Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve.
artificial intelligence
big data
blood pressure
deep learning
deep neural networks
digital health
hypertension
machine learning
photoplethysmograph
wearable technology
Journal
Journal of cardiovascular development and disease
ISSN: 2308-3425
Titre abrégé: J Cardiovasc Dev Dis
Pays: Switzerland
ID NLM: 101651414
Informations de publication
Date de publication:
09 Feb 2023
09 Feb 2023
Historique:
received:
27
12
2022
revised:
05
02
2023
accepted:
07
02
2023
entrez:
24
2
2023
pubmed:
25
2
2023
medline:
25
2
2023
Statut:
epublish
Résumé
Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.
Identifiants
pubmed: 36826570
pii: jcdd10020074
doi: 10.3390/jcdd10020074
pmc: PMC9963880
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
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