Cardiovascular models for personalised medicine: Where now and where next?. (October 2019)

• Record Type: Journal Article
• Authors: Hose, D. Rodney; Lawford, Patricia V.; Huberts, Wouter; Hellevik, Leif Rune; Omholt, Stig W.; van de Vosse, Frans N.
• Is Part Of: Medical engineering & physics. Volume 72(2019)
• Page Start: 38
• Page End: 48
• Publication Date: 2019-10
• Subjects: Cardiovascular modelling -- Model personalisation -- Model -- Uncertainity -- Physiological modelling -- Clinical descision support; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2019.08.007
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Model personalisation requires more than anatomical personalisation. Model uncertainty and sensitivity are important considerations for clinical interpretation. Model verification and validation are critical for trust underpinning clinical decision support. The cardiovascular digital twin will support diagnosis and prognosis by responding continuously to increasing volumes of information collected as the individual goes about their daily life. Abstract: The aim of this position paper is to provide a brief overview of the current status of cardiovascular modelling and of the processes required and some of the challenges to be addressed to see wider exploitation in both personal health management and clinical practice. In most branches of engineering the concept of the digital twin, informed by extensive and continuous monitoring and coupled with robust data assimilation and simulation techniques, is gaining traction: the Gartner Group listed it as one of the top ten digital trends in 2018. The cardiovascular modelling community is starting to develop a much more systematic approach to the combination of physics, mathematics, control theory, artificial intelligence, machine learning, computer science and advanced engineering methodology, as well as working more closely with the clinical community to better understand and exploit physiological measurements, and indeed to develop jointly better measurement protocols informed by model-based understanding. Developments in physiological modelling, model personalisation, model outcome uncertainty, and the role of models in clinical decision support are addressed and 'where-next' steps and challenges discussed.