Real-time Artificial Intelligent (AI)-Assisted Muscle Ultrasound for Monitoring Muscle Mass Reduction in ICU Patients

Overview

This study aims to investigate the feasibility of using a real-time artificial intelligent (AI)-assisted tool for Rectus Femoris cross sectional area measurement from muscle ultrasound to improve reliability, reduce inter- and intra-observer variability and reduce operator time spent on ultrasound examination

This project proposes to develop computational methods to automatically analyze conventional 2D muscle ultrasound images in real time to assist operators circumvent achieve high quality reproducible views and measurements specifically for Rectus Femoris muscle. Study design: This is a prospective observational study to test the reliability of AI-assisted muscle ultrasound at the patient's bedside compared to standard RFCSA ultrasound. All measurements will be performed in adult patients with severe tetanus (Ablett Grade 3 or 4) admitted to the Adult ICU at HTD expected to stay at least 5 days. All patients are on mechanical ventilation, muscle relaxation and neuromuscular blockers following the Ministry of Health guidelines. Study procedures: Three ultrasound examinations will be carried out according to a standard operating procedure where patients are in the supine position with the leg in neutral rotation. Measurements will be taken using 12L-RS linear probe, Venue Go ultrasound machine (General Electric Healthcare, London, UK). Statistical analysis: Study will compare the intra- and interobserver variability of measurements and examination duration. All statistical analysis was performed with R version 4.0.4.

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Eligibility

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Outcomes