Development and Multicenter Validation of an AI-Based Remote Photoplethysmography (rPPG) Facial Scan for Multimodal Health Assessment

NCT07491978 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2026-04-20

No results posted yet for this study

Summary

The goal of this observational study is to learn if a non-contact facial scan using artificial intelligence (AI) can be used to check health status in adults living in urban areas such as Jakarta. The facial scan uses a method called remote photoplethysmography (rPPG), which measures small changes in blood flow from the face using a camera.

The main questions this study aims to answer are:

1. How close are the results from the facial scan to standard medical measurements, such as heart rate, breathing rate, blood pressure, and oxygen levels?
2. Can the facial scan estimate other health indicators, such as blood sugar, lipid profile, HbA1c, and hemoglobin levels?
3. Is there a relationship between the facial scan results and mental health, such as stress, anxiety, and depression?

Participants will take part in several simple and mostly non-invasive procedures:

1. Answer questionnaires about their mental health and daily habits
2. Have basic health checks, such as blood pressure, heart rate, and body measurements
3. Provide a blood sample for laboratory testing
4. Complete a facial scan using a camera for about 1 to 3 minutes

Researchers will compare the results from the facial scan with standard clinical and laboratory tests to see how well the technology works.

This study may help develop a simple and accessible screening tool that can be used for early detection of health risks. It may also support the use of digital health and telemedicine in community and clinical settings.

Conditions

  • Anxiety
  • Metabolic Syndrome
  • Hypertension
  • Diabetes (DM)
  • Tachycardia
  • ASCVD
  • Depression Disorder
  • Stress (Psychology)
  • Obesity & Overweight
  • Cardiometabolic Risk Factors
  • Cardiometabolic Health Indicators
  • Sleep
  • Wellness

Sponsors & Collaborators

  • Tarumanagara University

    lead OTHER

Principal Investigators

  • David Wongso · DexWellness

  • Putu Tommy Yudha Sumatera Suyasa · Faculty of Psychology, Universitas Tarumanagara

  • Meiske Yunithree Suparman · Faculty of Psychology, Universitas Tarumanagara

  • Ernawati Ernawati · Faculty of Medicine, Universitas Tarumanagara

  • Sri Tiatri · Faculty of Psychology, Universitas Tarumanagara

  • Yohanes Firmansyah · Faculty of Medicine, Universitas Tarumanagara

  • Alexander Halim Santoso · Faculty of Medicine, Universitas Tarumanagara

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-04-24
Primary Completion
2026-12-31
Completion
2027-03-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07491978 on ClinicalTrials.gov