Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives
Sharanya Manga,
Neha Muthavarapu,
Renisha Redij,
Bhavana Baraskar,
Avneet Kaur,
Sunil Gaddam,
Keerthy Gopalakrishnan,
Rutuja Shinde,
Anjali Rajagopal,
Poulami Samaddar,
Devanshi N. Damani,
Suganti Shivaram,
Shuvashis Dey,
Dipankar Mitra,
Sayan Roy,
Kanchan Kulkarni,
Shivaram P. Arunachalam
Affiliations
Sharanya Manga
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
Neha Muthavarapu
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
Renisha Redij
GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Bhavana Baraskar
Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
Avneet Kaur
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Sunil Gaddam
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Keerthy Gopalakrishnan
GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Rutuja Shinde
Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Anjali Rajagopal
Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Poulami Samaddar
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Devanshi N. Damani
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
Suganti Shivaram
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
Shuvashis Dey
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Dipankar Mitra
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Sayan Roy
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Kanchan Kulkarni
Centre de Recherche Cardio-Thoracique de Bordeaux, University of Bordeaux, INSERM, U1045, 33000 Bordeaux, France
Shivaram P. Arunachalam
GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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.