Artificial Intelligence in Hypertension: A Futuristic Approach in Blood Pressure Management
Main Article Content
Abstract
Hypertension is not only the most prevalent disease but also is posing a global burden as well as national burden on the health system. Coping the disease with physical means is quite common but now in emerging India, the transformation in the management of the disease via technology is getting popular. In the same array, Artificial Intelligence is seeking its way for newer innovations in the field of medicine. AI brings a multitude of benefits to the field of medicine, transforming various aspects of healthcare delivery and improving patient outcomes. AI systems continuously learn from new data, adapting and improving over time to provide more accurate diagnostics and treatment recommendations. This article highlights the role of AI in managing hypertension, considering its diagnosis, treatment and limitations. AI-based systems are now replacing conventional BP monitors. Not only measurement but AI could play critical role in diagnosing, treating and managing hypertension. Moreover, the prediction of undiagnosed hypertension is also possible through AI. Though there are still technical glitches and biases in AI, it could be worked on in the future. Thus, AI-based healthcare systems will make clinical practice more precise and ensure personalized medicine.
Metrics
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
All open access articles published in the journal are distributed under the terms of the CC-BY-NC-SA 4.0 license (Creative Commons Attribution-Non-commercial-ShareAlike 4.0 International License) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work is properly cited and distributed under the same license (ShareAlike). Under Creative Commons, authors retain copyright in their articles.
References
Pan American Health Organization-PAHO/WHO. World Hypertension Day 2020. https://www.paho.org/en/campaigns/world-hypertension-day-2020#:~:text=Hypertension%20affects%20more%20than%2030,heart%20failure%2C%20arrhythmia%20and%20dementia. Accessed November 17, 2023.
World Health Organization (WHO). Hypertension 2023. https://www.who.int/news-room/fact-sheets/detail/hypertension. Accessed November 17, 2023.
Mendis S, Puska P, Norrving BE, World Health Organization. Global atlas on cardiovascular disease prevention and control. World Health Organization; 2011.
Pereira M, Lunet N, Azevedo A, Barros H. Differences in prevalence, awareness, treatment and control of hypertension between developing and developed countries. Journal of hypertension. 2009 May 1;27(5):963-75.
Egan BM, Kjeldsen SE, Grassi G, Esler M, Mancia G. The global burden of hypertension exceeds 1.4 billion people: should a systolic blood pressure target below 130 become the universal standard?. Journal of hypertension. 2019 Jun 1;37(6):1148-53.
Zhou B, Carrillo-Larco RM, Danaei G, Riley LM, Paciorek CJ, Stevens GA, Gregg EW, Bennett JE, Solomon B, Singleton RK, Sophiea MK. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. The Lancet. 2021 Sep 11;398(10304):957-80.
Kario K, Mogi M, Hoshide S. Latest hypertension research to inform clinical practice in Asia. Hypertension Research. 2022 Apr;45(4):555-72.
Gupta R. Trends in hypertension epidemiology in India. Journal of human hypertension. 2004 Feb;18(2):73-8.
Thankappan KR, Sivasankaran S, Khader SA, Padmanabhan PG, Sarma PS, Mini GK, Vasan RS. Prevalence, correlates, awareness, treatment, and control of hypertension in Kumarakom, Kerala: baseline results of community-based intervention program. Indian heart journal. 2006 Jan 1;58(1):28.
Gupta R. Meta-analysis of prevalence of hypertension in India. Indian heart journal. 1997 Jan 1;49(1):43-8.
Das SK, Sanyal K, Basu A. Study of urban community survey in India: growing trend of high prevalence of hypertension in a developing country. International journal of medical sciences. 2005;2(2):70.
World Health Organization. Noncommunicable diseases. World Health Organization. Regional Office for Europe; 2011.
Jose AP, Prabhakaran D. World hypertension day: contemporary issues faced in India. The Indian journal of medical research. 2019 May;149(5):567.
Lewington S. Prospective studies collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360:1903-13.
Dzau VJ, Balatbat CA. Future of hypertension: The need for transformation. Hypertension. 2019 Sep;74(3):450-7.
