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

Study Overview
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Brief Summary
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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

Detailed Description
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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.

Official Title
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Real-time AI-assisted Muscle Ultrasound for Monitoring Muscle Mass Reduction in Intensive Care Unit Patients

Conditions
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Tetanus

Intervention / Treatment
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* Device: Real-time AI-assisted muscle ultrasound


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age ≥16 years Written informed consent Staff and equipment available for ultrasound Admitted to Viet Anh Ward ICU with a diagnosis of meningitis or encephalitis or Ablett Grade 3 or 4 tetanus Within 72 hours of ICU admission Duration of ICU stay expected at least 5 days Exclusion Criteria: Informed consent not given Contraindication to ultrasound scan

Ages Eligible for Study
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Minimum Age: 16 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Supportive Care
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: The developed AI assistant, named RAIMUS, was deployed in real-time using the PRETUS tool. The ultrasound machine HDMI output was connected to the laptop via a USB framegrabber. This allowed the user to use an external screen with an AI overlay instead of the screen of the ultrasound machine. The interface to RAIMUS is as follows. On the right of the screen, there is a widget containing information from the automatic muscle segmentation, including the muscle delineation continuously overlaid onto the ultrasound image and the corresponding cross-sectional area in cm2. The segmentation overlay and related information can be enabled or disabled by the user.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Real-time AI-assisted muscle ultrasound<br>RAIMUS software provides automatic segmentation and size measurement for the RFCSA | Device: Real-time AI-assisted muscle ultrasound<br>* RAIMUS software provides automatic segmentation and size measurement for the RFCSA<br>|
| No Intervention: Manual muscle ultrasound<br>Manual segmentation and size measurement for the RFCSA | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Reproducibility of RFCSA measurements | In this trial, the users are randomly assigned to scan muscle ultrasound with and without AI-assisted software to measure the size of the Rectus Femoris muscle. The investigators will compare the reliability and agreement metrics of the RF measurement | during the study procedure | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Time spent on ultrasound examination | In this trial, the users are randomly assigned to scan muscle ultrasound with and without AI-assisted software to measure the size of the Rectus Femoris muscle. The investigators will record the time needed to carry out the muscle ultrasound examinations | during the study procedure | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Muscle Wasting, Intensive Care Unit, Muscle ultrasound, Artificial Intelligence, Deep Learning