Face video screening technology in a nutshell

• Face videos can be used to detect vital signs and blood biomarkers and assess a person’s health status and risk of developing diseases.
• The underlying technology is called Photoplethysmography (PPG). It leverages RGB light reflection patterns from a person’s face to detect changes in the skin and blood vessels.
• The technology can detect blood pressure, heart rate, heart rate variability, respiratory rate, stress levels, oxygen saturation, and, soon, even cholesterol and sugar levels (glycated hemoglobin). This information can be used to assess cardiovascular health (e.g. arrhythmias risk, stroke risk, hypertension risk, diabetes risk), respiratory health (e.g. sleep apnea), and mental health (e.g. stress and anxiety levels).
• The accuracy varies depending on the biomarker/ disease and the length of the face video. However, the technology showed relevant results, achieving accuracy levels higher than 90%.

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Introduction

Healthcare professionals have always been on the lookout for ways to detect diseases early and prevent them from happening or worsening. Early detection can lead to early intervention and better treatment outcomes. One promising method of early detection is using RGB light reflection from a person's face video. This technology is called Photoplethysmography (PPG). In the realm of non-invasive medical technologies, PPG stands out as a powerful tool that can provide valuable insights into our health. This non-invasive and low-cost method has the potential to revolutionize disease prevention and detection. In this blog post, we will delve into the world of PPG, exploring its principles, its role in disease detection, and the related accuracy levels.

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What is Photoplethysmography (PPG) - the technology behind face video health screenings?

PPG is a non-invasive optical technique that measures blood volume changes in tissues. It operates on the principle that blood absorbs and reflects light differently depending on its concentration and oxygenation level. By emitting light into the skin and detecting the reflected or transmitted light, PPG sensors capture variations in blood volume during each cardiac cycle.

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How does the technology work?

RGB light reflection is a technique that involves shining red, green, and blue light onto a person's face and measuring the amount of light that is reflected. By analyzing the patterns of reflection, machine learning algorithms can detect changes in the skin and blood vessels that may be indicative of certain diseases. For example, changes in the amount of light reflected from the skin can indicate the presence of skin cancer, while changes in the blood vessels can indicate cardiovascular disease.

More in detail, PPG detection process can be broken down into the following steps:

1. Light Emission: PPG begins with the emission of light, usually in the visible or near-infrared spectrum, into the tissue. Light-emitting diodes (LEDs) are commonly used as light sources.
2. Tissue Interaction: The emitted light interacts with the tissue, penetrating the skin and reaching blood vessels beneath. Oxygenated blood absorbs more light than deoxygenated blood, resulting in variations in light intensity.
3. Light Detection: Photodetectors, such as photodiodes or phototransistors, capture the light that is either reflected or transmitted through the tissue. These detectors convert the received light into electrical signals.
4. Signal Processing: The electrical signals from the photodetectors undergo signal processing to extract the pulsatile component corresponding to cardiac activity. This component represents changes in blood volume with each heartbeat.

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Final remarks

It's important to note that while PPG can provide valuable insights into various biomarkers and diseases, it may not be a definitive diagnostic tool. It helps prevent diseases and identify early signs of potential issues, however, in many cases, further clinical assessment and complementary tests may be necessary for a final diagnosis and treatment. PPG serves as a useful adjunctive tool for monitoring and early detection, facilitating timely medical intervention and improving patient outcomes.

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