ArticleJournal of clinical medicine2023
iERM: An Interpretable Deep Learning System to Classify Epiretinal Membrane for Different Optical Coherence Tomography Devices: A Multi-Center Analysis.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- DenseViT-OCT: A Hybrid CNN-Transformer Architecture with Multi-Scale Dense Feature Aggregation for Automated Epiretinal Membrane Severity Classification.Tomography (Ann Arbor, Mich.) · 2026Article
- A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis.Journal of healthcare informatics research · 2025Article
- Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants.Journal of clinical medicine · 2024Article
- Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.BMC medical informatics and decision making · 2024Article
- Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model.Diagnostics (Basel, Switzerland) · 2023Article
- Targeted bile acids metabolomics in cholesterol gallbladder polyps and gallstones: From analytical method development towards application to clinical samples.Journal of pharmaceutical analysis · 2023Article
- Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images.Biomedical signal processing and control · 2023Article
- RGSB-UNet: Hybrid Deep Learning Framework for Tumour Segmentation in Digital Pathology Images.Bioengineering (Basel, Switzerland) · 2023Article
- Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System.Healthcare (Basel, Switzerland) · 2023Article
- Diagnostic ability of deep learning in detection of pancreatic tumour.Scientific reports · 2023Article
- Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction.Diagnostics (Basel, Switzerland) · 2023Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Two-in-One Sensor Based on PV4D4-Coated TiOBiosensors · 2023Article
- Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Reduction of rupture risk in ICA aneurysms by endovascular techniques of coiling and stent: numerical study.Scientific reports · 2023Article
- An interpretable transformer network for the retinal disease classification using optical coherence tomography.Scientific reports · 2023Article
- Pulmonary Function Test and Obstructive Sleep Apnea Hypopnea Syndrome in Obese Adults: A Retrospective Study.International journal of chronic obstructive pulmonary disease · 2023Article
- The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review.Ophthalmology scienceReview
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Background: Epiretinal membranes (ERM) have been found to be common among individuals >50 years old. However, the severity grading assessment for ERM based on optical coherence tomography (OCT) images has remained a challenge due to lacking reliable and interpretable analysis methods. Thus, this study aimed to develop a two-stage deep learning (DL) system named iERM to provide accurate automatic grading of ERM for clinical practice. Methods: The iERM was trained based on human segmentation of key features to improve classification performance and simultaneously provide interpretability to the classification results. We developed and tested iERM using a total of 4547 OCT B-Scans of four different commercial OCT devices that were collected from nine international medical centers. Results: As per the results, the integrated network effectively improved the grading performance by 1−5.9% compared with the traditional classification DL model and achieved high accuracy scores of 82.9%, 87.0%, and 79.4% in the internal test dataset and two external test datasets, respectively. This is comparable to retinal specialists whose average accuracy scores are 87.8% and 79.4% in two external test datasets. Conclusion: This study proved to be a benchmark method to improve the performance and enhance the interpretability of the traditional DL model with the implementation of segmentation based on prior human knowledge. It may have the potential to provide precise guidance for ERM diagnosis and treatment.
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