This study aims to evaluate the feasibility and accuracy of a non-contact, camera-based physiological monitoring technology in a perioperative setting (including anesthesia induction, surgery, and recovery).Conventional vital sign monitoring tools-such as ECG leads, blood pressure cuffs, and pulse oximeters-require direct skin contact, which may pose risks of cross-infection or skin injury in vulnerable populations (e.g., newborns or elderly patients). This research utilizes remote Photoplethysmography (rPPG) technology to estimate vital signs, including heart rate, blood pressure, and blood oxygen saturation (SpO2), by analyzing facial video captured via standard camera devices (Logitech C930, iPhone 16 Pro Max, and Samsung Galaxy S24 Ultra).The primary goal is to assess the consistency and stability of this non-contact system compared to clinical gold-standard monitors (Masimo Root, SedLine O3, and Radical-7) during actual surgical procedures. The findings will serve as a foundation for developing non-invasive, supplementary monitoring tools in dynamic clinical environments.

Background and Rationale:

Perioperative vital sign monitoring is crucial for patient safety. However, current standard-of-care tools are predominantly contact-based or invasive. These devices can cause discomfort, limit patient mobility, and potentially damage fragile skin. While various non-contact sensing technologies (e.g., radar, thermal imaging) have been researched, high costs and poor adaptability to dynamic clinical settings have limited their routine use. Our team has previously validated a camera-based rPPG algorithm in clinical environments; this study seeks to further evaluate its feasibility within the rigorous conditions of an operating room (OR).

Study Objectives:

To evaluate the accuracy and consistency of a non-contact video monitoring system in measuring heart rate, blood pressure, and SpO2 across different surgical phases (pre-operative, intra-operative, and post-operative).To optimize algorithms using multi-point clinical data to enhance system robustness in dynamic surgical environments. To explore the potential of rPPG technology as a supplementary or alternative monitoring solution for patients unsuitable for traditional contact-based sensors.

Methodology:

This is a feasibility study conducted in a perioperative clinical environment. Participants will undergo simultaneous monitoring by two systems without interference with routine medical care:

1. Investigational Device (Non-contact): A software module utilizing rPPG technology installed on multiple platforms (Logitech C930 with Windows Laptop, iPhone 16 Pro Max, and Samsung Galaxy S24 Ultra). These devices capture facial video via front-facing cameras to calculate physiological parameters non-invasively.
2. Reference Standard (Clinical Monitors): The Masimo Root monitoring platform, equipped with SedLine O3 regional oximetry and Radical-7 pulse oximetry, will provide the benchmark physiological data.

Data Analysis:

The estimated physiological parameters from the non-contact software will be compared with the synchronized data from the Masimo Root system. Statistical analysis will focus on the correlation and limits of agreement between the two methods to determine the technical feasibility for future clinical translation.

Inclusion Criteria:

1. Patients aged >18 years.
2. Patients scheduled to undergo surgical procedures under general anesthesia.
3. American Society of Anesthesiologists (ASA) Physical Status I, II, or III.

Exclusion Criteria:

1. Patients aged < 18 years.
2. Pregnant patients.
3. Patients whose facial images cannot be captured or recognized (e.g., due to surgical drapes, severe edema, or major trauma).
4. Patients who refuse to participate or have not signed the informed consent form.
5. Other cases deemed unsuitable for the study by the clinical physician or anesthesiologist.

Surgical patients undergoing anesthesia and surgery at Taipei Veterans General Hospital who meet the specified inclusion and exclusion criteria.

Individual participant data will not be shared at this time to protect proprietary algorithmic information and ongoing technical development related to the monitoring software.