<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/NCT03780582</url>
  </required_header>
  <id_info>
    <org_study_id>EN-122018</org_study_id>
    <nct_id>NCT03780582</nct_id>
  </id_info>
  <brief_title>Evaluation of Use of Diagnostic AI for Lung Cancer in Practice</brief_title>
  <official_title>Evaluation of Use of Diagnostic AI for Lung Cancer in Practice</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Ensemble Group Holdings, LLC</agency>
      <agency_class>Industry</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Ensemble Group Holdings, LLC</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>
      This study investigates ways of improving radiologists performance of the classification of&#xD;
      CT-scans as cancerous or non-cancerous. Participants interact with an AI to classify CT-scans&#xD;
      under three different conditions.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The three conditions are as follows: &quot;probabilistic classification&quot;, where the radiologist&#xD;
      diagnoses scans using an AI cancer likelihood score; &quot;classification plus detection&quot;, where&#xD;
      the radiologist see detecting lung nodules in addition to the AI's probabilistic&#xD;
      classification score before making her own examination of the CT-scan; and &quot;classification&#xD;
      with delayed detection&quot;, where the radiologist identifies regions of interest independently&#xD;
      of the AI and then sees the AI's detected ROIs.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Active, not recruiting</last_known_status>
  <start_date type="Actual">December 14, 2018</start_date>
  <completion_date type="Anticipated">December 15, 2019</completion_date>
  <primary_completion_date type="Anticipated">December 15, 2019</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>Crossover Assignment</intervention_model>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>Single (Participant)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Classification accuracy</measure>
    <time_frame>up to 4 months after initiation of evaluation of the test set</time_frame>
    <description>This compares radiologists' classifications with the ground truth in the tested cases.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>detection concordance</measure>
    <time_frame>up to 4 months after initiation of evaluation of the test set</time_frame>
    <description>Evaluation of concordance between radiologists in the tested cases in detection of lung nodules &gt; 4 mm</description>
  </secondary_outcome>
  <number_of_arms>3</number_of_arms>
  <enrollment type="Anticipated">15</enrollment>
  <condition>Lung Cancer</condition>
  <arm_group>
    <arm_group_label>Probabilistic Classification</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Radiologists see a &quot;score&quot; from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Classification Plus Detection</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Radiologists see a &quot;score&quot; from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. They also see ROIs identified by the AI that represent lung nodules.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Classification With Delayed Detection</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Radiologists see a &quot;score&quot; from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. After identifying their own ROIs, the radiologist then can see ROIs identified by the AI that represent lung nodules before making final decisions.</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>AI-human interaction</intervention_name>
    <description>Exploring what kinds of AI-human interaction improve radiologists detection accuracy.</description>
    <arm_group_label>Classification Plus Detection</arm_group_label>
    <arm_group_label>Classification With Delayed Detection</arm_group_label>
    <arm_group_label>Probabilistic Classification</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  The participant performs radiology screenings professionally&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        -&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>University of Hong Kong</name>
      <address>
        <city>Hong Kong</city>
        <country>Hong Kong</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Hong Kong</country>
  </location_countries>
  <verification_date>July 2019</verification_date>
  <study_first_submitted>December 16, 2018</study_first_submitted>
  <study_first_submitted_qc>December 17, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 19, 2018</study_first_posted>
  <last_update_submitted>July 20, 2019</last_update_submitted>
  <last_update_submitted_qc>July 20, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">July 23, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Lung Neoplasms</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


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/NCT03780582
org study id: EN-122018
nct id: NCT03780582
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
This study investigates ways of improving radiologists performance of the classification of
      CT-scans as cancerous or non-cancerous. Participants interact with an AI to classify CT-scans
      under three different conditions.
The three conditions are as follows: "probabilistic classification", where the radiologist
      diagnoses scans using an AI cancer likelihood score; "classification plus detection", where
      the radiologist see detecting lung nodules in addition to the AI's probabilistic
      classification score before making her own examination of the CT-scan; and "classification
      with delayed detection", where the radiologist identifies regions of interest independently
      of the AI and then sees the AI's detected ROIs.
allocation: Randomized
intervention model: Crossover Assignment
primary purpose: Diagnostic
masking: Single (Participant)
measure: Classification accuracy
time frame: up to 4 months after initiation of evaluation of the test set
description: This compares radiologists' classifications with the ground truth in the tested cases.
measure: detection concordance
time frame: up to 4 months after initiation of evaluation of the test set
description: Evaluation of concordance between radiologists in the tested cases in detection of lung nodules > 4 mm
arm group label: Probabilistic Classification
arm group type: Experimental
description: Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan.
arm group label: Classification Plus Detection
arm group type: Experimental
description: Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. They also see ROIs identified by the AI that represent lung nodules.
arm group label: Classification With Delayed Detection
arm group type: Experimental
description: Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. After identifying their own ROIs, the radiologist then can see ROIs identified by the AI that represent lung nodules before making final decisions.
intervention type: Behavioral
intervention name: AI-human interaction
description: Exploring what kinds of AI-human interaction improve radiologists detection accuracy.
arm group label: Classification Plus Detection
arm group label: Classification With Delayed Detection
arm group label: Probabilistic Classification
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
facility: 
country: Hong Kong
responsible party type: Sponsor
mesh term: Lung Neoplasms
sharing ipd: Undecided