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/NCT06002373</url>
    </required_header>
    <id_info>
      <org_study_id>8114</org_study_id>
      <nct_id>NCT06002373</nct_id>
    </id_info>
    <brief_title>Assessment of Artificial Intelligence for Treatment Decision Recommendation of Adult Skeletal Class III Patients</brief_title>
    <official_title>Reliability Of Artificial Intelligence for Treatment Decision Recommendation of Adult Skeletal Class III Patients: A Diagnostic Test Accuracy Study</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Cairo University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Cairo University</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 study titled &quot;Reliability Of Artificial Intelligence for Treatment Decision&#xD;
        Recommendation of Adult Skeletal Class III Patients&quot; aims to assess the accuracy and&#xD;
        dependability of artificial intelligence (AI) in providing treatment decision recommendations&#xD;
        for adult patients with skeletal Class III malocclusion. Skeletal Class III malocclusion is&#xD;
        characterized by an underdeveloped upper jaw or an overdeveloped lower jaw, leading to facial&#xD;
        and dental irregularities. The study focuses on evaluating whether AI-based recommendations&#xD;
        can reliably guide orthodontic treatment planning for this specific patient group.&#xD;
  &#xD;
        This diagnostic test accuracy study involves collecting a diverse dataset of adult patients&#xD;
        diagnosed with skeletal Class III malocclusion. AI algorithms will be trained on this dataset&#xD;
        using various clinical and radiographic parameters to learn patterns and make treatment&#xD;
        recommendations. The study will then compare the AI-generated treatment recommendations to&#xD;
        those provided by experienced orthodontists.&#xD;
  &#xD;
        Key aspects of the study include:&#xD;
  &#xD;
        AI Reliability: The primary objective is to assess how consistently and accurately the AI&#xD;
        system can recommend appropriate treatment decisions for adult skeletal Class III patients.&#xD;
  &#xD;
        Diagnostic Test Accuracy: The study will determine the sensitivity, specificity, positive&#xD;
        predictive value, and negative predictive value of the AI-generated treatment&#xD;
        recommendations. This analysis will highlight the AI's ability to correctly identify patients&#xD;
        who require specific treatment interventions.&#xD;
  &#xD;
        Clinical Validity: Researchers will investigate whether the AI recommendations align with the&#xD;
        decisions made by experienced orthodontists. This assessment is crucial to establish the AI&#xD;
        system's clinical applicability.&#xD;
  &#xD;
        Potential Benefits: If the AI system proves reliable and accurate, it could offer a&#xD;
        time-efficient and standardized method for treatment decision support, aiding orthodontists&#xD;
        in providing personalized care to adult skeletal Class III patients.&#xD;
  &#xD;
        By conducting this study, researchers aim to contribute to the advancement of AI-assisted&#xD;
        medical decision-making within the field of orthodontics. Successful outcomes would have the&#xD;
        potential to revolutionize treatment planning processes, improve patient outcomes, and&#xD;
        provide a valuable tool for orthodontists to make informed treatment decisions for adult&#xD;
        skeletal Class III patients&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">May 1, 2023</start_date>
    <completion_date type="Anticipated">February 2024</completion_date>
    <primary_completion_date type="Anticipated">January 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>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>sensitivity and specificity</measure>
      <time_frame>1 month</time_frame>
      <description>the difference in sensitivity and specificity between the treatment decisions taken by the clinicians in comparison to those provided by the artificial intelligence software</description>
    </primary_outcome>
    <enrollment type="Anticipated">150</enrollment>
    <condition>Skeletal Dysplasia</condition>
    <condition>Reverse Overjet</condition>
    <eligibility>
      <study_pop>
        <textblock>
          Adult patients with skeletal Class III malocclusion&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Skeletally mature patients with CVMI 6.&#xD;
  &#xD;
            -  Skeletal class III patients&#xD;
  &#xD;
            -  No congenital deformity, syndrome, or cleft.&#xD;
  &#xD;
            -  No previous surgical intervention&#xD;
  &#xD;
            -  No mandibular transverse functional shift.&#xD;
  &#xD;
            -  Normal overjet, overbite after completion of treatment.&#xD;
  &#xD;
            -  Patients with well finished occlusion.&#xD;
  &#xD;
            -  Patients who have achieved adequate functional and aesthetic results at the end of&#xD;
               their treatment.&#xD;
  &#xD;
            -  Good quality initial and final lateral cephalometric radiographs.&#xD;
  &#xD;
            -  No sex predilection.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Adolescents and skeletally immature patients.&#xD;
  &#xD;
            -  Patients with pseudo class III.&#xD;
  &#xD;
            -  Syndromic patients.&#xD;
  &#xD;
            -  Patients with facial deformity at the naso-maxillary complex&#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>Maha AM Swelam, PhD</last_name>
      <phone>00201123344551</phone>
      <email>maha.swelam@gmail.com</email>
    </overall_contact>
    <location>
      <facility>
        <name>Cairo University</name>
        <address>
          <city>Cairo</city>
          <country>Egypt</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Abd El Rahim</last_name>
      </contact>
    </location>
    <location_countries>
      <country>Egypt</country>
    </location_countries>
    <verification_date>August 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>August 19, 2023</last_update_submitted>
    <last_update_submitted_qc>August 19, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 23, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Cairo University</investigator_affiliation>
      <investigator_full_name>Maha Abd El-Monem Nasr</investigator_full_name>
      <investigator_title>Principal Investigator</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Osteochondrodysplasias</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/NCT06002373
  org study id: 8114
  nct id: NCT06002373
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The study titled "Reliability Of Artificial Intelligence for Treatment Decision
        Recommendation of Adult Skeletal Class III Patients" aims to assess the accuracy and
        dependability of artificial intelligence (AI) in providing treatment decision recommendations
        for adult patients with skeletal Class III malocclusion. Skeletal Class III malocclusion is
        characterized by an underdeveloped upper jaw or an overdeveloped lower jaw, leading to facial
        and dental irregularities. The study focuses on evaluating whether AI-based recommendations
        can reliably guide orthodontic treatment planning for this specific patient group.
  
