ArticlePloS one2018
Keratoconus severity identification using unsupervised machine learning.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 4 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The public health impact of making chloramphenicol an over-the-counter antibiotic: a systematic literature review.International ophthalmology · 2025Pooled 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
- Artificial Intelligence-Derived Biomechanical Index for Early Corneal Ectasia Detection: Advancing Beyond Tomography.Ophthalmology science · 2026Article
- Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography.International journal of ophthalmology · 2026Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Novel Subclassification of Presbyopia Based on Lens Functional and Structural Features.Translational vision science & technology · 2025Observational
- Screening with the Bilateral Corneal Symmetry 3-D Analyzer.International journal of environmental research and public health · 2025Article
- Advances in machine learning for keratoconus diagnosis.International ophthalmology · 2025Review
- An explainable unsupervised learning approach for anomaly detection on cornealFrontiers in bioengineering and biotechnology · 2025Article
- Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices.Eye and vision (London, England) · 2024Article
- KeratoEL: Detection of keratoconus using corneal parameters with ensemble learning.Health science reports · 2024Article
- Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis.Journal of Taibah University Medical Sciences · 2024Review
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- Repeatability and reliability of semi-automated anterior segment-optical coherence tomography imaging compared to manual analysis in normal and keratoconus eyes.International ophthalmology · 2023Observational
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- Performance of ChatGPT in Diagnosis of Corneal Eye Diseases.medRxiv : the preprint server for health sciences · 2023Article
- Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review.Diagnostics (Basel, Switzerland) · 2023Review
- A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed an unsupervised machine learning algorithm and applied it to big corneal parameters to identify and monitor keratoconus stages. A big dataset of corneal swept source optical coherence tomography (OCT) images of 12,242 eyes acquired from SS-1000 CASIA OCT Imaging Systems in multiple centers across Japan was assembled. A total of 3,156 eyes with valid Ectasia Status Index (ESI) between zero and 100% were selected for the downstream analysis. Four hundred and twenty corneal topography, elevation, and pachymetry parameters (excluding ESI Keratoconus indices) were selected. The algorithm included three major steps. 1) Principal component analysis (PCA) was used to linearly reduce the dimensionality of the input data from 420 to eight significant principal components. 2) Manifold learning was used to further reducing the selected principal components nonlinearly to two eigen-parameters. 3) Finally, a density-based clustering was applied to the eigen-parameters to identify eyes with keratoconus. Visualization of clusters in 2-D space was used to validate the quality of learning subjectively and ESI was used to assess the accuracy of the identified clusters objectively. The proposed method identified four clusters; I: a cluster composed of mostly normal eyes (224 eyes with ESI equal to zero, 23 eyes with ESI between five and 29, and nine eyes with ESI greater than 29), II: a cluster composed of mostly healthy eyes and eyes with forme fruste keratoconus (1772 eyes with ESI equal to zero, 698 eyes with ESI between five and 29, and 117 eyes with ESI greater than 29), III: a cluster composed of mostly eyes with mild keratoconus stage (184 eyes with ESI greater than 29, 74 eyes with ESI between five and 29, and 6 eyes with ESI equal to zero), and IV: a cluster composed of eyes with mostly advanced keratoconus stage (80 eyes had ESI greater than 29 and 1 eye had ESI between five and 29). We found that keratoconus status and severity can be well identified using unsupervised machine learning algorithms along with linear and non-linear corneal data transformation. The proposed method can better identify and visualize the keratoconus stages.
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