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

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

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

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.

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.

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