ArticleThe British journal of ophthalmology2023
Development and validation of a deep learning system to classify aetiology and predict anatomical outcomes of macular hole.
Article in The British journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 30 citations in OpenAlex.
- An Agentic AI Workflow for Automated Macular Hole Diagnosis and Structured Report Generation.Ophthalmology and therapy · 2026Article
- Automated measurement of the minimum macular hole diameter based on optical coherence tomography images.BMC ophthalmology · 2026Article
- Artificial Intelligence in Vitreoretinal Surgery: A Systematic Review of Current Applications and Future Directions.Ophthalmology and therapy · 2026Review
- Macular hole detection and segmentation on fundus photography using large multimodal generative models for synthetic augmentation.BMC ophthalmology · 2026Article
- The Impact of Vitreoretinal Surgery in Patients with Uveitis: Current Strategies and Emerging Perspectives.Diagnostics (Basel, Switzerland) · 2026Review
- Novel TA-IVIZ Patch and BSS Tamponade for Macular Hole Repair: A Pilot Study.Translational vision science & technology · 2025Article
- Comparing Auto-Machine Learning and Expert-Designed Models in Diagnosing Vitreomacular Interface Disorders.Journal of clinical medicine · 2025Article
- Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study.International journal of retina and vitreous · 2025Article
- Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole.Bioengineering (Basel, Switzerland) · 2024Article
- Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.Ophthalmology and therapy · 2024Review
- Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans.PLOS digital health · 2024Article
- Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery.Scientific reports · 2024Article
- Novel Artificial Intelligence-Based Assessment of Imaging Biomarkers in Full-Thickness Macular Holes: Preliminary Data from a Pivotal Trial.Journal of clinical medicine · 2024Article
- Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images.International journal of ophthalmology · 2024Review
- Artificial intelligence in individualized retinal disease management.International journal of ophthalmology · 2024Review
- Artificial Intelligence in Ophthalmic Surgery: Current Applications and Expectations.Clinical ophthalmology (Auckland, N.Z.) · 2023Review
- Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features.Translational vision science & technology · 2022Article
Corrections and comments
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Authors and funding
21 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo develop a deep learning (DL) model for automatic classification of macular hole (MH) aetiology (idiopathic or secondary), and a multimodal deep fusion network (MDFN) model for reliable prediction of MH status (closed or open) at 1 month after vitrectomy and internal limiting membrane peeling (VILMP).
methodsIn this multicentre retrospective cohort study, a total of 330 MH eyes with 1082 optical coherence tomography (OCT) images and 3300 clinical data enrolled from four ophthalmic centres were used to train, validate and externally test the DL and MDFN models. 266 eyes from three centres were randomly split by eye-level into a training set (80%) and a validation set (20%). In the external testing dataset, 64 eyes were included from the remaining centre. All eyes underwent macular OCT scanning at baseline and 1 month after VILMP. The area under the receiver operated characteristic curve (AUC), accuracy, specificity and sensitivity were used to evaluate the performance of the models.
resultsIn the external testing set, the AUC, accuracy, specificity and sensitivity of the MH aetiology classification model were 0.965, 0.950, 0.870 and 0.938, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative MH status prediction model were 0.904, 0.825, 0.977 and 0.766, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative idiopathic MH status prediction model were 0.947, 0.875, 0.815 and 0.979, respectively.
conclusionOur DL-based models can accurately classify the MH aetiology and predict the MH status after VILMP. These models would help ophthalmologists in diagnosis and surgical planning of MH.
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