Artificial Intelligence and Hypertension: Recent Advances and Future Outlook. (2nd July 2020)

• Record Type: Journal Article
• Authors: Chaikijurajai, Thanat; Laffin, Luke J; Tang, Wai Hong Wilson
• Is Part Of: American journal of hypertension. Volume 33:Number 11(2020)
• Page Start: 967
• Page End: 974
• Publication Date: 2020-07-02
• Subjects: artificial intelligence -- blood pressure -- blood pressure measurement -- deep learning -- hypertension -- machine learning; Hypertension -- Periodicals; 616.132005
• DOI: 10.1093/ajh/hpaa102
• Languages: English
• ISSNs: 0895-7061

Abstract

Abstract: Prevention and treatment of hypertension (HTN) are a challenging public health problem. Recent evidence suggests that artificial intelligence (AI) has potential to be a promising tool for reducing the global burden of HTN, and furthering precision medicine related to cardiovascular (CV) diseases including HTN. Since AI can stimulate human thought processes and learning with complex algorithms and advanced computational power, AI can be applied to multimodal and big data, including genetics, epigenetics, proteomics, metabolomics, CV imaging, socioeconomic, behavioral, and environmental factors. AI demonstrates the ability to identify risk factors and phenotypes of HTN, predict the risk of incident HTN, diagnose HTN, estimate blood pressure (BP), develop novel cuffless methods for BP measurement, and comprehensively identify factors associated with treatment adherence and success. Moreover, AI has also been used to analyze data from major randomized controlled trials exploring different BP targets to uncover previously undescribed factors associated with CV outcomes. Therefore, AI-integrated HTN care has the potential to transform clinical practice by incorporating personalized prevention and treatment approaches, such as determining optimal and patient-specific BP goals, identifying the most effective antihypertensive medication regimen for an individual, and developing interventions targeting modifiable risk factors. Although the role of AI in HTN has been increasingly recognized over the past decade, it remains in its infancy, and future studies with big data analysis and N -of-1 study design are needed to further demonstrate the applicability of AI in HTN prevention and treatment.