Using Artificial Intelligence to Analyze Muscular Ultrasound for Detecting Weakness in Intensive Care Patients

What this Trial Is About

This research aims to investigate whether artificial intelligence AI can detect imaging features typical of Intensive Care Unit-acquired Weakness ICUAW using neuromuscular ultrasound. The study focuses on evaluating if AI-based image analysis can identify and monitor ICU patients with ICUAW and whether these AI results correlate with muscle weakness severity, visual muscle echogenicity grading, and 30- and 90-day patient outcomes. ICUAW is a common neuromuscular complication in critically ill patients, often difficult to assess due to patient sedation and limited cooperation during clinical exams. Participants will undergo non-invasive neuromuscular ultrasound of peripheral muscles in the upper and lower limbs. The ultrasound images will be processed using AI, specifically Convolutional Neural Networks, to classify muscle weakness severity. Explainable AI techniques will also be used to highlight the areas within the ultrasound images that contribute to the AIs decisions, helping to understand muscle changes. The study includes groups of critically ill patients with and without ICUAW as well as healthy controls. During the study, researchers will assess muscle echogenicity abnormalities by ultrasound on Day 14 and measure ICUAW severity through various scales. Additional outcomes such as ventilation duration, hospital stay length, survival, frailty, and overall recovery will be evaluated at 30 and 90 days. Data collection involves clinical examinations, scoring systems, and AI image analysis to improve diagnosis and monitoring of muscle weakness in ICU patients.

AI Based Muscular Ultrasound to Assess Intensive Care Unit-acquired Weakness