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

April 15, 2026 updated by: Yohanes Firmansyah, dr, MH, MM, Tarumanagara University

Development and Multicenter Validation of an AI-Based Remote Photoplethysmography (rPPG) Facial Scan for Multimodal Health Assessment: Agreement With Clinical, Laboratory, and Psychological Parameters in an Urban Population

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.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

Remote photoplethysmography (rPPG) is an emerging non-contact optical technology that enables extraction of physiological signals from facial video using standard cameras. This approach has gained increasing attention in telemedicine due to its scalability, cost-effectiveness, and ability to perform remote health screening. Recent advancements in artificial intelligence (AI) have further expanded the potential of rPPG beyond basic vital sign monitoring to include estimation of cardiometabolic biomarkers and health risk indices. However, comprehensive validation of rPPG-based systems against standardized clinical measurements, laboratory biomarkers, and psychological parameters remains limited, particularly in low- and middle-income settings such as Indonesia. Given the high burden of cardiometabolic diseases in urban populations like Jakarta, evaluating the accuracy and feasibility of AI-based facial scanning technologies is essential to support early detection and digital health integration.

Specific Objectives

  1. To assess the agreement between rPPG derived vital signs (heart rate, respiratory rate, blood pressure, SpO₂) and corresponding measurements obtained from standardized physical examination by trained personnel and validated medical devices
  2. To determine the degree of concordance between rPPG based estimates and laboratory values of hemoglobin, blood glucose, HbA1c, LDL, HDL, triglycerides, and total cholesterol.
  3. To analyze the association between rPPG derived physiological parameters and levels of depression, anxiety, and stress as measured by the DASS 21 questionnaire.
  4. To calculate mean arterial pressure (MAP), ASCVD risk scores, and heart age from rPPG outputs and to compare these indices with those derived from standard clinical and laboratory data.
  5. To develop and preliminarily evaluate exploratory algorithms using rPPG video data to estimate kidney function, liver function, muscle mass, visceral fat, body weight, body height, body mass index, and subcutaneous fat as potential screening parameters.

Methods This study will employ a multicenter observational design conducted across selected subdistricts in Jakarta and expanded to the Jabodetabek region. Adult participants will undergo comprehensive assessment including psychological questionnaires (DASS, PHQ, GAD), anthropometric measurements, body composition analysis, spirometry, muscle strength testing, and venous blood sampling. Blood samples will be analyzed using POCT (≤30 minutes) and ISO-standardized clinical laboratory methods. In parallel, participants will undergo a non-contact facial scan, generating rPPG-based outputs including vital signs, hemodynamic indices, and AI-estimated biomarkers. Statistical analysis will include Bland-Altman agreement analysis, Cohen's kappa for categorical variables, correlation analysis, and machine learning performance metrics (MAE, MSE, RMSE, R²).

Expected Results It is expected that rPPG-based measurements will demonstrate good agreement with standard clinical measurements for core vital signs (heart rate, respiratory rate, SpO₂), with moderate agreement for blood pressure and selected biomarkers. AI-based models are anticipated to show acceptable predictive performance for certain metabolic parameters and exploratory variables, supporting the feasibility of rPPG as a screening tool. The study is also expected to identify key confounding factors, such as skin tone and demographic variability, influencing signal accuracy.

Study Type

Observational

Enrollment (Estimated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

The study population consists of adult individuals aged 18 years and above residing in urban areas of Jakarta and the greater metropolitan region (Jabodetabek). Participants will be recruited through a multicenter community-based approach, including primary care facilities, community health posts, campuses, and workplace settings. The population is designed to reflect real-world heterogeneity, including variations in age, sex, ethnicity, and skin tone, as well as a spectrum of cardiometabolic conditions such as hypertension, diabetes mellitus, and dyslipidemia. Both healthy individuals and those with existing metabolic risk factors will be included to ensure broad applicability of findings in an urban population setting.

Description

Inclusion Criteria:

  1. Adults aged ≥18 years.
  2. Able and willing to provide written informed consent.
  3. Able to comply with study procedures, including face scan, physical examination, blood sampling, and questionnaire completion.
  4. Clinically stable at the time of assessment.

Exclusion Criteria:

  1. Facial conditions affecting the region of interest (ROI), such as injury, deformity, or impaired circulation, that may interfere with rPPG signal acquisition.
  2. Presence of facial tattoos or coverings that obstruct optical signal detection.
  3. Inability to remain still or comply with measurement procedures during data acquisition.
  4. Severe medical conditions that preclude safe participation, as judged by the investigator.

