A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-03, Vol.24 (6), p.1730
• Main Authors: Mousavi, Seyedeh Somayyeh, Reyna, Matthew A, Clifford, Gari D, Sameni, Reza
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Bayes Theorem; Bias; bias in blood pressure; Blood pressure; Blood Pressure - physiology; Blood Pressure Determination; Blood vessels; Compliance; cuff-based blood pressure; demographics; Heart rate; Humans; Hypertension; Hypertension - diagnosis; individualized medicine; Machine learning; Measurement; Patient satisfaction; Physiology; Review; Veins & arteries; Viscosity
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24061730

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been predominantly driven by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has raised significant concerns regarding the accuracy of reported BP values across settings. In this survey, which focuses mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices that use artificial intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and provide individualized BP-related cardiovascular risk indexes.