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/NCT05987215</url>
    </required_header>
    <id_info>
      <org_study_id>22-5019 / 69HCL22_1193</org_study_id>
      <nct_id>NCT05987215</nct_id>
    </id_info>
    <brief_title>Interest of Using Deep Learning Algorithm for Otosclerosis Detection on Temporal Bone High Resolution CT</brief_title>
    <acronym>OtoIA</acronym>
    <official_title>Interest of Using Deep Learning Algorithm for Otosclerosis Detection on Temporal Bone High Resolution CT</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Hospices Civils de Lyon</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Hospices Civils de Lyon</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        Otosclerosis is a relatively frequent pathology, of multifactorial origin with genetic and&#xD;
        hormonal part, predominantly in women. This disease causes a disorder of the bone metabolism&#xD;
        of the middle and inner ear, responsible for a progressive deafness, which can become severe.&#xD;
  &#xD;
        Several elements are necessary to make the diagnosis of otosclerosis: the clinical&#xD;
        examination and questioning, the audiometric assessment, and finally the temporal bone CT.&#xD;
  &#xD;
        The CT scan allows to detect foci of otosclerosis within the bone of the middle or inner ear.&#xD;
        This diagnosis is sometimes difficult and requires interpretation by a trained radiologist.&#xD;
  &#xD;
        The investigators would like to evaluate the ability of a deep learning algorithm to detect&#xD;
        these foci of otosclerosis, and to compare its diagnostic performance with a trained&#xD;
        radiologist.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Otosclerosis is a relatively frequent pathology, of multifactorial origin with genetic and&#xD;
        hormonal part, predominantly in women. This disease causes a disorder of the bone metabolism&#xD;
        of the middle and inner ear, responsible for a progressive deafness, which can become severe.&#xD;
  &#xD;
        Several elements are necessary to make the diagnosis of otosclerosis: the clinical&#xD;
        examination and questioning, the audiometric assessment, and finally the temporal bone CT.&#xD;
  &#xD;
        The CT scan allows to detect foci of otosclerosis within the bone of the middle or inner ear.&#xD;
        This diagnosis is sometimes difficult and requires interpretation by a trained radiologist.&#xD;
  &#xD;
        The investigators would like to evaluate the ability of a deep learning algorithm to detect&#xD;
        these foci of otosclerosis, and to compare its diagnostic performance with a trained&#xD;
        radiologist.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Active, not recruiting</overall_status>
    <start_date type="Actual">July 1, 2022</start_date>
    <completion_date type="Anticipated">October 1, 2023</completion_date>
    <primary_completion_date type="Actual">May 1, 2023</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Control</observational_model>
      <time_perspective>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Diagnostic performance of the artificial intelligence algorithm compared to the diagnostic performance of the radiologist : sensitivity, specificity, positive and negative predictive value, area under the ROC curve</measure>
      <time_frame>through study completion, an average of 5 months</time_frame>
      <description>These diagnostic performances will be established from the positive or negative diagnoses of the algorithm and the radiologist, compared to the &quot;case&quot; or &quot;control&quot; status of each patient included in the study</description>
    </primary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">240</enrollment>
    <condition>Otosclerosis</condition>
    <arm_group>
      <arm_group_label>CASE</arm_group_label>
      <description>Patients with surgically confirmed otosclerosis who initially consulted for conductive hearing loss with normal otoscopy, and with a high resolution computed tomography of temporal bone available</description>
    </arm_group>
    <arm_group>
      <arm_group_label>CONTROL</arm_group_label>
      <description>Random patients with a high resolution computed tomography scan of temporal bone performed without suspicion of otosclerosis and considered normal</description>
    </arm_group>
    <intervention>
      <intervention_type>Combination Product</intervention_type>
      <intervention_name>Radiologic diagnosis</intervention_name>
      <description>Each CT scan is interpreted by a radiologist and is assigned as positive or negative for the diagnosis of otosclerosis</description>
      <arm_group_label>CASE</arm_group_label>
      <arm_group_label>CONTROL</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>Artificial intelligence diagnosis</intervention_name>
      <description>Each CT scan is screened by the deep learning algorithm and is assigned as positive or negative for the diagnosis of otosclerosis</description>
      <arm_group_label>CASE</arm_group_label>
      <arm_group_label>CONTROL</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          The study population will include cases from a group of patients who have undergone surgery&#xD;
          for otosclerosis at the Lyon Sud hospital in France, and controls who have had a high&#xD;
          resolution temporal bone CT scan at the Lyon Sud hospital with no clinical suspicion for&#xD;
          otosclerosis and considered normal.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          -  Inclusion Criteria * :&#xD;
  &#xD;
                 -  age over 18&#xD;
  &#xD;
                 -  high resolution temporal bone CT scan available for analysis&#xD;
  &#xD;
                 -  for the &quot;case&quot; group : surgical confirmation of positive diagnosis for&#xD;
                    otosclerosis&#xD;
  &#xD;
                 -  for the &quot;control&quot; group : a first radiological analysis in favor of a normal&#xD;
                    temporal bone CT scanner and an initial radiologic report considered normal as&#xD;
                    well&#xD;
  &#xD;
            -  Exclusion Criteria * :&#xD;
  &#xD;
                 -  age under 18&#xD;
  &#xD;
                 -  no high resolution temporal bone CT scan available for analysis&#xD;
  &#xD;
                 -  unwillingness to participate in the study&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>110 Years</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Maxime FIEUX, MD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Hospices Civils de Lyon</affiliation>
    </overall_official>
    <location>
      <facility>
        <name>Hospices Civils de Lyon, Centre Hospitalier Lyon sud, Service d'ORL, d'otoneurchirurgie et de chirurgie cervico-facaile</name>
        <address>
          <city>Pierre-Bénite</city>
          <zip>69310</zip>
          <country>France</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>France</country>
    </location_countries>
    <verification_date>May 2023</verification_date>
    <study_first_submitted>May 17, 2023</study_first_submitted>
    <study_first_submitted_qc>August 9, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 14, 2023</study_first_posted>
    <last_update_submitted>August 9, 2023</last_update_submitted>
    <last_update_submitted_qc>August 9, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 14, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>otosclerosis</keyword>
    <keyword>IA</keyword>
    <keyword>deep learning</keyword>
    <keyword>temporal bone CT</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Otosclerosis</mesh_term>
    </condition_browse>
    <!-- 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/NCT05987215
  org study id: 22-5019 / 69HCL22_1193
  nct id: NCT05987215
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  Otosclerosis is a relatively frequent pathology, of multifactorial origin with genetic and
        hormonal part, predominantly in women. This disease causes a disorder of the bone metabolism
        of the middle and inner ear, responsible for a progressive deafness, which can become severe.
  
