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

Recruiting

• Conditions: Intensive Care Unit-acquired Weakness; Artifical Intelligence; Ultrasound

• Registration Number: NCT06765551
• Lead Sponsor: Jena University Hospital

Brief Summary

The aim of this observational case-control study is to investigate, whether artificial intelligence can detect ultrasound-derived imaging characteristics typical for intensive care unit-acquired weakness. The main questions it aims to answer are:

1. Is the evaluation of specific parameters of neuromuscular ultrasound using AI-based image analysis suitable for detecting and monitoring critically ill ICU patients with ICUAW?

2. Do the results of AI-based ultrasound image analysis correlate with:

(A) the severity of ICUAW (B) the visual grading of muscle echogenicity (C) the 30- and 90-day-outcome?

Detailed Description

Intensive Care Unit-Acquired Weakness (ICUAW) is one of the most common neuromuscular complications in patients treated in intensive care. With increasing disease severity and especially in analgosedated, ventilated and delirious patients with limited ability to cooperate during the clinical examination, the detection and follow-up of ICUAW is limited to impossible. The clinical diagnosis and severity assessment of ICUAW is usually carried out with the help of established diagnostic methods (e.g. clinical-neurological examination, Medical Research Council-Sum Score, electrophysiological examinations), which, however, cannot be carried out regularly if the patient does not cooperate, thus delaying the diagnosis of ICUAW and making follow-up more difficult. Neuromuscular ultrasound (NMUS), on the other hand, is an easy-to-use, non-invasive examination option that is largely independent of patient compliance and is increasingly being investigated in patients with ICUAW. It was shown that NMUS can detect ICUAW and is helpful in assessing the severity of muscular weakness. However, the standardized recording and follow-up by means of scoring procedures (e.g. the 4-stage Heckmatt Scala) is assessed as partially subjective by the examiner and each individual ultrasound image must be taken with the human eye, taking into account various image parameters. To overcome these diagnostic limitations, artificial intelligence (AI) could be a useful extension or even an alternative.

AI is already being used in a variety of ways in medical diagnostics (e.g. in the detection of tumors and organ assessment), and increasingly also in the analysis of ultrasound images. In this study, the investigators aim to use AI, specifically Convolutional Neural Networks (CNNs), to classify ultrasound images into different categories based on muscle weakness. The main benefit of using AI for such tasks lies in the automation it provides. Once an AI model has been trained on an initial set of images, it can quickly categorize new, unseen images, significantly reducing the time and human effort required for diagnosis. AI models can analyze large amounts of data quickly and consistently, which is especially beneficial in a clinical intensive care setting. By applying AI, this study aims to train the detection and classification of muscle weakness in patients treated in intensive care. However, one challenge with AI models is their "black box" nature, where the decision-making process is not transparent. To solve this problem, the investigators will use explainable AI techniques (XAI) such as Grad-CAM (Gradient-weighted Class Activation Mapping) to visualize the specific areas of the ultrasound images that the AI model focuses on in its analysis. This not only helps validate the AI decisions, but also provides insights into the morphological changes in the muscles that come with different degrees of weakness.

By integrating AI and XAI, the study team aims to not only automate the detection and categorization of muscle weakness, but also improve our understanding of the underlying morphological changes in muscles. This dual approach could lead to more accurate and reliable diagnostics and ultimately improve outcomes for patients in intensive care.

Study Timeline

• Study Start: 2024-10-01
• Study First Submitted: 2024-12-27
• Status Verified: 2025-01
• Study First Submitted QC: 2025-01-03
• Last Update Submitted: 2025-01-03
• Study First Posted: 2025-01-09
• Last Update Posted: 2025-01-09
• Primary Completion: 2025-12-31
• Study Completion: 2026-06-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 50

Inclusion Criteria

• Patients aged 18 years or above
• Major elective surgery, e.g. cardiothoracic or abdominal surgery
• Expected ICU stay >1 day postoperatively
• Healthy, age-machted subjects without ICUAW (recruited from staff of the department of anesthesiology and intensive care medicine)

Exclusion Criteria

• No informed consent
• Emergency surgery
• Previous participation in the same study
• Preexisting neuromuscular disease
• Preexisting central nervous system disease with residual neuromuscular impairment (e.g. cerebral haemorrhage, stroke, brain tumor)
• High-dose glucocorticoid therapy (>300 mg hydrocortisone or equivalent per day) before or during particiation in the study

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Incidence of abnormalities of muscle echogenicity assesed by muscular ultrasound	Day 14	Determination of the number of patients correctly classified as ICUAW (+) using AI-based ultrasound evaluation

Secondary Outcome Measures

Name	Time	Method
Incidence of ICUAW severity assessed by Heckmatt Scale	Day 14
Incidence of ICUAW severity assessed by Medical Research Council Sum Score	Day 14
Days on ventilation	Day 30
Days on vasopressors	Day 30
Number of days in Hospital	Day 30
Number of patients with survival	Day 90
Frailty assessed by FRAIL-Scale	Day 90
Global outcome assessed by Bathel Index	Day 90

Trial Locations

Locations (1)

Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital; 🇩🇪 Jena, Thüringen, Germany