  5. Incomplete data or withdrawal of consent during the study.

    -

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Agreement of rPPG-Derived Vital Signs With Standardized Clinical Measurements
Time Frame: At a single study visit during baseline assessment (cross-sectional measurement)
The primary outcome is the level of agreement between vital signs obtained from the artificial intelligence-based remote photoplethysmography (rPPG) facial scan and corresponding reference measurements obtained through standardized physical examination and validated medical devices. The vital signs assessed include heart rate, respiratory rate, blood pressure, and oxygen saturation (SpO₂). Agreement will be evaluated using paired comparisons between index and reference methods, primarily through Bland-Altman analysis, including mean difference (bias) and limits of agreement. This outcome is intended to determine the clinical validity of AI as a non-contact screening tool for core physiological parameters in adults.
At a single study visit during baseline assessment (cross-sectional measurement)
Concordance Between rPPG-Derived Biomarker Estimates and Standard Laboratory Measurements
Time Frame: At a single study visit during baseline assessment (cross-sectional measurement)
The outcome measures the degree of concordance between biomarker estimates derived from remote photoplethysmography (rPPG)-based analysis and corresponding reference values obtained from standardized point-of-care testing and clinical laboratory methods. Biomarkers assessed include hemoglobin, blood glucose, glycated hemoglobin (HbA1c), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, and total cholesterol. Concordance will be evaluated using correlation analysis and agreement statistics, including Bland-Altman analysis and appropriate regression-based performance metrics.
At a single study visit during baseline assessment (cross-sectional measurement)
Association Between rPPG-Derived Physiological Parameters and Psychological Status
Time Frame: At a single study visit during baseline assessment (cross-sectional measurement)
The outcome measures the association between physiological parameters derived from remote photoplethysmography (rPPG) and psychological status assessed using the DASS-21, PHQ and GAD. Physiological parameters include heart rate, respiratory rate, heart rate variability, and other autonomic-related indices. Psychological outcomes include depression, anxiety, and stress scores. The relationship will be analyzed using correlation and regression analyses to evaluate the extent to which rPPG-derived signals reflect mental health status.
At a single study visit during baseline assessment (cross-sectional measurement)
Agreement of rPPG-Derived Cardiovascular Risk Indices With Standard Clinical Calculations
Time Frame: At a single study visit during baseline assessment (cross-sectional measurement)
The outcome measures the level of agreement between cardiovascular risk indices derived from remote photoplethysmography (rPPG)-based parameters and those calculated using standard clinical and laboratory data. The indices include mean arterial pressure (MAP), atherosclerotic cardiovascular disease (ASCVD) risk score, and heart age. Agreement will be evaluated using Bland-Altman analysis, correlation coefficients, and classification concordance where applicable, to determine the reliability of rPPG-based estimations in reflecting established cardiovascular risk assessments.
At a single study visit during baseline assessment (cross-sectional measurement)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predictive Performance of rPPG-Based Models for Estimation of Organ Function and Body Composition
Time Frame: At a single study visit during baseline assessment (cross-sectional measurement)
The outcome measures the predictive performance of models derived from remote photoplethysmography (rPPG)-based data in estimating physiological and body composition parameters. These include kidney function, liver function, muscle mass, visceral fat, subcutaneous fat, body weight, body height, and body mass index (BMI). Model performance will be evaluated against reference standards obtained from clinical laboratory measurements and validated assessment tools using regression-based metrics such as mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and coefficient of determination (R²). This outcome is exploratory and aims to assess the feasibility of rPPG as a screening approach for broader health parameters.
At a single study visit during baseline assessment (cross-sectional measurement)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Tarumanagara University

Investigators

  • Study Director: David Wongso, DexWellness
  • Study Chair: Putu Tommy Yudha Sumatera Suyasa, Faculty of Psychology, Universitas Tarumanagara
  • Study Director: Meiske Yunithree Suparman, Faculty of Psychology, Universitas Tarumanagara
  • Principal Investigator: Ernawati Ernawati, Faculty of Medicine, Universitas Tarumanagara
  • Study Chair: Sri Tiatri, Faculty of Psychology, Universitas Tarumanagara
  • Study Director: Yohanes Firmansyah, Faculty of Medicine, Universitas Tarumanagara
  • Study Director: Alexander Halim Santoso, Faculty of Medicine, Universitas Tarumanagara

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

April 24, 2026

Primary Completion (Estimated)

December 31, 2026

Study Completion (Estimated)

March 30, 2027

Study Registration Dates

First Submitted

March 19, 2026

First Submitted That Met QC Criteria

March 19, 2026

First Posted (Actual)

March 25, 2026

Study Record Updates

Last Update Posted (Actual)

April 20, 2026

Last Update Submitted That Met QC Criteria

April 15, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 20260319

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

De-identified individual participant data (IPD) underlying the results reported in this study will be made available to qualified researchers upon reasonable request. Shared data will include key variables derived from clinical assessments, laboratory results, and processed outputs from non-contact physiological measurements. All data will be fully anonymized to ensure participant confidentiality, and no identifiable information, including facial images or raw video data, will be shared. Access will be granted after approval of a methodologically sound research proposal and, where applicable, ethical clearance. Data sharing will be conducted in accordance with institutional policies and applicable data protection regulations.

IPD Sharing Time Frame

De-identified individual participant data (IPD) will be available beginning 6 months after publication of the primary study results and will remain available for a period of 5 years following publication. Supporting documents, including the study protocol and statistical analysis plan, will be made available within the same timeframe.

IPD Sharing Access Criteria

Access to IPD will be granted to qualified researchers with a methodologically sound research proposal. Requests must include a clear scientific rationale and, where applicable, evidence of ethical approval. Data will be limited to de-identified datasets, study protocol, and statistical analysis plan. No identifiable information or raw facial/video data will be shared. Access will be provided upon approval by the principal investigator and institutional authority, and may require a data use agreement to ensure compliance with data protection and confidentiality standards.

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP
  • ICF
  • ANALYTIC_CODE
  • CSR

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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