ArticleTranslational vision science & technology2023
Deep Learning Using Preoperative AS-OCT Predicts Graft Detachment in DMEK.
Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.Translational vision science & technology · 2025Pooled it
- Artificial Intelligence for Predicting Postoperative Outcomes in Anterior Segment and Ocular Adnexa Surgery: A Systematic Review.Ophthalmology science · 2026Review
- Peripheral Retinal Vessel Density in Children and Adolescents with Myopia: A Deep Learning Assessment.Ophthalmology and therapy · 2026Article
- Development and Clinical Validation of the DMEK Risk and Outcome Prediction (DROP) Score: A Dynamic Temporal Machine Learning Framework.Journal of clinical medicine · 2026Article
- Anterior Chamber Inflammation and Descemet Membrane Endothelial Keratoplasty: An Anterior Segment-OCT-Based Analysis.Ophthalmology science · 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
- Emerging Innovations in the Treatment of Fuchs Endothelial Corneal Dystrophy: A Narrative Review.Medical sciences (Basel, Switzerland) · 2025Review
- Imaging biomarkers in ophthalmology: hype, hope or game-changer?BMJ open ophthalmology · 2025Article
- Current concepts and recent trends in endothelial keratoplasty.World journal of transplantation · 2025Review
- Artificial Intelligence Aided Analysis of Anterior Segment Optical Coherence Tomography Imaging to Monitor the Device-Cornea Joint After Synthetic Cornea Implantation.Translational vision science & technology · 2025Article
- The role of anterior segment optical coherence tomography in post-cataract surgery Descemet membrane detachment.International ophthalmology · 2025Review
- Artificial intelligence for posterior capsule opacification.Frontiers in medicine · 2025Review
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- Predicting Long-Term Endothelial Cell Loss after Preloaded Descemet Membrane Endothelial Keratoplasty in Fuchs' Endothelial Corneal Dystrophy: A Mathematical Model.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To evaluate a novel deep learning algorithm to distinguish between eyes that may or may not have a graft detachment based on pre-Descemet membrane endothelial keratoplasty (DMEK) anterior segment optical coherence tomography (AS-OCT) images. Methods: Retrospective cohort study. A multiple-instance learning artificial intelligence (MIL-AI) model using a ResNet-101 backbone was designed. AS-OCT images were split into training and testing sets. The MIL-AI model was trained and validated on the training set. Model performance and heatmaps were calculated from the testing set. Classification performance metrics included F1 score (harmonic mean of recall and precision), specificity, sensitivity, and area under curve (AUC). Finally, MIL-AI performance was compared to manual classification by an experienced ophthalmologist. Results: In total, 9466 images of 74 eyes (128 images per eye) were included in the study. Images from 50 eyes were used to train and validate the MIL-AI system, while the remaining 24 eyes were used as the test set to determine its performance and generate heatmaps for visualization. The performance metrics on the test set (95% confidence interval) were as follows: F1 score, 0.77 (0.57-0.91); precision, 0.67 (0.44-0.88); specificity, 0.45 (0.15-0.75); sensitivity, 0.92 (0.73-1.00); and AUC, 0.63 (0.52-0.86). MIL-AI performance was more sensitive (92% vs. 31%) but less specific (45% vs. 64%) than the ophthalmologist's performance. Conclusions: The MIL-AI predicts with high sensitivity the eyes that may have post-DMEK graft detachment requiring rebubbling. Larger-scale clinical trials are warranted to validate the model. Translational Relevance: MIL-AI models represent an opportunity for implementation in routine DMEK suitability screening.
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