Related papers: Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina

Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina

URL: http://arxiv.org/abs/2402.18600v1
Date: Wed, 28 Feb 2024 00:31:17 GMT
Title: Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina
Authors: Yasin Sadeghi Bazargani, Majid Mirzaei, Navid Sobhi, Mirsaeed Abdollahi, Ali Jafarizadeh, Siamak Pedrammehr, Roohallah Alizadehsani, Ru San Tan, Sheikh Mohammed Shariful Islam, U. Rajendra Acharya
Abstract summary: We review the literature for studies on AI applications based on retinal images related to diabetes diagnosis, prognostication, and management. We will describe the findings of holistic AI-assisted diabetes care, including but not limited to DR screening. We will discuss barriers to implementing such systems, including issues concerning ethics, data privacy, equitable access, and explainability.
Score: 7.740438266232459
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: 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.

Related papers

CDRH Seeks Public Comment: Digital Health Technologies for Detecting Prediabetes and Undiagnosed Type 2 Diabetes [0.0]
FDA asked for public comments on role of digital health technologies (DHTs) in detecting prediabetes and undiagnosed type 2 diabetes. DHTs capture health signals like glucose, diet, symptoms and community insights. Subpopulations that could benefit most from remote screening tools include rural residents, minority groups, high-risk individuals and those with limited healthcare access.
arXiv Detail & Related papers (2023-12-18T14:20:53Z)
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade [8.500005168315292]
Use of artificial intelligence (AI) methods has been on the rise lately for the analysis of different CVD-related topics. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with non-invasive retinal scanning. There is great potential to reduce the number of cardiovascular events and the financial strain on healthcare systems by utilizing AI-assisted early detection and prediction of cardiovascular diseases on a large scale.
arXiv Detail & Related papers (2023-11-11T13:09:11Z)
Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis [59.35504779947686]
GPT-4V is OpenAI's newest model for multimodal medical diagnosis. Our evaluation encompasses 17 human body systems. GPT-4V demonstrates proficiency in distinguishing between medical image modalities and anatomy. It faces significant challenges in disease diagnosis and generating comprehensive reports.
arXiv Detail & Related papers (2023-10-15T18:32:27Z)
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images [51.27125547308154]
We organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge.
arXiv Detail & Related papers (2023-04-05T12:04:55Z)
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
arXiv Detail & Related papers (2023-02-11T18:07:11Z)
Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases [0.0]
This study aims to evaluate an explainable staged grading process without using deep Convolutional Neural Networks (CNNs) directly. We have proposed a clinician-in-the-loop assisted intelligent workflow that performs a retinal vascular assessment on the fundus images. The semiautomatic methodology aims to have a federated approach to AI in healthcare applications with more inputs and interpretations from clinicians.
arXiv Detail & Related papers (2022-12-14T07:00:31Z)
A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening [0.0]
Diabetes Mellitus (DM) can lead to significant microvasculature disruptions that eventually causes diabetic retinopathy (DR) Our review is intended for anyone, from student to established researcher, who wants to understand what can be accomplished with CAD systems.
arXiv Detail & Related papers (2022-08-03T02:11:42Z)
An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes [59.116811751334225]
VirtualDSA++ is an algorithm designed to segment and label the cerebrovascular tree on CTA scans. We extend the labeling mechanism for the cerebral arteries to identify occluded vessels. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features.
arXiv Detail & Related papers (2022-04-26T14:20:26Z)
An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
arXiv Detail & Related papers (2021-03-02T13:14:15Z)
A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
arXiv Detail & Related papers (2020-08-22T07:48:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.