ReviewDiagnostics (Basel, Switzerland)2023
Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Early Detection of Keratoconus: Diagnostic Advances and Their Impact on Visual Outcomes: A Systematic Review.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Biomechanical changes in keratoconus after customized stromal augmentation.Taiwan journal of ophthalmologyTrial
- 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
- Trends in the Management of Keratoconus in Australia.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- Corneal tomographic and densitometric characteristics in children with allergic conjunctivitis: a comparative pentacam AXL study.BMC ophthalmology · 2026Article
- A Novel Chaos-inspired Artificial Intelligence-based Model for Keratoconus Prediction.Journal of ophthalmic & vision research · 2026Article
- Evaluating corneal topographic patterns in keratoconic eyes and their association with keratoconus changes over time.International journal of ophthalmology · 2026Article
- The microenvironment of ocular surface in keratoconus: a systematic review.Eye and vision (London, England) · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Applications of machine learning in glaucoma diagnosis based on tabular data: a systematic review.BMC biomedical engineering · 2025Article
- Highly efficient stacking ensemble learning model for automated keratoconus screening.Eye and vision (London, England) · 2025Article
- Corneal Topography - a Review of Available Investigation Methods and Impact in the Diagnosis and Follow-Up of Keratoconus.Maedica · 2025Article
- Early diagnosis of keratoconus using corneal biomechanics and OCT derived technologies.Eye and vision (London, England) · 2025Review
- Pilot Programme for Keratoconus Screening and Management in Adolescents with Down Syndrome.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2025Review
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 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
- The Combined Utilization of Epithelial Thickness and Tomographic Parameters in Keratoconus Detection.Journal of ophthalmology · 2025Article
- 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
7 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.
Indexed as
Identifiers
What OpenQuestion holds
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.