TY - CHAP
T1 - A state-of-the-art review on computational methods for predicting the occurrence of cardiac autonomic neuropathy
AU - Hui, Jeremy Man Ho
AU - Lee, Yan Hiu Athena
AU - Tse, Gary
AU - Liu, Tong
AU - Jeevaratnam, Kamalan
AU - Liu, Haipeng
N1 - Publisher Copyright:
© 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Cardiac autonomic neuropathy (CAN) is a serious complication of type 2 diabetes mellitus (T2DM) that is associated with an increased risk of cardiovascular mortality. Nonetheless, it is a common and often underdiagnosed complication of T2DM. CAN occurs due to damage to the autonomic nervous system and manifests as coronary vessels ischemia, arrhythmias, silent myocardial infarction, severe orthostatic hypotension, and sudden death syndrome. Clinical approaches for evaluating CAN include assessment of symptoms and signs, cardiovascular reflex tests based on heart rate and blood pressure known as Ewing's test, electrocardiography, and heart rate variability. In recent years, there has been an extraordinary advancement in AI-assisted diagnostics. AI-assisted diagnosis and prediction of CAN based on Ewing's test and other clinical features could facilitate the early detection of CAN. Further research utilizing large-scale databases, novel algorithms and features could further enhance the performance of AI-assisted diagnosis and prediction of CAN.
AB - Cardiac autonomic neuropathy (CAN) is a serious complication of type 2 diabetes mellitus (T2DM) that is associated with an increased risk of cardiovascular mortality. Nonetheless, it is a common and often underdiagnosed complication of T2DM. CAN occurs due to damage to the autonomic nervous system and manifests as coronary vessels ischemia, arrhythmias, silent myocardial infarction, severe orthostatic hypotension, and sudden death syndrome. Clinical approaches for evaluating CAN include assessment of symptoms and signs, cardiovascular reflex tests based on heart rate and blood pressure known as Ewing's test, electrocardiography, and heart rate variability. In recent years, there has been an extraordinary advancement in AI-assisted diagnostics. AI-assisted diagnosis and prediction of CAN based on Ewing's test and other clinical features could facilitate the early detection of CAN. Further research utilizing large-scale databases, novel algorithms and features could further enhance the performance of AI-assisted diagnosis and prediction of CAN.
KW - Artificial intelligence
KW - Cardiac autonomic neuropathy
KW - Ewing test
KW - Heart rate variability
KW - Type 2 diabetes mellitus
UR - http://www.scopus.com/inward/record.url?scp=85214754113&partnerID=8YFLogxK
U2 - 10.1016/B978-0-323-95686-4.00023-X
DO - 10.1016/B978-0-323-95686-4.00023-X
M3 - Chapter
AN - SCOPUS:85214754113
SN - 9780323956932
SP - 319
EP - 335
BT - Internet of Things and Machine Learning for Type I and Type II Diabetes
ER -
