Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910911255543808 |
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| author | Bazargani, Yasin Sadeghi Mirzaei, Majid Sobhi, Navid Abdollahi, Mirsaeed Jafarizadeh, Ali Pedrammehr, Siamak Alizadehsani, Roohallah Tan, Ru San Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra |
| author_facet | Bazargani, Yasin Sadeghi Mirzaei, Majid Sobhi, Navid Abdollahi, Mirsaeed Jafarizadeh, Ali Pedrammehr, Siamak Alizadehsani, Roohallah Tan, Ru San Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra |
| contents | Diabetes mellitus (DM) predisposes patients to vascular complications. Retinal images and vasculature reflect the body's micro- and macrovascular health. They can be used to diagnose DM complications, including diabetic retinopathy (DR), neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as forecast the risk of cardiovascular events. Artificial intelligence (AI)-enabled systems developed for high-throughput detection of DR using digitized retinal images have become clinically adopted. Beyond DR screening, AI integration also holds immense potential to address challenges associated with the holistic care of the patient with DM. In this work, we aim to comprehensively review the literature for studies on AI applications based on retinal images related to DM diagnosis, prognostication, and management. We will describe the findings of holistic AI-assisted diabetes care, including but not limited to DR screening, and discuss barriers to implementing such systems, including issues concerning ethics, data privacy, equitable access, and explainability. With the ability to evaluate the patient's health status vis a vis DM complication as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18600 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina Bazargani, Yasin Sadeghi Mirzaei, Majid Sobhi, Navid Abdollahi, Mirsaeed Jafarizadeh, Ali Pedrammehr, Siamak Alizadehsani, Roohallah Tan, Ru San Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Image and Video Processing Artificial Intelligence Tissues and Organs J.3.2; J.3.3 Diabetes mellitus (DM) predisposes patients to vascular complications. Retinal images and vasculature reflect the body's micro- and macrovascular health. They can be used to diagnose DM complications, including diabetic retinopathy (DR), neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as forecast the risk of cardiovascular events. Artificial intelligence (AI)-enabled systems developed for high-throughput detection of DR using digitized retinal images have become clinically adopted. Beyond DR screening, AI integration also holds immense potential to address challenges associated with the holistic care of the patient with DM. In this work, we aim to comprehensively review the literature for studies on AI applications based on retinal images related to DM diagnosis, prognostication, and management. We will describe the findings of holistic AI-assisted diabetes care, including but not limited to DR screening, and discuss barriers to implementing such systems, including issues concerning ethics, data privacy, equitable access, and explainability. With the ability to evaluate the patient's health status vis a vis DM complication as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM. |
| title | Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina |
| topic | Image and Video Processing Artificial Intelligence Tissues and Organs J.3.2; J.3.3 |
| url | https://arxiv.org/abs/2402.18600 |