⚡ Quick Summary
This scoping review highlights the role of artificial intelligence (AI) in the management of epiretinal membranes (ERM), showcasing its potential to enhance detection, characterization, and prognostication in ophthalmology. The findings indicate that AI models can achieve performance levels comparable to human specialists, paving the way for improved clinical decision-making. 👁️
🔍 Key Details
- 📊 Dataset: 390 articles reviewed, 33 studies included
- 🧩 Features used: OCT scans and fundus photographs
- ⚙️ Technology: 61 distinct AI models analyzed
- 🏆 Performance: 63% of studies reported AI performance equal to or better than human graders
🔑 Key Takeaways
- 🤖 AI shows promise in enhancing ERM detection and management.
- 📈 70% of studies focused on AI for diagnosis of ERM.
- 🖼️ Imaging techniques such as OCT scans were predominant in the studies.
- 🔍 AI models can identify morphological properties of ERM effectively.
- 🔮 Future research should focus on validating AI algorithms for personalized treatment plans.
- 💡 82% of studies developed AI models using imaging data.
- 🧑⚕️ AI performance was comparable to that of retinal specialists in many cases.
- 📅 Study timeframe: Literature search conducted until November 2024.
📚 Background
The management of epiretinal membranes (ERM) is a significant aspect of ophthalmology, often requiring clinical evaluation and imaging for effective treatment. Traditional methods can be subjective, leading to variability in diagnosis and treatment planning. The integration of artificial intelligence into this field holds the potential to standardize and enhance the accuracy of ERM assessments, ultimately improving patient outcomes.
🗒️ Study
This scoping review aimed to synthesize the existing literature on AI applications in ERM management. A comprehensive search across five electronic databases was conducted, focusing on studies that utilized AI algorithms for ERM diagnosis, characterization, and prognostication. The review included 33 studies that met the inclusion criteria, providing a detailed overview of methodologies and performance metrics.
📈 Results
The review identified a total of 61 distinct AI models across the included studies. Notably, 30 studies (91%) reported their training and validation methods, with the majority employing supervised learning. The results indicated that AI-driven assessments could effectively detect ERM and predict visual outcomes, with 63% of studies demonstrating performance on par with human graders.
🌍 Impact and Implications
The findings from this review suggest that AI has the potential to revolutionize the management of ERM in clinical settings. By enhancing the accuracy of diagnosis and prognostication, AI can assist ophthalmologists in making more informed decisions regarding surgical interventions. This could lead to improved patient outcomes and a more personalized approach to treatment, ultimately benefiting those affected by ERM.
🔮 Conclusion
This scoping review underscores the significant role of artificial intelligence in the care of epiretinal membranes. With its ability to match or exceed human performance in certain aspects, AI presents a promising avenue for enhancing clinical decision-making in ophthalmology. Continued research and validation of these algorithms will be crucial in realizing their full potential in personalized patient care.
💬 Your comments
What are your thoughts on the integration of AI in ophthalmology? Do you believe it can truly enhance patient care? Let’s discuss! 💬 Leave your thoughts in the comments below or connect with us on social media:
The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review.
Abstract
TOPIC: In ophthalmology, artificial intelligence (AI) demonstrates potential in using ophthalmic imaging across diverse diseases, often matching ophthalmologists’ performance. However, the range of machine learning models for epiretinal membrane (ERM) management, which differ in methodology, application, and performance, remains largely unsynthesized.
CLINICAL RELEVANCE: Epiretinal membrane management relies on clinical evaluation and imaging, with surgical intervention considered in cases of significant impairment. AI analysis of ophthalmic images and clinical features could enhance ERM detection, characterization, and prognostication, potentially improving clinical decision-making. This scoping review aims to evaluate the methodologies, applications, and reported performance of AI models in ERM diagnosis, characterization, and prognostication.
METHODS: A comprehensive literature search was conducted across 5 electronic databases including Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science Core Collection from inception to November 14, 2024. Studies pertaining to AI algorithms in the context of ERM were included. The primary outcomes measured will be the reported design, application in ERM management, and performance of each AI model.
RESULTS: Three hundred ninety articles were retrieved, with 33 studies meeting inclusion criteria. There were 30 studies (91%) reporting their training and validation methods. Altogether, 61 distinct AI models were included. OCT scans and fundus photographs were used in 26 (79%) and 7 (21%) papers, respectively. Supervised learning and both supervised and unsupervised learning were used in 32 (97%) and 1 (3%) studies, respectively. Twenty-seven studies (82%) developed or adapted AI models using images, whereas 5 (15%) had models using both images and clinical features, and 1 (3%) used preoperative and postoperative clinical features without ophthalmic images. Study objectives were categorized into 3 stages of ERM care. Twenty-three studies (70%) implemented AI for diagnosis (stage 1), 1 (3%) identified ERM characteristics (stage 2), and 6 (18%) predicted vision impairment after diagnosis or postoperative vision outcomes (stage 3). No articles studied treatment planning. Three studies (9%) used AI in stages 1 and 2. Of the 16 studies comparing AI performance to human graders (i.e., retinal specialists, general ophthalmologists, and trainees), 10 (63%) reported equivalent or higher performance.
CONCLUSION: Artificial intelligence-driven assessments of ophthalmic images and clinical features demonstrated high performance in detecting ERM, identifying its morphological properties, and predicting visual outcomes following ERM surgery. Future research might consider the validation of algorithms for clinical applications in personal treatment plan development, ideally to identify patients who might benefit most from surgery.
FINANCIAL DISCLOSURES: The author(s) have no proprietary or commercial interest in any materials discussed in this article.
Author: [‘Mikhail D’, ‘Milad D’, ‘Antaki F’, ‘Hammamji K’, ‘Qian CX’, ‘Rezende FA’, ‘Duval R’]
Journal: Ophthalmol Sci
Citation: Mikhail D, et al. The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review. The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review. 2025; 5:100689. doi: 10.1016/j.xops.2024.100689