Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives.

Journal: Sensors (Basel, Switzerland)
PMID:

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.

Authors

  • 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
    Mayo Clinic Rochester, MN.
  • 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
    Department of Nuclear Medicine, Medstar Washington Hospital Center, Washington, District of Columbia.
  • Shivaram P Arunachalam
    Mayo Clinic Rochester, MN.