ArticleEye and vision (London, England)2021
Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity.
Article in Eye and vision (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04251143 (Dresden Corneal Disease and Treatment Study - Analysis for Quality Inspection of Follow-up and Treatment Procedures of Corneal Diseases of the Department of Ophthalmology), which is not on this map. Cited by 30 papers, 3 of them syntheses 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.
Dresden Corneal Disease and Treatment Study - Analysis for Quality Inspection of Follow-up and Treatment Procedures of Corneal Diseases of the Department of Ophthalmology (University Hospital Carl Gustav Carus Dresden)
Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Early Detection of Keratoconus: Diagnostic Advances and Their Impact on Visual Outcomes: A Systematic Review.Medicina (Kaunas, Lithuania) · 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
- Global spatiotemporal biomechanics using video swin transformer: multiscale validation and clinical impact for keratoconus suspects.NPJ digital medicine · 2026Article
- Staging of Keratoconus Indices Based on Localized Corneal Biomechanics Stress-Strain Index Map.Ophthalmology science · 2026Article
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- A Novel Chaos-inspired Artificial Intelligence-based Model for Keratoconus Prediction.Journal of ophthalmic & vision research · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Artificial intelligence derived grading of mustard gas induced corneal injury and opacity.Scientific reports · 2025Article
- 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
- Development and validation of the VAE-NT index: a novel biomechanical parameter for distinguishing subclinical corneal abnormalities.Frontiers in bioengineering and biotechnology · 2025Article
- Non-orthogonal spectacle correction for irregular astigmatism.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Understanding Keratoconus in Down Syndrome: From Etiology to Management - A Narrative Review.Clinical ophthalmology (Auckland, N.Z.) · 2025Review
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Keratectasia severity staging and progression assessment based on the biomechanical E-staging.Eye and vision (London, England) · 2024Review
- Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis.Journal of Taibah University Medical Sciences · 2024Review
- Assessing progression limits in different grades of keratoconus from a novel perspective: precision of measurements of the corneal epithelium.Eye and vision (London, England) · 2024Article
- Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundTo investigate machine-learning (ML) algorithms to differentiate corneal biomechanical properties between different topographical stages of keratoconus (KC) by dynamic Scheimpflug tonometry (CST, Corvis ST, Oculus, Wetzlar, Germany). In the following, ML models were used to predict the severity in a training and validation dataset.
methodsThree hundred and eighteen keratoconic and one hundred sixteen healthy eyes were included in this monocentric and cross-sectional pilot study. Dynamic corneal response (DCR) and corneal thickness related (pachymetric) parameters from CST were chosen by appropriated selection techniques to develop a ML algorithm. The stage of KC was determined by the topographical keratoconus classification system (TKC, Pentacam, Oculus). Patients who were classified as TKC 1, TKC 2 and TKC 3 were assigned to subgroup mild, moderate, and advanced KC. If patients were classified as TKC 1-2, TKC 2-3 or TKC 3-4, they were assigned to subgroups according to the normative range of further corneal indices (index of surface variance, keratoconus index and minimum radius). Patients classified as TKC 4 were not included in this study due to the limited amount of cases. Linear discriminant analysis (LDA) and random forest (RF) algorithms were used to develop the classification models. Data were divided into training (70% of cases) and validation (30% of cases) datasets.
resultsLDA model predicted healthy, mild, moderate, and advanced KC eyes with a sensitivity (S
conclusionThe RF model showed good accuracy in predicting healthy eyes and various stages of KC. The accuracy was superior with respect to the LDA model. The clinical importance of the models is that the standalone dynamic Scheimpflug tonometry is able to predict the severity of KC without having the keratometric data.
trial registrationNCT04251143 at Clinicaltrials.gov, registered at 12 March 2018 (Retrospectively registered).
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.