ArticleScientific reports2022
A machine learning approach to explore predictors of graft detachment following posterior lamellar keratoplasty: a nationwide registry study.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Eye bank pre-stripped versus surgeon-stripped graft for Descemet membrane endothelial keratoplasty: a systematic review and meta-analysis.Cell and tissue banking · 2025Pooled it
- Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.Translational vision science & technology · 2025Pooled it
- Descemet membrane endothelial keratoplasty compared with ultrathin Descemet stripping automated endothelial keratoplasty: a meta-analysis.BMJ open ophthalmology · 2023Pooled it
- Artificial Intelligence for Predicting Postoperative Outcomes in Anterior Segment and Ocular Adnexa Surgery: A Systematic Review.Ophthalmology science · 2026Review
- Development and Clinical Validation of the DMEK Risk and Outcome Prediction (DROP) Score: A Dynamic Temporal Machine Learning Framework.Journal of clinical medicine · 2026Article
- Artificial intelligence in corneal endothelial disease and endothelial keratoplasty: current evidence, clinical readiness, and future directions.Frontiers in ophthalmology · 2026Review
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Current concepts and recent trends in endothelial keratoplasty.World journal of transplantation · 2025Review
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Machine learning methods to identify risk factors for corneal graft rejection in keratoconus.Scientific reports · 2024Article
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- Article
- Deep Learning Using Preoperative AS-OCT Predicts Graft Detachment in DMEK.Translational vision science & technology · 2023Article
- Risk Factors for Descemet Membrane Endothelial Keratoplasty Rejection: Current Perspectives- Systematic Review.Clinical ophthalmology (Auckland, N.Z.) · 2023Review
- Impact of Donor, Recipient, and Graft Characteristics on Corneal Transplantation Outcomes.Clinical ophthalmology (Auckland, N.Z.) · 2023Article
- Detection of graft failure in post-keratoplasty patients by automated deep learning.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
Corrections and comments
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Authors and funding
6 authors at 12 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning can be used to explore the complex multifactorial patterns underlying postsurgical graft detachment after endothelial corneal transplantation surgery and to evaluate the marginal effect of various practice pattern modulations. We included all posterior lamellar keratoplasty procedures recorded in the Dutch Cornea Transplant Registry from 2015 through 2018 and collected the center-specific practice patterns using a questionnaire. All available data regarding the donor, recipient, surgery, and practice pattern, were coded into 91 factors that might be associated with the occurrence of a graft detachment. In this research, we used three machine learning methods; a regularized logistic regression (lasso), classification tree analysis (CTA), and random forest classification (RFC), to select the most predictive subset of variables for graft detachment. A total of 3647 transplants were included in our analysis and the overall prevalence of graft detachment was 9.9%. In an independent test set the area under the curve for the lasso, CTA, and RFC was 0.70, 0.65, and 0.72, respectively. Identified risk factors included: a Descemet membrane endothelial keratoplasty procedure, prior graft failure, and the use of sulfur hexafluoride gas. Factors with a reduced risk included: performing combined procedures, using pre-cut donor tissue, and a pre-operative laser iridotomy. These results can help surgeons to review their practice patterns and generate hypotheses for empirical research regarding the origins of graft detachments.
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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.