xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT06034093</url>
    </required_header>
    <id_info>
      <org_study_id>01NVb</org_study_id>
      <secondary_id>OxTREC 516-20</secondary_id>
      <nct_id>NCT06034093</nct_id>
    </id_info>
    <brief_title>Real-time Artificial Intelligent (AI)-Assisted Muscle Ultrasound for Monitoring Muscle Mass Reduction in ICU Patients</brief_title>
    <acronym>RAIMUS</acronym>
    <official_title>Real-time AI-assisted Muscle Ultrasound for Monitoring Muscle Mass Reduction in Intensive Care Unit Patients</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Oxford University Clinical Research Unit, Vietnam</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>King's College London</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Oxford University Clinical Research Unit, Vietnam</source>
    <oversight_info>
      <has_dmc>Yes</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        This study aims to investigate the feasibility of using a real-time artificial intelligent&#xD;
        (AI)-assisted tool for Rectus Femoris cross sectional area measurement from muscle ultrasound&#xD;
        to improve reliability, reduce inter- and intra-observer variability and reduce operator time&#xD;
        spent on ultrasound examination&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        This project proposes to develop computational methods to automatically analyze conventional&#xD;
        2D muscle ultrasound images in real time to assist operators circumvent achieve high quality&#xD;
        reproducible views and measurements specifically for Rectus Femoris muscle.&#xD;
  &#xD;
        Study design: This is a prospective observational study to test the reliability of&#xD;
        AI-assisted muscle ultrasound at the patient's bedside compared to standard RFCSA ultrasound.&#xD;
        All measurements will be performed in adult patients with severe tetanus (Ablett Grade 3 or&#xD;
        4) admitted to the Adult ICU at HTD expected to stay at least 5 days. All patients are on&#xD;
        mechanical ventilation, muscle relaxation and neuromuscular blockers following the Ministry&#xD;
        of Health guidelines.&#xD;
  &#xD;
        Study procedures: Three ultrasound examinations will be carried out according to a standard&#xD;
        operating procedure where patients are in the supine position with the leg in neutral&#xD;
        rotation. Measurements will be taken using 12L-RS linear probe, Venue Go ultrasound machine&#xD;
        (General Electric Healthcare, London, UK).&#xD;
  &#xD;
        Statistical analysis: Study will compare the intra- and interobserver variability of&#xD;
        measurements and examination duration. All statistical analysis was performed with R version&#xD;
        4.0.4.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">June 1, 2020</start_date>
    <completion_date type="Anticipated">December 31, 2023</completion_date>
    <primary_completion_date type="Anticipated">December 31, 2023</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>Randomized</allocation>
      <intervention_model>Parallel Assignment</intervention_model>
      <intervention_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.&#xD;
  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.</intervention_model_description>
      <primary_purpose>Supportive Care</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Reproducibility of RFCSA measurements</measure>
      <time_frame>during the study procedure</time_frame>
      <description>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</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Time spent on ultrasound examination</measure>
      <time_frame>during the study procedure</time_frame>
      <description>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</description>
    </secondary_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Anticipated">20</enrollment>
    <condition>Tetanus</condition>
    <arm_group>
      <arm_group_label>Real-time AI-assisted muscle ultrasound</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>RAIMUS software provides automatic segmentation and size measurement for the RFCSA</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Manual muscle ultrasound</arm_group_label>
      <arm_group_type>No Intervention</arm_group_type>
      <description>Manual segmentation and size measurement for the RFCSA</description>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>Real-time AI-assisted muscle ultrasound</intervention_name>
      <description>RAIMUS software provides automatic segmentation and size measurement for the RFCSA</description>
      <arm_group_label>Real-time AI-assisted muscle ultrasound</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Age ≥16 years&#xD;
  &#xD;
            -  Written informed consent&#xD;
  &#xD;
            -  Staff and equipment available for ultrasound&#xD;
  &#xD;
            -  Admitted to Viet Anh Ward ICU with a diagnosis of meningitis or encephalitis or Ablett&#xD;
               Grade 3 or 4 tetanus&#xD;
  &#xD;
            -  Within 72 hours of ICU admission&#xD;
  &#xD;
            -  Duration of ICU stay expected at least 5 days&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Informed consent not given&#xD;
  &#xD;
            -  Contraindication to ultrasound scan&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>16 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Sophie Yacoub, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Oxford University Clinical Research Unit</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Nhat TH Phung, BSc</last_name>
      <phone>+84347900911</phone>
      <email>nhatpth@oucru.org</email>
    </overall_contact>
    <location>
      <facility>
        <name>Hospital for Tropical Diseases at Ho Chi Minh city</name>
        <address>
          <city>Ho Chi Minh City</city>
          <zip>700000</zip>
          <country>Vietnam</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Nhat</last_name>
      </contact>
      <investigator>
        <last_name>Nhat TH Phung, BSc</last_name>
        <role>Sub-Investigator</role>
      </investigator>
      <investigator>
        <last_name>Louise Thwaites, PhD</last_name>
        <role>Sub-Investigator</role>
      </investigator>
      <investigator>
        <last_name>Sophie Yacoub, PhD</last_name>
        <role>Principal Investigator</role>
      </investigator>
      <investigator>
        <last_name>Hao V Nguyen, PhD</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location_countries>
      <country>Vietnam</country>
    </location_countries>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 30, 2023</study_first_submitted>
    <study_first_submitted_qc>September 10, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 13, 2023</study_first_posted>
    <last_update_submitted>September 10, 2023</last_update_submitted>
    <last_update_submitted_qc>September 10, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 13, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Muscle Wasting</keyword>
    <keyword>Intensive Care Unit</keyword>
    <keyword>Muscle ultrasound</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Deep Learning</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Tetanus</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT06034093
  org study id: 01NVb
  secondary id: OxTREC 516-20
  nct id: NCT06034093
  lead sponsor: 
  collaborator: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  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.
  allocation: Randomized
  intervention model: Parallel Assignment
  intervention 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.
  primary purpose: Supportive Care
  masking: None (Open Label)
  measure: Reproducibility of RFCSA measurements
  time frame: during the study procedure
  description: 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
  measure: Time spent on ultrasound examination
  time frame: during the study procedure
  description: 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
  arm group label: Real-time AI-assisted muscle ultrasound
  arm group type: Experimental
  description: RAIMUS software provides automatic segmentation and size measurement for the RFCSA
  arm group label: Manual muscle ultrasound
  arm group type: No Intervention
  description: Manual segmentation and size measurement for the RFCSA
  intervention type: Device
  intervention name: Real-time AI-assisted muscle ultrasound
  description: RAIMUS software provides automatic segmentation and size measurement for the RFCSA
  arm group label: Real-time AI-assisted muscle ultrasound
  criteria: 
  gender: All
  minimum age: 16 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Sophie Yacoub, PhD
  role: Principal Investigator
  affiliation: Oxford University Clinical Research Unit
  last name: Nhat TH Phung, BSc
  phone: +84347900911
  email: nhatpth@oucru.org
  facility: 
  status: Recruiting
  contact: 
  investigator: 
  investigator: 
  investigator: 
  investigator: 
  country: Vietnam
  responsible party type: Sponsor
  mesh term: Tetanus
  sharing ipd: No