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

NCT07491978 | Not Yet Recruiting DMC

• 1 other identifier: 20260319
• Study Type: observational
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

Brief 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. 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. 2.Can the facial scan estimate other health indicators, such as blood sugar, lipid profile, HbA1c, and hemoglobin levels?
3. 3.Is there a relationship between the facial scan results and mental health, such as stress, anxiety, and depression?
4. 4.Answer questionnaires about their mental health and daily habits
5. 5.Have basic health checks, such as blood pressure, heart rate, and body measurements
6. 6.Provide a blood sample for laboratory testing
7. 7.Complete a facial scan using a camera for about 1 to 3 minutes

Conditions

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

Keywords

remote photoplethysmography; rPPG; artificial intelligence; digital biomarker; telemedicine; vital signs; cardiometabolic risk; machine learning; facial scan; screening tool

Outcome Measures

Primary Outcomes (4)

• Agreement of rPPG-Derived Vital Signs With Standardized Clinical Measurements

  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

  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

  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

  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 Outcomes (1)

• Predictive Performance of rPPG-Based Models for Estimation of Organ Function and Body Composition

  At a single study visit during baseline assessment (cross-sectional measurement)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Tarumanagara University: lead

Related Publications (9)

• Tan SYL, Chai JX, Choi M, Javaid U, Tan BPY, Chow BSY, Abdullah HR. Remote Photoplethysmography Technology for Blood Pressure and Hemoglobin Level Assessment in the Preoperative Assessment Setting: Algorithm Development Study. JMIR Form Res. 2025 Jun 6;9:e60455. doi: 10.2196/60455.

  PMID: 40479628 BACKGROUND

• Ahmad Hatib NA, Lee JH, Chong SL, Sng QW, Tan VSR, Ong GY, Lim AM, Quek BH, How MS, Chan JMF, Saffari SE, Ng KC. A two-phased study on the use of remote photoplethysmography (rPPG) in paediatric care. Ann Transl Med. 2024 Jun 10;12(3):46. doi: 10.21037/atm-23-1896. Epub 2024 May 27.

  PMID: 38911566 BACKGROUND

• Allado E, Poussel M, Renno J, Moussu A, Hily O, Temperelli M, Albuisson E, Chenuel B. Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial. J Clin Med. 2022 Jun 24;11(13):3647. doi: 10.3390/jcm11133647.

  PMID: 35806932 BACKGROUND

• Padaki AS, Zarzour AL, Keene KR, Canepa CA, Levin DR, Antonsen EL. Clinical validation of non-contact vital signs in an emergency department setting. Front Med Technol. 2026 Jan 20;7:1728913. doi: 10.3389/fmedt.2025.1728913. eCollection 2025.

  PMID: 41640807 BACKGROUND

• Brown A, Tulkens J, Mattelin M, Sanglet T, Dhuyvetters B. Remote photoplethysmography for health assessment: a review informed by IntelliProve technology. Front Digit Health. 2026 Jan 5;7:1667423. doi: 10.3389/fdgth.2025.1667423. eCollection 2025.

  PMID: 41561164 BACKGROUND

• Heiden E, Jones T, Brogaard Maczka A, Kapoor M, Chauhan M, Wiffen L, Barham H, Holland J, Saxena M, Wegerif S, Brown T, Lomax M, Massey H, Rostami S, Pearce L, Chauhan A. Measurement of Vital Signs Using Lifelight Remote Photoplethysmography: Results of the VISION-D and VISION-V Observational Studies. JMIR Form Res. 2022 Nov 14;6(11):e36340. doi: 10.2196/36340.

  PMID: 36374541 BACKGROUND

• Debnath U, Kim S. A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning. Biomed Eng Online. 2025 Jun 20;24(1):73. doi: 10.1186/s12938-025-01405-5.

  PMID: 40542336 BACKGROUND

• Wiffen L, Brown T, Brogaard Maczka A, Kapoor M, Pearce L, Chauhan M, Chauhan AJ, Saxena M; Lifelight Trials Group. Measurement of Vital Signs by Lifelight Software in Comparison to Standard of Care Multisite Development (VISION-MD): Protocol for an Observational Study. JMIR Res Protoc. 2023 Jan 11;12:e41533. doi: 10.2196/41533.

  PMID: 36630158 BACKGROUND

• Misra G, Wegerif S, Fairlie L, Kapoor M, Fok J, Salt G, Halbert J, Maconochie I, Mullen N. The Measurement of Vital Signs in Pediatric Patients by Lifelight Software in Comparison to the Standard of Care: Protocol for the VISION-Junior Observational Study. JMIR Res Protoc. 2025 Mar 14;14:e58334. doi: 10.2196/58334.

  PMID: 40085833 BACKGROUND

Biospecimen

Retention: SAMPLES WITHOUT DNA

Venous blood samples will be collected from participants for point-of-care testing (POCT) and standardized clinical laboratory analysis. The samples include whole blood, serum, and/or plasma used for measurement of routine clinical parameters such as hemoglobin, blood glucose, HbA1c, and lipid profile. No biospecimens will be retained for long-term storage beyond the duration required for immediate analysis. Additionally, no DNA extraction, genetic analysis, or genomic testing will be performed. All samples will be processed and disposed of according to standard clinical laboratory protocols after analysis.

MeSH Terms

Conditions

Metabolic Syndrome; Hypertension; Diabetes Mellitus; Tachycardia; Anxiety Disorders; Obesity; Overweight

Condition Hierarchy (Ancestors)

Insulin Resistance; Hyperinsulinism; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Vascular Diseases; Cardiovascular Diseases; Endocrine System Diseases; Arrhythmias, Cardiac; Heart Diseases; Cardiac Conduction System Disease; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Mental Disorders; Overnutrition; Nutrition Disorders; Body Weight; Signs and Symptoms

Study Officials

• David Wongso

  DexWellness

  STUDY DIRECTOR

• Putu Tommy Yudha Sumatera Suyasa

  Faculty of Psychology, Universitas Tarumanagara

  STUDY CHAIR

• Meiske Yunithree Suparman

  Faculty of Psychology, Universitas Tarumanagara

  STUDY DIRECTOR

• Ernawati Ernawati

  Faculty of Medicine, Universitas Tarumanagara

  PRINCIPAL INVESTIGATOR

• Sri Tiatri

  Faculty of Psychology, Universitas Tarumanagara

  STUDY CHAIR

• Yohanes Firmansyah

  Faculty of Medicine, Universitas Tarumanagara

  STUDY DIRECTOR

• Alexander Halim Santoso

  Faculty of Medicine, Universitas Tarumanagara

  STUDY DIRECTOR

Central Study Contacts

Ernawati Ernawati, Dr

CONTACT

+6281219308742; ernawati@fk.untar.ac.id

Yohanes Firmansyah, MD

CONTACT

+6281297934375; yohanes@fk.untar.ac.id

Study Design

• Study Type: observational
• Observational Model: ECOLOGIC OR COMMUNITY
• Time Perspective: CROSS SECTIONAL
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 19, 2026
• First Posted: March 25, 2026
• Study Start: April 24, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): March 30, 2027
• Last Updated: April 20, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE
• 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.
• 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.