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Clinical Trial Summary

Participants (patients and volunteers) will be recruited to have their blood pressure measured by standard blood pressure assessment methods while having their face video recorded. The data collected will help improve the blood pressure measurement accuracy of Transdermal Optical Imaging, which relies on machine learning to extract physiological information from videos recorded.


Clinical Trial Description

There are many ways to measure blood pressure (monitors, mercury sphygmomanometer, aneroid devices), with most relying on a cuff-inflation. Transdermal Optical Imaging measures blood pressure using a video captured by any conventional camera (e.g., those on a phone, tablet, laptop) and advanced machine learning algorithms.

The current study aims to improve the accuracy of Transdermal Optical Imaging algorithms for measuring blood pressure. The investigators will recruit participants (patients with medical problems and healthy volunteers) to have their blood pressure measured in various ways (e.g.,by registered nurses with sphygmomanometer and stethoscope, continuous blood pressure monitor, etc.). Further, participants will have their faces video-recorded intermittently between standard measurements or at the same time as standard measures. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT04539860
Study type Observational
Source University of Toronto
Contact Kang Lee, PhD
Phone 416-934-4597
Email kang.lee@utoronto.ca
Status Recruiting
Phase
Start date August 25, 2020
Completion date August 2021

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