Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives
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 phys...
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/12/5744 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Sharanya Manga |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:56:33Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:56:33Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f4a7e2b7dc3e498a8d8051976892669a2023-11-18T12:35:39ZengMDPI AGSensors1424-82202023-06-012312574410.3390/s23125744Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future PerspectivesSharanya Manga0Neha Muthavarapu1Renisha Redij2Bhavana Baraskar3Avneet Kaur4Sunil Gaddam5Keerthy Gopalakrishnan6Rutuja Shinde7Anjali Rajagopal8Poulami Samaddar9Devanshi N. Damani10Suganti Shivaram11Shuvashis Dey12Dipankar Mitra13Sayan Roy14Kanchan Kulkarni15Shivaram P. Arunachalam16Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USAGIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USAMicrowave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USAMicrowave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USAGIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Medicine, Mayo Clinic, Rochester, MN 55905, USAMicrowave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USAMicrowave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USAMicrowave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USAMicrowave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USACentre de Recherche Cardio-Thoracique de Bordeaux, University of Bordeaux, INSERM, U1045, 33000 Bordeaux, FranceGIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USAThe 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.https://www.mdpi.com/1424-8220/23/12/5744microwavesdielectric propertiespermittivityconductivitymicrowave imagingblood pressure |
| spellingShingle | 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 Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives Sensors microwaves dielectric properties permittivity conductivity microwave imaging blood pressure |
| title | Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives |
| title_full | Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives |
| title_fullStr | Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives |
| title_full_unstemmed | Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives |
| title_short | Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives |
| title_sort | estimation of physiologic pressures invasive and non invasive techniques ai models and future perspectives |
| topic | microwaves dielectric properties permittivity conductivity microwave imaging blood pressure |
| url | https://www.mdpi.com/1424-8220/23/12/5744 |
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