ArticleJournal of clinical medicine2021
Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning.
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, 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Prediction of Maximum Keratometry Progression With Stratified Progression Criteria in Keratoconus Patients Using Machine Learning.Translational vision science & technology · 2026Article
- Automated Triage for New Keratoconus Referrals Using Multimodal Deep Learning.Ophthalmology science · 2026Article
- 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
- Treatment-aware artificial intelligence for keratoconus progression prediction.International ophthalmology · 2026Article
- A Novel Chaos-inspired Artificial Intelligence-based Model for Keratoconus Prediction.Journal of ophthalmic & vision research · 2026Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Cochrane corner: artificial intelligence for keratoconus.Eye (London, England) · 2024Article
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Keratoconus Progression Determined at the First Visit: A Deep Learning Approach With Fusion of Imaging and Numerical Clinical Data.Translational vision science & technology · 2024Article
- Article
- 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
- Advances in artificial intelligence models and algorithms in the field of optometry.Frontiers in cell and developmental biology · 2023Review
- Development and validation to predict visual acuity and keratometry two years after corneal crosslinking with progressive keratoconus by machine learning.Frontiers in medicine · 2023Article
- Prediction of posterior elevation stability in keratoconus.Frontiers in bioengineering and biotechnology · 2023Article
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
- Advances in artificial intelligence applications for ocular surface diseases diagnosis.Frontiers in cell and developmental biology · 2022Review
- Keratoconus Diagnostic and Treatment Algorithms Based on Machine-Learning Methods.Diagnostics (Basel, Switzerland) · 2021Article
- Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps.Annals of translational medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR
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.