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Manga S, Muthavarapu N, Redij R, Baraskar B, Kaur A, Gaddam S, Gopalakrishnan K, Shinde R, Rajagopal A, Samaddar P, Damani DN, Shivaram S, Dey S, Mitra D, Roy S, Kulkarni K, Arunachalam SP. Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives. SENSORS (BASEL, SWITZERLAND) 2023; 23:5744. [PMID: 37420919 DOI: 10.3390/s23125744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/25/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
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.
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Affiliation(s)
- 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
| | | | - 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
- Microwave Engineering and Imaging Laboratory (MEIL), 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
| | | | - 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
- Department of Internal Medicine, Texas Tech University Health Science Center, El Paso, TX 79995, 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
- Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA
| | - Dipankar Mitra
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Computer Science, University of Wisconsin-La Crosse, La Crosse, WI 54601, USA
| | - Sayan Roy
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Electrical Engineering and Computer Science, South Dakota Mines, Rapid City, SD 57701, USA
| | - Kanchan Kulkarni
- Centre de Recherche Cardio-Thoracique de Bordeaux, University of Bordeaux, INSERM, U1045, 33000 Bordeaux, France
- IHU Liryc, Heart Rhythm Disease Institute, Fondation Bordeaux Université, Bordeaux, 33600 Pessac, France
| | - Shivaram P Arunachalam
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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