Stoumpos AI, Kitsios F, Talias MA. Digital Transformation in Healthcare: Technology Acceptance and Its Applications. International journal of environmental research and public health. 2023 Feb 15;20(4):3407.
Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer?. The American journal of medicine. 2018 Feb 1;131(2):129-33.
Umapathy VR, Raj RD, Yadav S, Munavarah SA, Anandapandian PA, Mary AV, Padmavathy K, Akshay R, Rajkumar DS, Anandapandian IV PA, Mary V. Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field. Cureus. 2023 Sep 21;15(9).
Bitkina OV, Park J, Kim HK. Application of artificial intelligence in medical technologies: A systematic review of main trends. Digital Health. 2023 Jul;9:20552076231189331.
https://www.britannica.com/technology/artificial-intelligence
Moor J. The Dartmouth College artificial intelligence conference: The next fifty years. Ai Magazine. 2006 Dec 15;27(4):87-.
Padmanabhan S, Tran TQ, Dominiczak AF. Artificial intelligence in hypertension: seeing through a glass darkly. Circulation Research. 2021 Apr 2;128(7):1100-18.
Howard J. Artificial intelligence: Implications for the future of work. American journal of industrial medicine. 2019 Nov;62(11):917-26.
Letzen B, Wang CJ, Chapiro J. The role of artificial intelligence in interventional oncology: a primer. Journal of vascular and interventional radiology: JVIR. 2019 Jan;30(1):38-41.
Alexander G, Staggers N. A systematic review of the designs of clinical technology: findings and recommendations for future research. Advances in nursing science. 2009 Jul 1;32(3):252-79.
Matias I, Garcia N, Pirbhulal S, Felizardo V, Pombo N, Zacarias H, Sousa M, Zdravevski E. Prediction of Atrial Fibrillation using artificial intelligence on Electrocardiograms: A systematic review. Computer Science Review. 2021 Feb 1;39:100334.
Mehra S, Hasanuzzaman M. Detection of Offensive Language in Social Media Posts (Doctoral dissertation, Ph. D. thesis). 2020 May
Sarker IH, Furhad MH, Nowrozy R. Ai-driven cybersecurity: an overview, security intelligence modeling and research directions. SN Computer Science. 2021 May;2:1-8.
Sarker IH. Machine learning: Algorithms, real-world applications and research directions. SN computer science. 2021 May;2(3):160.
Balafar MA, Ramli AR, Saripan MI, Mashohor S. Review of brain MRI image segmentation methods. Artificial Intelligence Review. 2010 Mar;33:261-74.
Gudigar A, Raghavendra U, Hegde A, Kalyani M, Ciaccio EJ, Acharya UR. Brain pathology identification using computer aided diagnostic tool: A systematic review. Computer Methods and Programs in Biomedicine. 2020 Apr 1;187:105205.
Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health. 2021 Jan;18(1):271.
Huang KH, Tan F, Wang TD, Yang YJ. A highly sensitive pressure-sensing array for blood pressure estimation assisted by machine-learning techniques. Sensors. 2019 Feb 19;19(4):848.
Chowdhury MH, Shuzan MN, Chowdhury ME, Mahbub ZB, Uddin MM, Khandakar A, Reaz MB. Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques. Sensors. 2020 Jan;20(11):3127.
Quan X, Liu J, Roxlo T, Siddharth S, Leong W, Muir A, Cheong SM, Rao A. Advances in non-invasive blood pressure monitoring. Sensors. 2021 Jun 22;21(13):4273.
Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artificial intelligence in medicine. 2011 Oct 1;53(2):127-38.
Solà J, Proença M, Braun F, Pierrel N, Degiorgis Y, Verjus C, Lemay M, Bertschi M, Schoettker P. Continuous non-invasive monitoring of blood pressure in the operating room: a cuffless optical technology at the fingertip. Current Directions in Biomedical Engineering. 2016 Sep 1;2(1):267-71.
Miao F, Fu N, Zhang YT, Ding XR, Hong X, He Q, Li Y. A novel continuous blood pressure estimation approach based on data mining techniques. IEEE journal of biomedical and health informatics. 2017 Apr 28;21(6):1730-40.
