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/NCT05981651</url>
    </required_header>
    <id_info>
      <org_study_id>PekingUMCH record I-22PJ1121</org_study_id>
      <nct_id>NCT05981651</nct_id>
    </id_info>
    <brief_title>Development and Application of a Dynamic Three-dimensional Quantitative Facial Measurement Device</brief_title>
    <official_title>Development and Application of a Dynamic Three-dimensional Quantitative Facial Measurement Device</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Peking Union Medical College Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Peking Union Medical College Hospital</source>
    <oversight_info>
      <has_dmc>No</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>
        The aim of this project is to successfully develop and industrialise the &quot;Facial Movement 3D&#xD;
        Dynamic Quantitative Measurement Device&quot;, which is a commercial device that can provide&#xD;
        dynamic indicators of facial movement, and can practically solve the evaluation problems of&#xD;
        facial paralysis for doctors and patients, and has important clinical value and social&#xD;
        benefits.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Based on the current optimal high-precision face key point technology, the effective&#xD;
        high-precision automatic face recognition model conforming to the Asian face is established&#xD;
        through specialised training with targeted marker data, time series training optimisation&#xD;
        based on optical flow, and local image tracking technology. The 3D reconstruction technique&#xD;
        is then used to reconstruct the 3D spatial coordinates of the face keypoints, so as to&#xD;
        realise the multi-camera 3D tracking of the face keypoints. The displacement of the key&#xD;
        points is used to respond to the facial muscle activity characteristics of facial paralysis&#xD;
        patients.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">July 1, 2022</start_date>
    <completion_date type="Anticipated">December 30, 2024</completion_date>
    <primary_completion_date type="Anticipated">December 30, 2024</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Facial feature point displacement distance</measure>
      <time_frame>3 months</time_frame>
      <description>Displacement of corresponding facial feature points when volunteers perform specific actions such as specific expressions (raising eyebrow, closing eyes, shrugging nose, smiling and whistling)</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Facial Feature Point Velocity</measure>
      <time_frame>3 months</time_frame>
      <description>Velocity of the corresponding facial feature points of volunteers when accomplishing specific actions such as specific expressions (raising eyebrows, closing eyes, shrugging nose, smiling, whistling)</description>
    </primary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">200</enrollment>
    <condition>Facial Palsy</condition>
    <arm_group>
      <arm_group_label>training session</arm_group_label>
      <description>A total of 150 facial expression videos of healthy volunteers and facial palsy patients were collected as a training set for the algorithm</description>
    </arm_group>
    <arm_group>
      <arm_group_label>test set</arm_group_label>
      <description>A total of 50 cases of healthy volunteers and patients with facial palsy were used as the test set for the algorithm</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>non-intervention</intervention_name>
      <description>non-intervention</description>
      <arm_group_label>test set</arm_group_label>
      <arm_group_label>training session</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Based on previous face data collection often use larger public datasets, such as the AFLW&#xD;
          dataset with 20,000 images. we need the same order of magnitude of local ethnographic data&#xD;
          for migration learning. At the same time, the data in the dataset is improved according to&#xD;
          the need to improve the accuracy of the labelled data at key points. Take about 100&#xD;
          effective frames for each data set. Need to complete about 200 people of various types of&#xD;
          face data acquisition. The test group data is generally designed to be more than 20% of the&#xD;
          training data, so the number of test group samples is designed to be 50 groups.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. Older than 14 years&#xD;
  &#xD;
            2. No history of facial surgery, facial trauma, or scars obscuring the facial features.&#xD;
  &#xD;
            3. Able to perform relevant actions according to instructions&#xD;
  &#xD;
            4. Willingness to participate in the study&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            1. Unwillingness to participate in the study&#xD;
  &#xD;
            2. Unable to co-operate with relevant command actions&#xD;
  &#xD;
            3. Obvious trauma, scars, etc. obscuring the facial features&#xD;
  &#xD;
            4. Less than 14 years of age&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>14 Years</minimum_age>
      <maximum_age>70 Years</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Guodong Feng</last_name>
      <role>Principal Investigator</role>
      <affiliation>Peking Union Medical College Hospital</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Guodong Feng</last_name>
      <phone>15001275266</phone>
      <phone_ext>010</phone_ext>
      <email>fengguodong2013@163.com</email>
    </overall_contact>
    <location>
      <facility>
        <name>Peking Union Medical College Hospital</name>
        <address>
          <city>Beijing</city>
          <state>Beijing</state>
          <zip>100730</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Guodong Feng</last_name>
        <phone>15001275266</phone>
        <phone_ext>010</phone_ext>
        <email>fengguodong2013@163.com</email>
      </contact>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>July 27, 2023</study_first_submitted>
    <study_first_submitted_qc>August 7, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 8, 2023</study_first_posted>
    <last_update_submitted>August 7, 2023</last_update_submitted>
    <last_update_submitted_qc>August 7, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 8, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Peking Union Medical College Hospital</investigator_affiliation>
      <investigator_full_name>Guodong Feng</investigator_full_name>
      <investigator_title>chief physician</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Facial Paralysis</mesh_term>
      <mesh_term>Facies</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/NCT05981651
  org study id: PekingUMCH record I-22PJ1121
  nct id: NCT05981651
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The aim of this project is to successfully develop and industrialise the "Facial Movement 3D
        Dynamic Quantitative Measurement Device", which is a commercial device that can provide
        dynamic indicators of facial movement, and can practically solve the evaluation problems of
        facial paralysis for doctors and patients, and has important clinical value and social
        benefits.
  Based on the current optimal high-precision face key point technology, the effective
        high-precision automatic face recognition model conforming to the Asian face is established
        through specialised training with targeted marker data, time series training optimisation
        based on optical flow, and local image tracking technology. The 3D reconstruction technique
        is then used to reconstruct the 3D spatial coordinates of the face keypoints, so as to
        realise the multi-camera 3D tracking of the face keypoints. The displacement of the key
        points is used to respond to the facial muscle activity characteristics of facial paralysis
        patients.
  observational model: Cohort
  time perspective: Prospective
  measure: Facial feature point displacement distance
  time frame: 3 months
  description: Displacement of corresponding facial feature points when volunteers perform specific actions such as specific expressions (raising eyebrow, closing eyes, shrugging nose, smiling and whistling)
  measure: Facial Feature Point Velocity
  time frame: 3 months
  description: Velocity of the corresponding facial feature points of volunteers when accomplishing specific actions such as specific expressions (raising eyebrows, closing eyes, shrugging nose, smiling, whistling)
  arm group label: training session
  description: A total of 150 facial expression videos of healthy volunteers and facial palsy patients were collected as a training set for the algorithm
  arm group label: test set
  description: A total of 50 cases of healthy volunteers and patients with facial palsy were used as the test set for the algorithm
  intervention type: Other
  intervention name: non-intervention
  description: non-intervention
  arm group label: test set
  arm group label: training session
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 14 Years
  maximum age: 70 Years
  healthy volunteers: Accepts Healthy Volunteers
  last name: Guodong Feng
  role: Principal Investigator
  affiliation: Peking Union Medical College Hospital
  last name: Guodong Feng
  phone: 15001275266
  phone ext: 010
  email: fengguodong2013@163.com
  facility: 
  status: Recruiting
  contact: 
  country: China
  responsible party type: Principal Investigator
  investigator affiliation: Peking Union Medical College Hospital
  investigator full name: Guodong Feng
  investigator title: chief physician
  mesh term: Facial Paralysis
  mesh term: Facies
  sharing ipd: No