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Diabetic Retinopathy Detection Through Smartphonebased Retinal Imaging Approach: A Brief Review
Artificial intelligence (AI) algorithms provide powerful tools for analyzing massive amounts of medical data and information. Such capabilities, particularly, help infer useful patterns and insights that may not be obvious to humans, thereby triggering strong interest in developing medical applications, especially those related to the diagnosis of diseases. Several studies have found that AI can be applied to enhance the automated early and accurate detection of vision-threatening diseases of the retina using fundus images, the most prominent of which is diabetic retinopathy (DR). This paper briefly reviews various retinal imaging techniques and proposes a standalone application for detecting DR by integrating a simplified retinal imaging smartphone-based technique with a machine learning algorithm. While ophthalmologists traditionally use clinical ophthalmoscopy for retinal exams to screen for this disease, recent technological advances now allow smartphones to serve as cost-effective tools for retinal imaging as they offer accurate, high-quality images, making it a practical option for widespread use.
Diabetic Retinopathy Detection Through Smartphonebased Retinal Imaging Approach: A Brief Review
Artificial intelligence (AI) algorithms provide powerful tools for analyzing massive amounts of medical data and information. Such capabilities, particularly, help infer useful patterns and insights that may not be obvious to humans, thereby triggering strong interest in developing medical applications, especially those related to the diagnosis of diseases. Several studies have found that AI can be applied to enhance the automated early and accurate detection of vision-threatening diseases of the retina using fundus images, the most prominent of which is diabetic retinopathy (DR). This paper briefly reviews various retinal imaging techniques and proposes a standalone application for detecting DR by integrating a simplified retinal imaging smartphone-based technique with a machine learning algorithm. While ophthalmologists traditionally use clinical ophthalmoscopy for retinal exams to screen for this disease, recent technological advances now allow smartphones to serve as cost-effective tools for retinal imaging as they offer accurate, high-quality images, making it a practical option for widespread use.
Diabetic Retinopathy Detection Through Smartphonebased Retinal Imaging Approach: A Brief Review
Ahmed Al-Tayeb, Khawla (Autor:in) / Jarndal, Anwar (Autor:in) / Dahrouj, Hayssam (Autor:in) / Dautov, Kassen (Autor:in)
03.06.2024
489865 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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