        This diagnostic test accuracy study involves collecting a diverse dataset of adult patients
        diagnosed with skeletal Class III malocclusion. AI algorithms will be trained on this dataset
        using various clinical and radiographic parameters to learn patterns and make treatment
        recommendations. The study will then compare the AI-generated treatment recommendations to
        those provided by experienced orthodontists.
  
        Key aspects of the study include:
  
        AI Reliability: The primary objective is to assess how consistently and accurately the AI
        system can recommend appropriate treatment decisions for adult skeletal Class III patients.
  
        Diagnostic Test Accuracy: The study will determine the sensitivity, specificity, positive
        predictive value, and negative predictive value of the AI-generated treatment
        recommendations. This analysis will highlight the AI's ability to correctly identify patients
        who require specific treatment interventions.
  
        Clinical Validity: Researchers will investigate whether the AI recommendations align with the
        decisions made by experienced orthodontists. This assessment is crucial to establish the AI
        system's clinical applicability.
  
        Potential Benefits: If the AI system proves reliable and accurate, it could offer a
        time-efficient and standardized method for treatment decision support, aiding orthodontists
        in providing personalized care to adult skeletal Class III patients.
  
        By conducting this study, researchers aim to contribute to the advancement of AI-assisted
        medical decision-making within the field of orthodontics. Successful outcomes would have the
        potential to revolutionize treatment planning processes, improve patient outcomes, and
        provide a valuable tool for orthodontists to make informed treatment decisions for adult
        skeletal Class III patients
  observational model: Cohort
  time perspective: Retrospective
  measure: sensitivity and specificity
  time frame: 1 month
  description: the difference in sensitivity and specificity between the treatment decisions taken by the clinicians in comparison to those provided by the artificial intelligence software
  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: Maha AM Swelam, PhD
  phone: 00201123344551
  email: maha.swelam@gmail.com
  facility: 
  status: Recruiting
  contact: 
  country: Egypt
  responsible party type: Principal Investigator
  investigator affiliation: Cairo University
  investigator full name: Maha Abd El-Monem Nasr
  investigator title: Principal Investigator
  mesh term: Osteochondrodysplasias