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Using AI for Detection, Prediction and Classification of Retinal Detachment
Hesham Zaky1, Ahmed Salem1, Mahmoud Alzubaidi1
1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Studies in Health Technology and Informatics
|June 30, 2023
Summary
Machine learning and deep learning algorithms show promise in detecting, classifying, and predicting retinal detachment (RD). AI analysis of medical imaging, like fundus photography, aids early detection of this vision-threatening condition.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Retinal detachment (RD) is a severe eye condition that can lead to vision loss if not treated promptly.
- Early detection of peripheral retinal detachment is crucial for effective treatment and vision preservation.
Purpose of the Study:
- To conduct a scoping review of machine learning (ML) and deep learning (DL) algorithms for detecting, classifying, and predicting retinal detachment (RD).
- To provide an overview of current trends and practices in applying AI to medical imaging for RD management.
Main Methods:
- A comprehensive literature search was performed across five major databases (PubMed, Google Scholar, ScienceDirect, Scopus, IEEE).
- Two independent reviewers conducted study selection and data extraction.
- 32 studies were included from an initial pool of 666 references based on eligibility criteria.
Main Results:
- The review analyzed the performance metrics of various ML and DL algorithms applied to RD detection, classification, and prediction.
- Emerging trends in AI-driven approaches for analyzing medical imaging, such as fundus photography, were identified.
- The study highlights the potential of AI in facilitating earlier diagnosis of peripheral retinal detachment.
Conclusions:
- ML and DL algorithms represent a rapidly advancing field with significant potential in the early detection and management of retinal detachment.
- AI-powered analysis of medical imaging holds promise for improving diagnostic accuracy and patient outcomes in ophthalmology.
- Further research and validation are warranted to fully integrate these AI tools into clinical practice for retinal detachment.
