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/NCT06002412</url>
    </required_header>
    <id_info>
      <org_study_id>CASMI005</org_study_id>
      <nct_id>NCT06002412</nct_id>
    </id_info>
    <brief_title>Quality Control of Ultrasound Images During Early Pregnancy Via AI</brief_title>
    <official_title>Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Chinese Academy of Sciences</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Beijing Obstetrics and Gynecology Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Peking University Third Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Changsha Hospital for Maternal and Child Health Care</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Second Xiangya Hospital of Central South University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Chinese Academy of Sciences</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 research integrates artificial intelligence to enhance early pregnancy ultrasonography&#xD;
        quality control, focusing on specific fetal sections. In collaboration with prominent medical&#xD;
        institutions, the investigators have amassed extensive fetal ultrasound data. The&#xD;
        investigators aim to develop a deep learning model that can accurately identify essential&#xD;
        anatomical areas in ultrasound images and evaluate their quality. This tool is expected to&#xD;
        significantly decrease misdiagnoses of conditions like Down Syndrome and neural system&#xD;
        deformities by ensuring real-time image quality assessment.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        This research is dedicated to integrating artificial intelligence technology to optimize the&#xD;
        quality control process of early pregnancy ultrasonography. The ultrasound images involved&#xD;
        primarily focus on the median sagittal section, NT section, and choroid plexus of the fetus&#xD;
        during early pregnancy. In this regard, the investigators have collaborated with renowned&#xD;
        medical institutions such as Beijing Obstetrics and Gynecology Hospital, Peking University&#xD;
        Third Hospital, Changsha Hospital for Maternal and Child Health Care, and Second Xiangya&#xD;
        Hospital of Central South University to retrospectively and prospectively collect a vast&#xD;
        amount of early pregnancy fetal ultrasound image data. Based on this, the investigators plan&#xD;
        to establish a model rooted in deep learning. This model will be capable of precisely&#xD;
        identifying key anatomical regions in standard ultrasound scan images. Furthermore, by&#xD;
        recognizing these anatomical structures, the model will determine whether the ultrasound&#xD;
        image meets the standard scanning quality. This model is anticipated to serve as a powerful&#xD;
        auxiliary tool in obstetric ultrasonography, enabling real-time assessment of ultrasound&#xD;
        image quality, thereby significantly reducing the rates of missed and misdiagnosed fetal&#xD;
        diseases such as Down Syndrome and neural system malformations.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">September 1, 2023</start_date>
    <completion_date type="Anticipated">July 30, 2028</completion_date>
    <primary_completion_date type="Anticipated">December 31, 2023</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>PR curve of image quality control module</measure>
      <time_frame>one month</time_frame>
      <description>Using Precision-Recall curve and mean average percision as evaluating indicator of image quality control model.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>The accuracy of intelligent analysis system in image quality control module</measure>
      <time_frame>one month</time_frame>
      <description>The agreement between the prediction outcome of intelligent analysis system and the golden standard</description>
    </secondary_outcome>
    <number_of_groups>4</number_of_groups>
    <enrollment type="Anticipated">400</enrollment>
    <condition>Early Pregnancy</condition>
    <arm_group>
      <arm_group_label>Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University</arm_group_label>
      <description>Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Peking University Third Hospital</arm_group_label>
      <description>Peking University Third Hospital collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Changsha Hospital for Maternal and Child Health Care</arm_group_label>
      <description>Changsha Hospital for Maternal and Child Health Care collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Second Xiangya Hospital of Central South University</arm_group_label>
      <description>Second Xiangya Hospital of Central South University collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Image quality control</intervention_name>
      <description>The investigators identify the region of interest in the relevant section to give a conclusion on whether the image is standard or not, guiding clinicians to standardize the operation, and reducing the rate of misdiagnosis and underdiagnosis.</description>
      <arm_group_label>Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University</arm_group_label>
      <arm_group_label>Changsha Hospital for Maternal and Child Health Care</arm_group_label>
      <arm_group_label>Peking University Third Hospital</arm_group_label>
      <arm_group_label>Second Xiangya Hospital of Central South University</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Women in early pregnancy&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Women in early pregnancy who have detailed personal information and ultrasound images.&#xD;
  &#xD;
            -  The ultrasound images should clearly show the fetus's median sagittal, NT, and choroid&#xD;
               plexus views.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Ultrasound images from women in mid to late pregnancy.&#xD;
  &#xD;
            -  Ultrasound images that are unclear or blurry, making evaluation difficult.&#xD;
  &#xD;
            -  Women who did not provide complete personal and medical information during the&#xD;
               ultrasound scan.&#xD;
        </textblock>
      </criteria>
      <gender>Female</gender>
      <minimum_age>20 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Di Dong, Ph.D</last_name>
      <phone>+86 13811833760</phone>
      <email>di.dong@ia.ac.cn</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Yali Zang, Ph.D</last_name>
      <email>yali.zang@ia.ac.cn</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University</name>
        <address>
          <city>Beijing</city>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Di Dong</last_name>
        <email>di.dong@ia.ac.cn</email>
      </contact>
    </location>
    <location>
      <facility>
        <name>Peking University Third Hospital</name>
        <address>
          <city>Beijing</city>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Di Dong</last_name>
