TY - JOUR
T1 - Estimation of Physiologic Pressures
T2 - Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives
AU - Manga, Sharanya
AU - Muthavarapu, Neha
AU - Redij, Renisha
AU - Baraskar, Bhavana
AU - Kaur, Avneet
AU - Gaddam, Sunil
AU - Gopalakrishnan, Keerthy
AU - Shinde, Rutuja
AU - Rajagopal, Anjali
AU - Samaddar, Poulami
AU - Damani, Devanshi N.
AU - Shivaram, Suganti
AU - Dey, Shuvashis
AU - Mitra, Dipankar
AU - Roy, Sayan
AU - Kulkarni, Kanchan
AU - Arunachalam, Shivaram P.
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/6
Y1 - 2023/6
N2 - The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.
AB - The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.
KW - blood pressure
KW - capillary wedge pressure
KW - conductivity
KW - dielectric properties
KW - hepatic portal gradients
KW - intracranial pressures
KW - microwave imaging
KW - microwaves
KW - noninvasive sensors
KW - permittivity
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U2 - 10.3390/s23125744
DO - 10.3390/s23125744
M3 - Review article
C2 - 37420919
AN - SCOPUS:85164000197
SN - 1424-8220
VL - 23
JO - Sensors
JF - Sensors
IS - 12
M1 - 5744
ER -