ArticleScientific reports2023
Deep learning-based prediction of the retinal structural alterations after epiretinal membrane surgery.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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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.
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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
7 citing papers in PubMed.
- Artificial Intelligence in Vitreoretinal Surgery: A Systematic Review of Current Applications and Future Directions.Ophthalmology and therapy · 2026Review
- Bibliometric analysis of research trends on the epiretinal membrane since 2000.Acta biochimica Polonica · 2026Article
- A computer vision-based framework for objective evaluation of sunken upper eyelid.Scientific reports · 2025Article
- Label-free optical microscopy with artificial intelligence: a new paradigm in pathology.Biophotonics discovery · 2025Review
- Automated Imaging of Cataract Surgery Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- Based on TransRes-Pix2Pix network to generate the OBL image during SMILE surgery.Frontiers in cell and developmental biology · 2025Article
- Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery.Scientific reports · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To generate and evaluate synthesized postoperative OCT images of epiretinal membrane (ERM) based on preoperative OCT images using deep learning methodology. This study included a total 500 pairs of preoperative and postoperative optical coherence tomography (OCT) images for training a neural network. 60 preoperative OCT images were used to test the neural networks performance, and the corresponding postoperative OCT images were used to evaluate the synthesized images in terms of structural similarity index measure (SSIM). The SSIM was used to quantify how similar the synthesized postoperative OCT image was to the actual postoperative OCT image. The Pix2Pix GAN model was used to generate synthesized postoperative OCT images. Total 60 synthesized OCT images were generated with training values at 800 epochs. The mean SSIM of synthesized postoperative OCT to the actual postoperative OCT was 0.913. Pix2Pix GAN model has a possibility to generate predictive postoperative OCT images following ERM removal surgery.
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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.