        Several elements are necessary to make the diagnosis of otosclerosis: the clinical
        examination and questioning, the audiometric assessment, and finally the temporal bone CT.
  
        The CT scan allows to detect foci of otosclerosis within the bone of the middle or inner ear.
        This diagnosis is sometimes difficult and requires interpretation by a trained radiologist.
  
        The investigators would like to evaluate the ability of a deep learning algorithm to detect
        these foci of otosclerosis, and to compare its diagnostic performance with a trained
        radiologist.
  Otosclerosis is a relatively frequent pathology, of multifactorial origin with genetic and
        hormonal part, predominantly in women. This disease causes a disorder of the bone metabolism
        of the middle and inner ear, responsible for a progressive deafness, which can become severe.
  
        Several elements are necessary to make the diagnosis of otosclerosis: the clinical
        examination and questioning, the audiometric assessment, and finally the temporal bone CT.
  
        The CT scan allows to detect foci of otosclerosis within the bone of the middle or inner ear.
        This diagnosis is sometimes difficult and requires interpretation by a trained radiologist.
  
        The investigators would like to evaluate the ability of a deep learning algorithm to detect
        these foci of otosclerosis, and to compare its diagnostic performance with a trained
        radiologist.
  observational model: Case-Control
  time perspective: Retrospective
  measure: Diagnostic performance of the artificial intelligence algorithm compared to the diagnostic performance of the radiologist : sensitivity, specificity, positive and negative predictive value, area under the ROC curve
  time frame: through study completion, an average of 5 months
  description: These diagnostic performances will be established from the positive or negative diagnoses of the algorithm and the radiologist, compared to the "case" or "control" status of each patient included in the study
  arm group label: CASE
  description: Patients with surgically confirmed otosclerosis who initially consulted for conductive hearing loss with normal otoscopy, and with a high resolution computed tomography of temporal bone available
  arm group label: CONTROL
  description: Random patients with a high resolution computed tomography scan of temporal bone performed without suspicion of otosclerosis and considered normal
  intervention type: Combination Product
  intervention name: Radiologic diagnosis
  description: Each CT scan is interpreted by a radiologist and is assigned as positive or negative for the diagnosis of otosclerosis
  arm group label: CASE
  arm group label: CONTROL
  intervention type: Diagnostic Test
  intervention name: Artificial intelligence diagnosis
  description: Each CT scan is screened by the deep learning algorithm and is assigned as positive or negative for the diagnosis of otosclerosis
  arm group label: CASE
  arm group label: CONTROL
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 110 Years
  healthy volunteers: Accepts Healthy Volunteers
  last name: Maxime FIEUX, MD
  role: Principal Investigator
  affiliation: Hospices Civils de Lyon
  facility: 
  country: France
  responsible party type: Sponsor
  mesh term: Otosclerosis