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.
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.
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.
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.
Ecologic or community
Cross-sectional
Not listed
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.
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.
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.
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.
Predictive Performance of rPPG-Based Models for Estimation of Organ Function and Body Composition
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.
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