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Henry B, Merz M, Hoang H, Abdulkarim G, Wosik J, Schoettker P. Cuffless Blood Pressure in clinical practice: challenges, opportunities and current limits. Blood Press 2024; 33:2304190. [PMID: 38245864 DOI: 10.1080/08037051.2024.2304190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/07/2024] [Indexed: 01/22/2024]
Abstract
Background: Cuffless blood pressure measurement technologies have attracted significant attention for their potential to transform cardiovascular monitoring.Methods: This updated narrative review thoroughly examines the challenges, opportunities, and limitations associated with the implementation of cuffless blood pressure monitoring systems.Results: Diverse technologies, including photoplethysmography, tonometry, and ECG analysis, enable cuffless blood pressure measurement and are integrated into devices like smartphones and smartwatches. Signal processing emerges as a critical aspect, dictating the accuracy and reliability of readings. Despite its potential, the integration of cuffless technologies into clinical practice faces obstacles, including the need to address concerns related to accuracy, calibration, and standardization across diverse devices and patient populations. The development of robust algorithms to mitigate artifacts and environmental disturbances is essential for extracting clear physiological signals. Based on extensive research, this review emphasizes the necessity for standardized protocols, validation studies, and regulatory frameworks to ensure the reliability and safety of cuffless blood pressure monitoring devices and their implementation in mainstream medical practice. Interdisciplinary collaborations between engineers, clinicians, and regulatory bodies are crucial to address technical, clinical, and regulatory complexities during implementation. In conclusion, while cuffless blood pressure monitoring holds immense potential to transform cardiovascular care. The resolution of existing challenges and the establishment of rigorous standards are imperative for its seamless incorporation into routine clinical practice.Conclusion: The emergence of these new technologies shifts the paradigm of cardiovascular health management, presenting a new possibility for non-invasive continuous and dynamic monitoring. The concept of cuffless blood pressure measurement is viable and more finely tuned devices are expected to enter the market, which could redefine our understanding of blood pressure and hypertension.
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Affiliation(s)
- Benoit Henry
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Maxime Merz
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Harry Hoang
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ghaith Abdulkarim
- Neuro-Informatics Laboratory, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN, USA
| | - Jedrek Wosik
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Patrick Schoettker
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Kario K, Williams B, Tomitani N, McManus RJ, Schutte AE, Avolio A, Shimbo D, Wang JG, Khan NA, Picone DS, Tan I, Charlton PH, Satoh M, Mmopi KN, Lopez-Lopez JP, Bothe TL, Bianchini E, Bhandari B, Lopez-Rivera J, Charchar FJ, Tomaszewski M, Stergiou G. Innovations in blood pressure measurement and reporting technology: International Society of Hypertension position paper endorsed by the World Hypertension League, European Society of Hypertension, Asian Pacific Society of Hypertension, and Latin American Society of Hypertension. J Hypertens 2024:00004872-990000000-00518. [PMID: 39246139 DOI: 10.1097/hjh.0000000000003827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2024]
Abstract
Blood pressure (BP) is a key contributor to the lifetime risk of preclinical organ damage and cardiovascular disease. Traditional clinic-based BP readings are typically measured infrequently and under standardized/resting conditions and therefore do not capture BP values during normal everyday activity. Therefore, current hypertension guidelines emphasize the importance of incorporating out-of-office BP measurement into strategies for hypertension diagnosis and management. However, conventional home and ambulatory BP monitoring devices use the upper-arm cuff oscillometric method and only provide intermittent BP readings under static conditions or in a limited number of situations. New innovations include technologies for BP estimation based on processing of sensor signals supported by artificial intelligence tools, technologies for remote monitoring, reporting and storage of BP data, and technologies for BP data interpretation and patient interaction designed to improve hypertension management ("digital therapeutics"). The number and volume of data relating to new devices/technologies is increasing rapidly and will continue to grow. This International Society of Hypertension position paper describes the new devices/technologies, presents evidence relating to new BP measurement techniques and related indices, highlights standard for the validation of new devices/technologies, discusses the reliability and utility of novel BP monitoring devices, the association of these metrics with clinical outcomes, and the use of digital therapeutics. It also highlights the challenges and evidence gaps that need to be overcome before these new technologies can be considered as a user-friendly and accurate source of novel BP data to inform clinical hypertension management strategies.
