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/NCT06040723</url>
    </required_header>
    <id_info>
      <org_study_id>AI04</org_study_id>
      <nct_id>NCT06040723</nct_id>
    </id_info>
    <brief_title>Hill-grade Knowledge Via Integrated Neural-network for Gastroscopy</brief_title>
    <acronym>HillKING</acronym>
    <official_title>Real Time Determination of the Hill Grade During Gastroscopy Using Artificial Intelligence</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Wuerzburg University Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Wuerzburg University 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 Hill classification, also known as the Hill grade, is a system used to classify the&#xD;
        severity of gastroesophageal valve incompetence, specifically related to gastroesophageal&#xD;
        reflux disease (GERD) and hiatal hernia. This study aims to compare the ability of physicians&#xD;
        versus an AI model to asses the Hill grade during gastroscopy.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Objective:&#xD;
  &#xD;
        The primary goal of this study is to compare the accuracy in determining the Hill&#xD;
        classification during gastroscopy between an artificial intelligence (AI) based system and&#xD;
        physicians performing the examination. Secondary outcomes include evaluation of the per-class&#xD;
        accuracy and other statistical measures such as precision, recall and f1 score.&#xD;
  &#xD;
        Study Design:&#xD;
  &#xD;
        Singlecenter, endoscopist blinded study. The model considered in a previous study achieved a&#xD;
        mean accuracy of 88%. All participants initially attended a lecture serving as a refresher&#xD;
        regarding the Hill classification. Subsequently, physicians were asked to provide the Hill&#xD;
        classification for test images expert annotated images depicting different Hill grades,&#xD;
        achieving mean accuracy of 72%. Thus 127 paired measurements are required. Taking patient&#xD;
        drop-out into consideration, at least 159 patients need to be recruited. Upon examination of&#xD;
        the flap-valve during endoscopy, the physician is required to store an image of the&#xD;
        flap-valve during retroflexion, which is part of the standard procedure, based on which they&#xD;
        determine the Hill classification. The prediction of the AI model on this image is considered&#xD;
        the model output and is considered the model's output. A group of three expert endoscopists&#xD;
        determines the Hill classification for each image, based on majority vote, which is treated&#xD;
        as the gold standard.&#xD;
  &#xD;
        AI setup and limitations:&#xD;
  &#xD;
        There are no limitations caused by the AI. The method performs a frame-by-frame analysis of&#xD;
        the recording. These images are parsed from the AI based system in order to obtain&#xD;
        predictions. The only interactions required with the method is a button press that initiates&#xD;
        the examination recording process and a second button press to terminate the recording. This&#xD;
        is performed at the beginning and end of the examination respectively. The model used in this&#xD;
        study is an updated version of the model reported in a preliminary study, that has been&#xD;
        trained with more data together with an auxiliary output for predicting if the Hill&#xD;
        classification is relevant to the shown image.&#xD;
  &#xD;
        Study population:&#xD;
  &#xD;
        All adult patients appointed for gastroscopy that do not match the exclusion criteria will be&#xD;
        asked for informed consent. Exclusion criteria include previous surgical interventions or&#xD;
        altered anatomy that prevents the proper examination of the flap valve, examinations where&#xD;
        the flap-valve is not inspected, and examinations where the expert committee does not produce&#xD;
        a majority vote.&#xD;
  &#xD;
        Intervention:&#xD;
  &#xD;
        The physician performs the examination as usual. Upon inspection of the flap valve, the&#xD;
        physician captures an image of the examination, as usual, and gives their assessment of the&#xD;
        Hill grade. The output of the model for the same image is considered the model prediction.&#xD;
        The physician is blinded to the model's prediction.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">October 1, 2023</start_date>
    <completion_date type="Anticipated">March 31, 2024</completion_date>
    <primary_completion_date type="Anticipated">January 31, 2024</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Only</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Comparison of correct and erroneous assessments of the Hill grade between physicians and AI method.</measure>
      <time_frame>Through study completion, an average of 5 months</time_frame>
      <description>Binary assessment of physician vs AI correct and erroneous predictions.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Per Hill grade comparison of correct and erroneous assessments between physicians and AI method.</measure>
      <time_frame>Through study completion, an average of 5 months</time_frame>
      <description>Description: The correct and erroneous predictions for each specific Hill class.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Per Hill grade accuracy, precision, and recall of the endoscopists and AI method assessments.</measure>
      <time_frame>Through study completion, an average of 5 months</time_frame>
      <description>The accuracy, precision, and recall statistics for each class (1v0) over the four different Hill classes.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Distance for label assessment from gold standard label.</measure>
      <time_frame>Through study completion, an average of 5 months</time_frame>
      <description>Comparison of the distance between the gold standard label and the label assigned by the phyisician and AI method.</description>
    </secondary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">159</enrollment>
    <condition>Gastroesophageal Hernia</condition>
    <arm_group>
      <arm_group_label>Experimental: Intervention arm</arm_group_label>
      <description>All patients within the study are included in the intervention arm: The Hill classification is determined by the physician and the AI method.</description>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>EndoMind</intervention_name>
      <description>The EndoMind system is equipped with an AI model for predicting the Hill grade during gastroscopy.</description>
      <arm_group_label>Experimental: Intervention arm</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          All adult patients that have not undergone and are not undergoing a surgery for&#xD;
          reconstruction of the flap-valve.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Adult patients (&gt;18 years)&#xD;
  &#xD;
            -  Scheduled gastroscopy&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
          Examination level&#xD;
  &#xD;
            -  Previous surgical interventions or altered anatomy that prevents the proper&#xD;
               examination of the flap valve&#xD;
  &#xD;
            -  Flap-valve not inspected&#xD;
  &#xD;
          Data Level:&#xD;
  &#xD;
            -  Image during flap-valve inspection not stored&#xD;
  &#xD;
            -  Expert committee not resulting in a majority vote&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Alexander Hann, Professor</last_name>
      <phone>+49 931 201-45918</phone>
      <email>hann_a@ukw.de</email>
    </overall_contact>
    <location>
      <facility>
        <name>Universitätsklinikum Würzburg</name>
        <address>
          <city>Würzburg</city>
          <state>Bayern</state>
          <zip>97080</zip>
          <country>Germany</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>Germany</country>
    </location_countries>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>September 8, 2023</study_first_submitted>
    <study_first_submitted_qc>September 8, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 15, 2023</study_first_posted>
    <last_update_submitted>September 8, 2023</last_update_submitted>
    <last_update_submitted_qc>September 8, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 15, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Hill grade</keyword>
    <keyword>Gastroscopy</keyword>
    <keyword>Gastroesophageal Flap Valve</keyword>
    <keyword>Artificial Intelligence</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Hernia</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/NCT06040723
  org study id: AI04
  nct id: NCT06040723
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The Hill classification, also known as the Hill grade, is a system used to classify the
        severity of gastroesophageal valve incompetence, specifically related to gastroesophageal
        reflux disease (GERD) and hiatal hernia. This study aims to compare the ability of physicians
        versus an AI model to asses the Hill grade during gastroscopy.
  Objective:
  
