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This study presents a novel approach for modeling the AV in the CV system, offering an improved method for estimating crucial parameters like LVQ across various AV conditions, including aortic stenosis (AS). The model, based on AV channel length during the entire cardiac phase, introduces a time\u2010varying AV resistance (TV\u2010AVR) parameterized by the pressure ratio across the AV and LVQ, enabling the simulation of both healthy and AS\u2010related conditions. To validate this model, in vitro measurements are compared using a hybrid mock circulatory loop device. An unconventional use of a convolutional neural network (CNN) corrects the model's estimates, eliminating the need for labeled datasets. This approach, incorporating real\u2010time learning and transforming 1\u2010D CV signals into 2\u2010D tensors, significantly improves the accuracy of LVQ measurements, achieving an error rate of less than 3.41\u2009\u00b1\u20094.84% for CO in healthy conditions and 2.83\u2009\u00b1\u20091.35% in AS cases\u2014a 33.13% enhancement over linear diode models. These results underscore the potential of this approach for enhancing the diagnosis, prediction, and treatment of AV diseases. The key contributions of the proposed method encompass nonlinear TV\u2010AVR estimation, investigation of transient CV responses, prediction of instantaneous CO, development of a flexible framework for noninvasive measurements integration, and the introduction of an adjustable resistance model using an extended Kalman filter (EKF) and CNN combination, all without requiring labeled data.<\/jats:p>","DOI":"10.1002\/cnm.3838","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T10:44:48Z","timestamp":1718707488000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An intelligent aortic valve model for complete cardiac cycle"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2261-8218","authenticated-orcid":false,"given":"Mehmet","family":"Iscan","sequence":"first","affiliation":[{"name":"Mechatronics Engineering Department Yildiz Technical University  Istanbul Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8404-9877","authenticated-orcid":false,"given":"Aydin","family":"Yesildirek","sequence":"additional","affiliation":[{"name":"Mechatronics Engineering Department Yildiz Technical University  Istanbul Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0828\u2010282x(09)70098\u20104"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.21037\/jtd\u201022\u2010278"},{"issue":"3","key":"e_1_2_9_4_1","first-page":"11","article-title":"Fluid\u2010structure interaction modeling of aortic valve stenosis at different heart rates","volume":"18","author":"Bahraseman HG","year":"2016","journal-title":"Acta Bioeng Biomech"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3063129"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbiomech.2019.07.010"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"CouceiroR CarvalhoP PaivaRP et al.Beat\u2010to\u2010beat cardiac output inference using heart sounds. 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