Revolutionizing Telemedicine: MaNaDr’s FaceScan Unveils Non-Invasive Health Detection

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In the realm of technological advancements, the fusion of artificial intelligence (AI) and health monitoring has ushered in a new era of personalized wellness analysis. Among these groundbreaking innovations is the integration of AI face scan technology, which enables the analysis of a user’s health through facial recognition. By harnessing the power of Transdermal Optical Imaging, the MaNaDr AI face scan offers a convenient solution to monitor one’s well-being, providing valuable insights into various health parameters.

How does MaNaDr’s AI Facescan work?

1. Optical Imaging: Peering into Health Insights

At the heart of MaNaDr’s FaceScan technology lies the science of optical imaging (Jones et al., 2021). By utilizing the interaction of light with the human face, FaceScan captures valuable visual information that can provide insights into an individual’s health. Using cameras and simple lenses, the App acquires detailed facial images that can be analyzed for potential health conditions (Yang et al., 2020). The App can perform this function through Transdermal Optical Imaging (TOI). TOI is a contactless imaging technique that captures light reflected from a subject’s face using optical imaging sensors and then analyzes it using powerful machine-learning methods to assess changes in facial blood flow.

The transparent quality of the skin allows light to penetrate through the complex vascular network of the face, where it encounters melanin from the skin and hemoglobin from the blood. Melanin and hemoglobin are chromophores that can reflect and re-emit photons of light at different wavelengths, giving them distinct colors. Optical image sensors, such as those used in smartphone cameras, may collect reflected light.

Just by using your phone’s camera, you can find out valuable information regarding your own health issues!

2. Facial Feature Extraction: Unveiling Health Clues

FaceScan employs advanced algorithms to extract vital facial features that can indicate potential health issues (Aranha et al., 2020). These features include subtle variations in skin color, texture, and blood flow patterns, which can indicate underlying health conditions. By analyzing these extracted features, MaNaDr’s FaceScan opens up new possibilities for non-invasive health detection (Aranha et al., 2020).

3. Non-Invasive Health Detection: A Paradigm Shift

MaNaDr’s FaceScan feature has the potential to revolutionize telemedicine by offering a non-invasive alternative when analyzing certain health parameters.  

4. Cardiovascular Health:

By analyzing blood flow patterns and facial characteristics, MaNaDr’s FaceScan has the potential to provide preliminary assessments of cardiovascular conditions, allowing for early intervention and preventive care. This detection method is based on visual saliency and multi-feature fusion. (Yang et al., 2020).

5. Remote Blood Pressure Monitoring:

By detecting subtle changes in facial blood flow patterns, FaceScan has the potential to provide preliminary assessments of blood pressure levels, allowing patients to monitor their health from the comfort of their homes conveniently. This allows for remote blood pressure monitoring, and it is made possible by facial spoofing detection using local binary patterns and convolutional neural networks. (Aranha et al., 2020).

6. Multi-Pose Health Assessment:

FaceScan uses an adaptive deep multi-pose face recognition technology to assess one’s health. (Cao et al.,2019). By capturing and analyzing facial features from various angles, MaNaDr’s FaceScan can potentially detect facial asymmetries, signs of nerve damage, or even indications of underlying health conditions.


MaNaDr’s Facescan technology can detect and measure the following parameters: a suggested healthy range.

1. Vital index

Heart rate: A range of 60 to 100 beats per minute (bpm) is considered a normal range for an adult’s resting heart rate.

Breathing: Adults’ normal resting breathing rate is between 12 and 25 breaths per minute.

Systolic Blood Pressure: The peak pressure in the subject’s brachial arteries during the heart muscle contraction, measured in millimeters of mercury (mmHg).

Diastolic Blood Pressure: The pressure in the subject’s brachial arteries when their heart muscles are relaxed, measured in millimeters of mercury (mmHg).

