Key result
A systematic literature review evaluated various AI algorithms and information-digging strategies for the classification, automated diagnosis, detection, and early prediction of DCAN.
Why the study?
Standard Ewing battery testing for DCAN cannot distinguish sub-clinical CAN and requires patient cooperation, highlighting the need for AI-based automated diagnostic approaches.
Do AI algorithms improve the automated diagnosis, detection, and early prediction of diabetic cardiac autonomic neuropathy?
Systematic Review
Do AI algorithms improve the automated diagnosis, detection, and early prediction of diabetic cardiac autonomic neuropathy?
Answers are grounded in this paper and cite their sources.
AI algorithms offer potential strategies for the automated diagnosis and early prediction of diabetic cardiac autonomic neuropathy, addressing limitations of standard clinical methods like the Ewing battery.
Cardiac autonomic neuropathy (CAN) is one of the most serious complications of diabetes known as DCAN. It is a complexity of Diabetes Mellitus (DM) that is allied with extended hazards of cardiovascular mortality. The management along with early detection of DCAN is significant as early intervention can forestall further disease progression. At present, the Ewing battery is the standard clinical method for assessing DCAN. However, it cannot distinguish sub-clinical CAN and it also requires patient cooperation. In this paper, a systematic literature review has been represented which focuses on information-digging strategies in consideration of AI technologies. In this paper, various strategies, methods, and algorithms are compared and represented with their results which are used for Classification, Automated Diagnosis, Detection, and Early prediction of DCAN tasks.
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Nagar et al. (2022) conducted a systematic review in Diabetic Cardiac Autonomic Neuropathy. AI Algorithms was evaluated on Classification, Automated Diagnosis, Detection, and Early prediction of DCAN. A systematic literature review evaluated various AI algorithms and information-digging strategies for the classification, automated diagnosis, detection, and early prediction of DCAN.