6/13/2023 0 Comments Korotkoff soundsWe present a review of the rational use of K-sounds to evaluate changes in cardiac functions in real-time for rapid detection of patients with cardiac dysfunction roughly. Based on this idea, we propose a feasible means of early screening with respect to the cardiac function. We hypothesized that HF occurrence involves alterations of K-sounds signals, and the use of the K-sounds approach to assessing cardiac functions will aid clinical decisions. It is reasonable to perform data labeling of actual clinical outcomes, which will enable the use of DL-based approaches to dynamically monitor cardiac functions with K-sounds and to predict the onset and progression of HF. Based on the characteristics of DL, it may be challenging to establish signal differences between K-sounds in patients suffering from HF and patients with normal cardiac functions. These algorithms can mine informative data features from massive data, and they only need to give good data annotations to obtain satisfactory training results. Deep learning (DL) algorithms have been developed ( 20, 21). The medical field is also benefiting from the advances in AI algorithms, which have increased collaborations in medical–industrial crossover projects ( 17– 19). Technological advances have introduced the use of artificial intelligence (AI) algorithms in various fields. The five temporal phases of K-sounds are complex, and currently, the intrinsic mechanisms have not been fully established ( 16). Due to widespread awareness of BP measurements and the high prevalence of hypertension and HF, Cotoi’s vision using K-sounds can still be used to monitor cardiac functions. As a result of the limitations of relevant equipment in that era, the theory was not well accepted. evaluated the possibility of using K-sounds to assess ventricular performance ( 15). The K-sounds are not limited to BP measurements, and some clinical treatments have long been noted. However, currently, the assessment of cardiac functions is challenging. It is difficult to predict at which point HF will occur however, it is still feasible to stabilize cardiac functions, thereby preventing the onset of HF through reasonable monitoring. The onset of acute HF is attributed to various triggers ( 11, 13, 14). Heart failure involves the deterioration of cardiac functions. Moreover, they do not reflect dynamic changes in cardiac functions. These predictive models are generally based on patients’ laboratory findings, with some accessible clinical features that enrich model heterogeneity and quantify the risk score, but they are more cumbersome and complex in their operationalization ( 12). Thus, various prediction models for HF have emerged, and they have substantially improved in the last decade. HF is a slow myocardial remodeling process, and as a complex group of clinical symptoms, clinical guidelines emphasize its prevention ( 6– 11). Prolonged inappropriate elevations of BP can lead to a series of adverse cardiovascular events that eventually result in cardiovascular end-stage heart failure (HF) ( 4, 5). Accurate BP measurement facilitates daily monitoring of an individual’s vital signs, while inaccurate results can cause unnecessary panic, as a 5-mmHg error can halve or double the number of hypertensive patients ( 2, 3).Īccurate BP monitoring is clinically important. When done by a trained clinical practitioner, the K-sounds approach may yield more accurate results than the automated oscillometric method ( 2). Blood pressure (BP) measurement using Korotkoff sounds (K-sounds), which are considered the gold standard for BP measurement, has been performed for over 100 years ( 1).
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