        The primary goal of this study is to compare the accuracy in determining the Hill
        classification during gastroscopy between an artificial intelligence (AI) based system and
        physicians performing the examination. Secondary outcomes include evaluation of the per-class
        accuracy and other statistical measures such as precision, recall and f1 score.
  
        Study Design:
  
        Singlecenter, endoscopist blinded study. The model considered in a previous study achieved a
        mean accuracy of 88%. All participants initially attended a lecture serving as a refresher
        regarding the Hill classification. Subsequently, physicians were asked to provide the Hill
        classification for test images expert annotated images depicting different Hill grades,
        achieving mean accuracy of 72%. Thus 127 paired measurements are required. Taking patient
        drop-out into consideration, at least 159 patients need to be recruited. Upon examination of
        the flap-valve during endoscopy, the physician is required to store an image of the
        flap-valve during retroflexion, which is part of the standard procedure, based on which they
        determine the Hill classification. The prediction of the AI model on this image is considered
        the model output and is considered the model's output. A group of three expert endoscopists
        determines the Hill classification for each image, based on majority vote, which is treated
        as the gold standard.
  
        AI setup and limitations:
  
        There are no limitations caused by the AI. The method performs a frame-by-frame analysis of
        the recording. These images are parsed from the AI based system in order to obtain
        predictions. The only interactions required with the method is a button press that initiates
        the examination recording process and a second button press to terminate the recording. This
        is performed at the beginning and end of the examination respectively. The model used in this
        study is an updated version of the model reported in a preliminary study, that has been
        trained with more data together with an auxiliary output for predicting if the Hill
        classification is relevant to the shown image.
  
        Study population:
  
        All adult patients appointed for gastroscopy that do not match the exclusion criteria will be
        asked for informed consent. Exclusion criteria include previous surgical interventions or
        altered anatomy that prevents the proper examination of the flap valve, examinations where
        the flap-valve is not inspected, and examinations where the expert committee does not produce
        a majority vote.
  
        Intervention:
  
        The physician performs the examination as usual. Upon inspection of the flap valve, the
        physician captures an image of the examination, as usual, and gives their assessment of the
        Hill grade. The output of the model for the same image is considered the model prediction.
        The physician is blinded to the model's prediction.
  observational model: Case-Only
  time perspective: Prospective
  measure: Comparison of correct and erroneous assessments of the Hill grade between physicians and AI method.
  time frame: Through study completion, an average of 5 months
  description: Binary assessment of physician vs AI correct and erroneous predictions.
  measure: Per Hill grade comparison of correct and erroneous assessments between physicians and AI method.
  time frame: Through study completion, an average of 5 months
  description: Description: The correct and erroneous predictions for each specific Hill class.
  measure: Per Hill grade accuracy, precision, and recall of the endoscopists and AI method assessments.
  time frame: Through study completion, an average of 5 months
  description: The accuracy, precision, and recall statistics for each class (1v0) over the four different Hill classes.
  measure: Distance for label assessment from gold standard label.
  time frame: Through study completion, an average of 5 months
  description: Comparison of the distance between the gold standard label and the label assigned by the phyisician and AI method.
  arm group label: Experimental: Intervention arm
  description: All patients within the study are included in the intervention arm: The Hill classification is determined by the physician and the AI method.
  intervention type: Device
  intervention name: EndoMind
  description: The EndoMind system is equipped with an AI model for predicting the Hill grade during gastroscopy.
  arm group label: Experimental: Intervention arm
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Alexander Hann, Professor
  phone: +49 931 201-45918
  email: hann_a@ukw.de
  facility: 
  country: Germany
  responsible party type: Sponsor
  mesh term: Hernia
  sharing ipd: No