        <email>di.dong@ia.ac.cn</email>
      </contact>
    </location>
    <location>
      <facility>
        <name>Changsha Hospital for Maternal and Child Health Care</name>
        <address>
          <city>Changsha</city>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Di Dong</last_name>
        <email>di.dong@ia.ac.cn</email>
      </contact>
    </location>
    <location>
      <facility>
        <name>Second Xiangya Hospital of Central South University</name>
        <address>
          <city>Changsha</city>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Di Dong</last_name>
        <email>di.dong@ia.ac.cn</email>
      </contact>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 13, 2023</study_first_submitted>
    <study_first_submitted_qc>August 13, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 21, 2023</study_first_posted>
    <last_update_submitted>September 6, 2023</last_update_submitted>
    <last_update_submitted_qc>September 6, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 8, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Chinese Academy of Sciences</investigator_affiliation>
      <investigator_full_name>Di Dong</investigator_full_name>
      <investigator_title>Professor</investigator_title>
    </responsible_party>
    <keyword>Early Pregnancy</keyword>
    <keyword>Ultrasound</keyword>
    <keyword>Quality Control</keyword>
    <patient_data>
      <sharing_ipd>Yes</sharing_ipd>
      <ipd_description>Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.</ipd_description>
      <ipd_info_type>Study Protocol</ipd_info_type>
      <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
      <ipd_info_type>Analytic Code</ipd_info_type>
      <ipd_time_frame>Data will become available 6 months after study completion and will remain available for a period of 5 years.</ipd_time_frame>
      <ipd_access_criteria>Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access.</ipd_access_criteria>
      <ipd_url>http://www.radiomics.net.cn/</ipd_url>
    </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/NCT06002412
  org study id: CASMI005
  nct id: NCT06002412
  lead sponsor: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  This research integrates artificial intelligence to enhance early pregnancy ultrasonography
        quality control, focusing on specific fetal sections. In collaboration with prominent medical
        institutions, the investigators have amassed extensive fetal ultrasound data. The
        investigators aim to develop a deep learning model that can accurately identify essential
        anatomical areas in ultrasound images and evaluate their quality. This tool is expected to
        significantly decrease misdiagnoses of conditions like Down Syndrome and neural system
        deformities by ensuring real-time image quality assessment.
  This research is dedicated to integrating artificial intelligence technology to optimize the
        quality control process of early pregnancy ultrasonography. The ultrasound images involved
        primarily focus on the median sagittal section, NT section, and choroid plexus of the fetus
        during early pregnancy. In this regard, the investigators have collaborated with renowned
        medical institutions such as Beijing Obstetrics and Gynecology Hospital, Peking University
        Third Hospital, Changsha Hospital for Maternal and Child Health Care, and Second Xiangya
        Hospital of Central South University to retrospectively and prospectively collect a vast
        amount of early pregnancy fetal ultrasound image data. Based on this, the investigators plan
        to establish a model rooted in deep learning. This model will be capable of precisely
        identifying key anatomical regions in standard ultrasound scan images. Furthermore, by
        recognizing these anatomical structures, the model will determine whether the ultrasound
        image meets the standard scanning quality. This model is anticipated to serve as a powerful
        auxiliary tool in obstetric ultrasonography, enabling real-time assessment of ultrasound
        image quality, thereby significantly reducing the rates of missed and misdiagnosed fetal
        diseases such as Down Syndrome and neural system malformations.
  observational model: Cohort
  time perspective: Prospective
  measure: PR curve of image quality control module
  time frame: one month
  description: Using Precision-Recall curve and mean average percision as evaluating indicator of image quality control model.
  measure: The accuracy of intelligent analysis system in image quality control module
  time frame: one month
  description: The agreement between the prediction outcome of intelligent analysis system and the golden standard
  arm group label: Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University
  description: Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps.
  arm group label: Peking University Third Hospital
  description: Peking University Third Hospital collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps.
  arm group label: Changsha Hospital for Maternal and Child Health Care
  description: Changsha Hospital for Maternal and Child Health Care collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps.
  arm group label: Second Xiangya Hospital of Central South University
  description: Second Xiangya Hospital of Central South University collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps.
  intervention type: Other
  intervention name: Image quality control
  description: The investigators identify the region of interest in the relevant section to give a conclusion on whether the image is standard or not, guiding clinicians to standardize the operation, and reducing the rate of misdiagnosis and underdiagnosis.
  arm group label: Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University
  arm group label: Changsha Hospital for Maternal and Child Health Care
  arm group label: Peking University Third Hospital
  arm group label: Second Xiangya Hospital of Central South University
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: Female
  minimum age: 20 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Di Dong, Ph.D
  phone: +86 13811833760
  email: di.dong@ia.ac.cn
  last name: Yali Zang, Ph.D
  email: yali.zang@ia.ac.cn
  facility: 
  status: Recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  country: China
  responsible party type: Principal Investigator
  investigator affiliation: Chinese Academy of Sciences
  investigator full name: Di Dong
  investigator title: Professor
  sharing ipd: Yes
  ipd description: Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.
  ipd info type: Study Protocol
  ipd info type: Statistical Analysis Plan (SAP)
  ipd info type: Analytic Code
  ipd time frame: Data will become available 6 months after study completion and will remain available for a period of 5 years.
  ipd access criteria: Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access.
  ipd url: http://www.radiomics.net.cn/