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Affiliation(s)
- Kazuomi Kario
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Bryan Williams
- University College London (UCL) and National Insitute for Health Research UCL Hospitals Biomedical Research Centre, London, United Kingdom
| | - Naoko Tomitani
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Richard J McManus
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Aletta E Schutte
- School of Population Health, University of New South Wales; The George Institute for Global Health, Sydney, Australia
| | - Alberto Avolio
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Daichi Shimbo
- Hypertension Lab, Columbia University Irving Medical Center, New York, NY, USA
| | - Ji-Guang Wang
- Centre for Epidemiological Studies and Clinical Trials, Shanghai Key Laboratory of Hypertension, Department of Hypertension, Ruijin Hospital, The Shanghai Institute of Hypertension, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Nadia A Khan
- Center for Advancing Health Outcomes, Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Dean S Picone
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
- Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia
| | - Isabella Tan
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Michihiro Satoh
- Division of Public Health, Hygiene and Epidemiology, Faculty of Medicine, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Keneilwe Nkgola Mmopi
- Department of Biomedical Sciences, Faculty of Medicine. University of Botswana, Gaborone, Botswana
| | - Jose P Lopez-Lopez
- Masira Research Institute, Medical School, Universidad de Santander, Bucaramanga, Colombia
| | - Tomas L Bothe
- Charité - Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Berlin, Germany
| | - Elisabetta Bianchini
- Institute of Clinical Physiology, Italian National Research Council, Pisa, Italy
| | - Buna Bhandari
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Jesús Lopez-Rivera
- Unidad de Hipertension arterial, V departamento, Hospital Central San Cristobal, Tachira, Venezuela
| | - Fadi J Charchar
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat
- Department of Physiology, University of Melbourne, Melbourne, Australia
- Department of Cardiovascular Sciences, University of Leicester, Leicester
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester
- Manchester Royal Infirmary, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - George Stergiou
- Hypertension Center STRIDE-7, National and Kapodistrian University of Athens, School of Medicine, Third Department of Medicine, Sotiria Hospital, Athens, Greece
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Song Z, Asiedu M, Wang S, Li Q, Ozturk A, Mittal V, Schoen S, Ramaswamy S, Pierce TT, Samir AE, Eldar YC, Chandrakasan A, Kumar V. Memory-efficient low-compute segmentation algorithms for bladder-monitoring smart ultrasound devices. Sci Rep 2023; 13:16450. [PMID: 37777523 PMCID: PMC10542811 DOI: 10.1038/s41598-023-42000-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 09/04/2023] [Indexed: 10/02/2023] Open
Abstract
Post-operative urinary retention is a medical condition where patients cannot urinate despite having a full bladder. Ultrasound imaging of the bladder is used to estimate urine volume for early diagnosis and management of urine retention. Moreover, the use of bladder ultrasound can reduce the need for an indwelling urinary catheter and the risk of catheter-associated urinary tract infection. Wearable ultrasound devices combined with machine-learning based bladder volume estimation algorithms reduce the burdens of nurses in hospital settings and improve outpatient care. However, existing algorithms are memory and computation intensive, thereby demanding the use of expensive GPUs. In this paper, we develop and validate a low-compute memory-efficient deep learning model for accurate bladder region segmentation and urine volume calculation. B-mode ultrasound bladder images of 360 patients were divided into training and validation sets; another 74 patients were used as the test dataset. Our 1-bit quantized models with 4-bits and 6-bits skip connections achieved an accuracy within [Formula: see text] and [Formula: see text], respectively, of a full precision state-of-the-art neural network (NN) without any floating-point operations and with an [Formula: see text] and [Formula: see text] reduction in memory requirements to fit under 150 kB. The means and standard deviations of the volume estimation errors, relative to estimates from ground-truth clinician annotations, were [Formula: see text] ml and [Formula: see text] ml, respectively. This lightweight NN can be easily integrated on the wearable ultrasound device for automated and continuous monitoring of urine volume. Our approach can potentially be extended to other clinical applications, such as monitoring blood pressure and fetal heart rate.
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Affiliation(s)
- Zhiye Song
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Mercy Asiedu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
| | - Shuhang Wang
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
| | - Qian Li
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
- Department of Ultrasound, Shenzhen University General Hospital, Shenzhen, Guangdong, China
| | - Arinc Ozturk
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
| | - Vipasha Mittal
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Scott Schoen
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
| | | | - Theodore T Pierce
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
| | - Anthony E Samir
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
| | - Yonina C Eldar
- Department of Computer Science and Applied Mathematics, Weizmann institute of Science, Rehovot, Israel
| | - Anantha Chandrakasan
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Viksit Kumar
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
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Ismail SNA, Nayan NA, Jaafar R, May Z. Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach. SENSORS (BASEL, SWITZERLAND) 2022; 22:6195. [PMID: 36015956 PMCID: PMC9412312 DOI: 10.3390/s22166195] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/25/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.
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Affiliation(s)
- Siti Nor Ashikin Ismail
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia
| | - Nazrul Anuar Nayan
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia
- Institute Islam Hadhari, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia
| | - Rosmina Jaafar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia
| | - Zazilah May
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia
- Electrical and Electronic Engineering Department, Universiti Teknologi Petronas, Seri Iskandar 32610, Perak, Malaysia
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