Digital Early Warning System for Acute Lung Injury in Liver Surgery
This study is developing a computer system that uses artificial intelligence to predict acute lung injury (a serious breathing problem) in patients undergoing major liver surgery. The system will monitor heart and lung interactions to detect warning signs early so doctors can intervene quickly.
Key Objective: The potential benefit is early detection of acute lung injury in liver surgery patients, which could allow doctors to treat the condition before it becomes severe.
Who to Consider: Patients scheduled to undergo major liver surgery who are interested in participating in research to improve early detection of lung complications should consider enrolling.
Trial Parameters
Brief Summary
This study focuses on developing an explainable machine learning model based on cardiopulmonary interaction characteristics to achieve early prediction of acute lung injury (ALI) in patients undergoing major liver surgery. The research will establish a digital early-warning system for ALI to provide support for clinical diagnosis and treatment decisions, thereby reducing the incidence and fatality rate of ALI.
Eligibility Criteria
Inclusion Criteria: * Age ≥ 18 years * Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.) * Voluntary participation with signed informed consent