Wang L, Zhou W, Xing Y, Zhou X. A novel neural network model for blood pressure estimation using photoplethesmography without electrocardiogram. Journal of healthcare engineering. 2018 Mar 7;2018.
Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, Ramirez A, Schlaich M, Stergiou GS, Tomaszewski M, Wainford RD. 2020 International Society of Hypertension global hypertension practice guidelines. Hypertension. 2020 Jun;75(6):1334-57.
Elgendi M, Fletcher R, Liang Y, Howard N, Lovell NH, Abbott D, Lim K, Ward R. The use of photoplethysmography for assessing hypertension. NPJ digital medicine. 2019 Jun 26;2(1):60.
Kario K. Management of hypertension in the digital era: small wearable monitoring devices for remote blood pressure monitoring. Hypertension. 2020 Sep;76(3):640-50.
LaFreniere D, Zulkernine F, Barber D, Martin K. Using machine learning to predict hypertension from a clinical dataset. In2016 IEEE symposium series on computational intelligence (SSCI) 2016 Dec 6 (pp. 1-7). IEEE.
Hermida RC, Smolensky MH, Ayala DE, Portaluppi F. Ambulatory Blood Pressure Monitoring (ABPM) as the reference standard for diagnosis of hypertension and assessment of vascular risk in adults. Chronobiology International. 2015 Nov 26;32(10):1329-42.
Pierdomenico SD, Cuccurullo F. Prognostic value of white-coat and masked hypertension diagnosed by ambulatory monitoring in initially untreated subjects: an updated meta analysis. American journal of hypertension. 2011 Jan 1;24(1):52-8.
Whelton WP. 2017 Guideline for the prevention, detection, evaluation, and management of high blood pressure in adults. J Am Coll Cardiol. 2017.Simonite T. AI Can Help Apple Watch Predict High Blood Pressure, Sleep Apnea.
Haugg F, Elgendi M, Menon C. Assessment of blood pressure using only a smartphone and machine learning techniques: A systematic review. Frontiers in Cardiovascular Medicine. 2022 Jun 13;9:894224.
David Petronzio. Hampshire AI technology used to take blood pressure. BBC News. 2023. https://www.bbc.com/news/uk-england-hampshire-67134503
Miyashita M, Brady M. The health care benefits of combining wearables and AI. Harv. Bus. Rev. 2019 Jun 18.
Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, Clement DL, Coca A, De Simone G, Dominiczak A, Kahan T. 2018 ESC/ESH Guidelines for the management of arterial hypertension: The Task Force for the management of arterial hypertension of the European Society of Cardiology (ESC) and the European Society of Hypertension (ESH). European heart journal. 2018 Sep 1;39(33):3021-104.
Whelton PK, Carey RM, Aronow WS, Casey DE, Collins KJ, Dennison Himmelfarb C, DePalma SM, Gidding S, Jamerson KA, Jones DW, MacLaughlin EJ. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Journal of the American College of Cardiology. 2018 May 15;71(19):e127-248.
Samant R, Rao S. Evaluation of artificial neural networks in prediction of essential hypertension. International Journal of Computer Applications. 2013 Jan 1;81(12).
LaFreniere D, Zulkernine F, Barber D, Martin K. Using machine learning to predict hypertension from a clinical dataset. In2016 IEEE symposium series on computational intelligence (SSCI) 2016 Dec 6 (pp. 1-7). IEEE.
Wu TH, Pang GK, Kwong EW. Predicting systolic blood pressure using machine learning. In7th international conference on information and automation for sustainability 2014 Dec 22 (pp. 1-6). IEEE.
Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature biomedical engineering. 2018 Mar;2(3):158-64.
Chaikijurajai T, Laffin LJ, Tang WH. Artificial intelligence and hypertension: recent advances and future outlook. American Journal of Hypertension. 2020 Nov;33(11):967-74.
Ioannis Paschalidis. New artificial intelligence program could help treat hypertension. National Science Foundation (NSF). 2023 June
Dawes TJ, de Marvao A, Shi W, Fletcher T, Watson GM, Wharton J, Rhodes CJ, Howard LS, Gibbs JS, Rueckert D, Cook SA. Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology. 2017 May;283(2):381-90.
Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2014 Oct 22;31(5):761-3.
Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning–based sequence model. Nature methods. 2015 Oct;12(10):931-4.
Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nature genetics. 2018 Aug;50(8):1171-9.
Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA, Fritzilas N, Hakenberg J, Dutta A, Shon J, Xu J. Predicting the clinical impact of human mutation with deep neural networks. Nature genetics. 2018 Aug;50(8):1161-70.
Huan T, Meng Q, Saleh MA, Norlander AE, Joehanes R, Zhu J, Chen BH, Zhang B, Johnson AD, Ying S, Courchesne P. Integrative network analysis reveals molecular mechanisms of blood pressure regulation. Molecular systems biology. 2015 Apr;11(4):799.
Li YH, Zhang GG, Wang N. Systematic characterization and prediction of human hypertension genes. Hypertension. 2017 Feb;69(2):349-55.
Alimadadi A, Manandhar I, Aryal S, Munroe PB, Joe B, Cheng X. Machine learning-based classification and diagnosis of clinical cardiomyopathies. Physiological Genomics. 2020 Sep 1;52(9):391-400.
Aryal S, Alimadadi A, Manandhar I, Joe B, Cheng X. Machine learning strategy for gut microbiome-based diagnostic screening of cardiovascular disease. Hypertension. 2020 Nov;76(5):1555-62.
Held E, Cape J, Tintle N. Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data. InBMC proceedings 2016 Oct (Vol. 10, No. 7, pp. 141-145). BioMed Central.
Rastegar S, Gholamhosseini H, Lowe A, Mehdipour F, Lindén M. Estimating Systolic Blood Pressure Using Convolutional Neural Networks. InpHealth 2019 Jan 1 (pp. 143-149).
Pei Z, Liu J, Liu M, Zhou W, Yan P, Wen S, Chen Y. Risk-predicting model for incident of essential hypertension based on environmental and genetic factors with support vector machine. Interdisciplinary Sciences: Computational Life Sciences. 2018 Mar;10:126-30.
Maxwell A, Li R, Yang B, Weng H, Ou A, Hong H, Zhou Z, Gong P, Zhang C. Deep learning architectures for multi-label classification of intelligent health risk prediction. BMC bioinformatics. 2017 Dec;18:121-31.
Koren G, Nordon G, Radinsky K, Shalev V. Machine learning of big data in gaining insight into successful treatment of hypertension. Pharmacology Research & Perspectives. 2018 Jun;6(3):e00396.
Guthrie NL, Berman MA, Edwards KL, Appelbaum KJ, Dey S, Carpenter J, Eisenberg DM, Katz DL. Achieving rapid blood pressure control with digital therapeutics: retrospective cohort and machine learning study. JMIR cardio. 2019 Mar 12;3(1):e13030.
João da Silva V, da Silva Souza V, Guimarães da Cruz R, Mesquita Vidal Martínez de Lucena J, Jazdi N, Ferreira de Lucena Junior V. Commercial devices-based system designed to improve the treatment adherence of hypertensive patients. Sensors. 2019 Oct 18;19(20):4539.
Miller DD. Machine intelligence in cardiovascular medicine. Cardiology in Review. 2020 Mar 1;28(2):53-64.
Padmanabhan S, Tran TQ, Dominiczak AF. Artificial intelligence in hypertension: seeing through a glass darkly. Circulation Research. 2021 Apr 2;128(7):1100-18.
Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, Min JK, Tang WW, Halperin JL, Narayan SM. Deep learning for cardiovascular medicine: a practical primer. European heart journal. 2019 Jul 1;40(25):2058-73.
Liu Y, Li Z, Xiong H, Gao X, Wu J. Understanding of internal clustering validation measures. In2010 IEEE international conference on data mining 2010 Dec 13 (pp. 911-916). IEEE.
Handelman GS, Kok HK, Chandra RV, Razavi AH, Huang S, Brooks M, Lee MJ, Asadi H. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. American Journal of Roentgenology. 2019 Jan;212(1):38-43.