The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey

Sensors

Ahmed Sharafeldeen¹  Ayman El-Baz¹  Mostafa Elrazzaz¹  Ahmed Elnakib¹  Mohamed Elsharkawy¹  Ali Mahmoud¹  Fahmi Khalifa¹  Ahmed Soliman¹  Harpal Singh Sandhu¹  Marah Alhalabi²  Mohammed Ghazal²  Ahmed Atwan³  Eman El-Daydamony³ 

[1] Bioengineering Department, University of Louisville, Louisville, KY 40292, USA;Electrical, Computer and Biomedical Engineering Department, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt;

关键词: diabetic retinopathy (DR);    computer-aided diagnostic system (CAD);    machine learning (ML);    deep learning (DL);    optical coherence tomography (OCT);    OCT angiography (OCTA);   

DOI  :  10.3390/s22093490

来源: DOAJ

【 摘 要 】

Diabetic retinopathy (DR) is a devastating condition caused by progressive changes in the retinal microvasculature. It is a leading cause of retinal blindness in people with diabetes. Long periods of uncontrolled blood sugar levels result in endothelial damage, leading to macular edema, altered retinal permeability, retinal ischemia, and neovascularization. In order to facilitate rapid screening and diagnosing, as well as grading of DR, different retinal modalities are utilized. Typically, a computer-aided diagnostic system (CAD) uses retinal images to aid the ophthalmologists in the diagnosis process. These CAD systems use a combination of machine learning (ML) models (e.g., deep learning (DL) approaches) to speed up the diagnosis and grading of DR. In this way, this survey provides a comprehensive overview of different imaging modalities used with ML/DL approaches in the DR diagnosis process. The four imaging modalities that we focused on are fluorescein angiography, fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). In addition, we discuss limitations of the literature that utilizes such modalities for DR diagnosis. In addition, we introduce research gaps and provide suggested solutions for the researchers to resolve. Lastly, we provide a thorough discussion about the challenges and future directions of the current state-of-the-art DL/ML approaches. We also elaborate on how integrating different imaging modalities with the clinical information and demographic data will lead to promising results for the scientists when diagnosing and grading DR. As a result of this article’s comparative analysis and discussion, it remains necessary to use DL methods over existing ML models to detect DR in multiple modalities.

【 授权许可】

Unknown