneal aberrations and photic phenomena in patients with keratoconus: a corneal topography imaging based study.International journal of ophthalmology · 2023Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) < 46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0°, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.
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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.