ArticleFrontiers in medicine2023
Development and validation to predict visual acuity and keratometry two years after corneal crosslinking with progressive keratoconus by machine learning.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Mitochondrial Quality Control Systems in Septic AKI: Molecular Mechanisms and Therapeutic Implications.International journal of medical sciences · 2025Article
- Digital transformation of care for keratoconus patients: ML modeling structural outcomes of corneal collagen cross-linking.Frontiers in medicine · 2025Article
- Individual Risk Assessment and Prognostication of Outcomes After Corneal Cross-Linking.Journal of ophthalmology · 2025Article
- Spinal Metastasis Pain Surveillance: A Comprehensive Imaging-Based Tool Design for Evaluating Metastatic Burden and Guiding Therapeutic Strategies.International journal of medical sciences · 2025Article
- Glycosphingolipids-Dependent Phospholipid Metabolism Enhances Cancer Initiation and Progression through SMPD1/GLTP/B3GALT4/ST8SIA6 Signaling Axis: A Novel Therapeutic Target.International journal of medical sciences · 2025Article
- Unveiling the role of risk factors and predictive models in acute type-a aortic dissection surgery: OI downregulation and its association with immune disorders.International journal of medical sciences · 2025Article
- Identifying Water-Salt Homeostasis and Inflammatory Response in Pathological Cardiac Surgery-Associated Acute Kidney Injury: NT-proBNP-related lncRNAs and miRNAs as Novel Diagnostic Biomarkers and Therapeutic Targets.International journal of medical sciences · 2025Article
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.Current oncology (Toronto, Ont.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To explore and validate the utility of machine learning (ML) methods using a limited sample size to predict changes in visual acuity and keratometry 2 years following corneal crosslinking (CXL) for progressive keratoconus. Methods: The study included all consecutive patients with progressive keratoconus who underwent CXL from July 2014 to December 2020, with a 2 year follow-up period before July 2022 to develop the model. Variables collected included patient demographics, visual acuity, spherical equivalence, and Pentacam parameters. Available case data were divided into training and testing data sets. Three ML models were evaluated based on their performance in predicting case corrected distance visual acuity (CDVA) and maximum keratometry (K Results: A total of 277 eyes from 195 patients were included in training and testing sets and 43 eyes from 35 patients were included in the validation set. The baseline CDVA (26.7%) and the ratio of steep keratometry to flat keratometry (K Conclusion: Application of a ML approach using XGBoost, and incorporation of identifiable parameters, considerably improved variation prediction accuracy of both CDVA and K
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.