Exploring the Use of AI-Assisted Video Monitoring to Predict Accidental Events in ICU Patients
Trial ID: NCT07307521
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-01
Completion date: 2028-12-31
Sponsor: Shanghai Zhongshan Hospital
Conditions: Behavioral Monitoring in ICU; Prediction of Accidental Events Using AI-Assisted Video Analysis; ICU Patient Safety and Early Warning System
Interventions: AI-Assisted Video Monitoring
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
This study aims to improve the safety and care of patients in the Intensive Care Unit (ICU) by using artificial intelligence (AI) to analyze video monitoring. ICU patients often face serious risks such as delirium, accidental removal of breathing tubes or lines, and sleep problems. These events can lead to medical emergencies, longer ICU stays, higher costs, and worse outcomes.

To address these challenges, we will place a small video camera above each ICU bed. The camera will record patient movements, body activity, and sleep patterns. At the same time, routine medical monitors will record heart rate, blood oxygen levels, and other vital signs. Noise levels in the room will also be measured. All these data help us understand the patient's behavior and condition more accurately.

The video recording does not involve extra treatment or additional procedures. All data are collected passively and safely. Patient privacy is strictly protected: the system will blur faces or replace them with digital avatars, and any information that could identify the patient or the environment will be masked. All videos are stored securely inside the hospital and are processed only after privacy protection.

Using these recordings, an AI model will be trained to recognize early warning signs of dangerous situations. For example, the system may detect early movements that suggest the patient is becoming agitated, confused, or trying to remove medical tubes. It may also identify severe sleep disturbance that may lead to delirium. If the AI can recognize these early changes, medical staff can intervene sooner and prevent harm.

About 300 patients from Fudan University Zhongshan Hospital will participate. Participation is voluntary. Patients or families will sign an informed consent form before being enrolled. The study has three stages:

Screening - understanding the study and signing consent. Data collection - video and medical monitor data are collected during the ICU stay.

Follow-up - telephone or in-person follow-up at 1 month and 6 months after discharge to evaluate recovery, sleep, mental status, and overall safety.

There are no direct medical risks from participating in this study because it only collects behavioral and monitoring data. The cameras do not interfere with treatment. Privacy and data security are the main considerations, and all measures strictly follow national laws and hospital regulations.

Participants may benefit from earlier identification of dangerous situations, which may help prevent accidental tube removal, severe agitation, or other emergencies. Even if no direct benefit occurs, the information collected may help improve future ICU care by enabling safer and more accurate monitoring systems.

Taking part in the study will not affect the patient's medical care. Patients may withdraw at any time without any consequences or loss of benefits.

This study hopes to build a reliable AI tool that can assist nurses and doctors in recognizing early signs of trouble, improving safety, and enhancing the quality of care for ICU patients.

Eligibility criteria:
Inclusion Criteria:

* Adult or pediatric patients admitted to the Intensive Care Unit (ICU).
* Patient or legally authorized representative is capable of understanding the study information and providing informed consent.
* Patient is expected to remain in the ICU long enough to allow video and physiologic data collection.
* Agreement to participate and allow video monitoring during the ICU stay.

Exclusion Criteria:

* Refusal to participate from the patient or legally authorized representative.
* Patients for whom continuous video monitoring is medically inappropriate or not feasible (e.g., isolation conditions preventing camera installation).
* Patients whose condition or legal status requires special restrictions on video recording (e.g., certain forensic or custodial cases).
* Any situation judged by the clinical team to place the patient at increased privacy or safety risk by participation.
* Withdrawal of consent at any point during the study.

Outcomes:
Accuracy of AI-Assisted Video Monitoring in Predicting Accidental Events