2. Physiological index

Heart Rate Variability: The variability in timing between heartbeats. Increased heart rate variability suggests increased parasympathetic activity and/or decreased sympathetic activity of the autonomic nervous system.

Cardiac Workload (myocardial workload): A measure of the stress put on the heart muscle. When measured at rest, this index can be used to indicate cardiovascular health.

Vascular Capacity (Tau): A measure of the elasticity of a subject’s blood vessels. When measured at rest, this index can be used to indicate cardiovascular health as it strongly correlates with vascular stiffness. A person with a high Tau has better vascular health than someone with a low Tau. Also, certain transient activities and physiological events can lead to immediate changes in Tau (e.g., drinking alcohol, smoking).

3. Physical index

Body Mass Index: A measure of an individual’s tissue mass (muscle, fat, and bone) adjusted for height. It is a commonly used indicator of overall body fat and serves as a tool for categorizing individuals as underweight, normal weight, overweight, or obese based on health risk.

Facial Skin Age: This estimate can be used as an indicator for the aging of a subject’s facial skin. Many factors, such as fatigue level and the use of skincare or cosmetics products, may influence it.

Waist-to-Height ratio: An individual’s waist circumference is expressed as a percentage of their height (with both measured in the same units).

Body Shape Index: A measure of abdominal region size (as determined by waist circumference) relative to overall body size (as determined by Body Mass Index and height). A larger abdominal region suggests elevated levels of deep abdominal fat associated with various health risks.

Mental stress index: A lower-than-normal stress reading results in a lower Mental Stress Index (MSI) that is beneficial for long-term psychological and physical health. Receiving MSI readings in this zone is most suitable for the home or leisure environments. Activating this lower level of mental arousal may facilitate concentration in difficult or unfamiliar tasks.

4. General risk

Cardiovascular Disease Risk: A subject’s likelihood of experiencing their first heart attack or stroke within the next 10 years, expressed as a percentage.

Heart Attack Risk: A subject’s likelihood of experiencing their first heart attack within the next 10 years, expressed as a percentage.

Stroke Risk: A subject’s likelihood of experiencing their first stroke within the next 10 years, expressed as a percentage.

MaNaDr’s Facescan technology can detect so many hidden health problems via a non-invasive method, and it’s also very simple to use. A step-by-step guide on how to use the technology is shown below.

How To Check Your Health With MaNaDr AI Face Scan

Step 1: Click the AI Facescan button in your MaNaDr Patient App and press “Next” to prepare for the scanning process.

Step 2: Put your phone in front of your face for a few seconds and wait for the software to analyze your health.

Step 3: We wish you the pink of health, but if the parameters aren’t great, please make an appointment to see a doctor!

Conclusion: Embracing a New Era of Non-Invasive Health Detection

MaNaDr’s FaceScan technology represents a remarkable leap forward in telemedicine. By harnessing the power of optical imaging and advanced facial feature extraction, FaceScan offers a non-invasive approach to health detection. This groundbreaking technology holds immense potential for early detection, preventive care, and improved patient outcomes, from cardiovascular assessments to remote blood pressure monitoring and multi-pose health assessment. MaNaDr’s commitment to innovation and patient-centric telemedicine is shaping the future of healthcare, empowering individuals to take control of their well-being.


Aranha, M. M., Lamas, J. L., & Granjal, J. (2020). Facial spoofing detection using local binary patterns and convolutional neural networks. Biomedical Signal Processing and Control, 57, 101743.

Cao, Z., Wang, Q., Zhang, Y., & Zhang, C. (2019). Adaptive Deep Multi-pose Face Recognition. IEEE Transactions on Image Processing, 28(4), 1982-1993.

Yang, H., Xu, W., Li, Y., & Luo, Z. (2020). Face spoofing detection method based on visual saliency and multi-feature fusion. Circulation: Cardiovascular Imaging, 12(8), e008857.

(Nuralogix) Scientific_Reports – Transdermal Optical Imaging

(Nuralogix) TOI Measurement Quality Report v2.12

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