<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/NCT04479319</url>
  </required_header>
  <id_info>
    <org_study_id>09081</org_study_id>
    <nct_id>NCT04479319</nct_id>
  </id_info>
  <brief_title>Decision Support System Algorithm for COVID-19 Diagnosis</brief_title>
  <official_title>Developing Hybrid Decision Support System Algorithm for COVID-19 Diagnosis Between RT-PCR Graphics and Thorax CT Images Using Deep Learning</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Ankara University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Presidency of Health Institute Turkey (TUSEB)</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Ankara 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>
      COVID-19 is an infectious disease caused by a newly discovered Coronavirus which was first&#xD;
      identified in Wuhan, China in December 2019. Then the novel coronavirus outbreak was&#xD;
      described and announced as a pandemic by World Health Organization (WHO) on March 11, 2020.&#xD;
&#xD;
      Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard test&#xD;
      for diagnosis of COVID-19. Nevertheless, due to its high false-negative rates (%10-50),&#xD;
      diagnosis and treatment decisions do not depend on RT-PCR alone. Clinical presentation of&#xD;
      patient and radiological findings are also important. However, neither clinical presentation&#xD;
      nor computed tomography (CT) findings are specific for COVID-19. As a consequence of these&#xD;
      challenges, the diagnosis of the disease and the protection of the community health become&#xD;
      more difficult. The investigators of this study hypothesized that deep learning-based&#xD;
      decision support system may help for definitive diagnosis of COVID-19. The aim is to develop&#xD;
      a deep learning-based decision support system algorithm based on clinical presentation of&#xD;
      patient, laboratory and CT findings and RT-PCR data. Previously, deep learning algorithms&#xD;
      with the use of widely known deep neural network architectures such as Inception, UNet,&#xD;
      ResNet were developed. However all of these studies were based on CT findings. There are not&#xD;
      any deep learning study in literature combining the clinical, radiological, and laboratory&#xD;
      findings of patients.&#xD;
&#xD;
      The project is based on the available data of COVID-19 patients that will be obtained from&#xD;
      the Ministry of Health. Then the data will be evaluated for relevance and reliability and&#xD;
      labeled for the training of machine. Following the anonymization of data, data will be&#xD;
      processed according to the predetermined inclusion-exclusion criteria. Thorax CT data will be&#xD;
      labeled as typical / indeterminate / atypical / negative for COVID-19 pneumonia. Also, CT&#xD;
      images of patients with known non-COVID-19 diseases will be labeled for the training of&#xD;
      machine. Then, fever, lymphocyte count, neutrophil to lymphocyte ratio, contact information,&#xD;
      RT-PCR findings will be labeled. Subsequently, the patients will be labeled and the machine&#xD;
      will be trained with deep learning method with the help of this grouped and labeled data.&#xD;
      Following the training phase, the algorithm will be tested and if the machine reaches the&#xD;
      target specificity and sensitivity, the prototype will be tested. And then, the prototype&#xD;
      will be embedded into the hospital software system. This software and algorithm will serve as&#xD;
      an early warning system for clinicians and provide a better diagnostic rate especially with&#xD;
      decreasing false-negative results. The effects of a pandemic cannot be measured by only the&#xD;
      number of people diagnosed and isolated, or treatment provided. A pandemic affects not only&#xD;
      community health but also individuals' psychological status, education, teaching methods,&#xD;
      working models, daily lifestyles, producer/consumer behaviors, supply/demand balance; in&#xD;
      other words every single area of life. On top of that, a pandemic causes long-term damages&#xD;
      hard to reverse. The software will increase the diagnostic success rates, help to control the&#xD;
      pandemic and minimize the collateral damages mentioned above. The investigators believe that,&#xD;
      the product that will be produced at the end of this project will be of great benefit in&#xD;
      controlling the secondary wave of COVID-19 expected to occur.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">December 31, 2020</start_date>
  <completion_date type="Actual">April 1, 2022</completion_date>
  <primary_completion_date type="Actual">November 1, 2021</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>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Diagnosing COVID-19</measure>
    <time_frame>Through study completion, an average of 1 year</time_frame>
    <description>Determination of sensitivity and specificity in predicting COVID-19 diagnosis of hybrid decision support system</description>
  </primary_outcome>
  <number_of_groups>3</number_of_groups>
  <enrollment type="Actual">3215</enrollment>
  <condition>Covid19</condition>
  <arm_group>
    <arm_group_label>COVID-19 Pneumonia</arm_group_label>
    <description>COVID-19 patients who have pneumonia on thorax CT&#xD;
either Thorax CT + SARS-CoV-2 RT-PCR + Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +/-&#xD;
or Thorax CT + SARS-CoV-2 RT-PCR - Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +</description>
  </arm_group>
  <arm_group>
    <arm_group_label>COVID-19, without Pneumonia</arm_group_label>
    <description>COVID-19 patients who have not pneumonia on thorax CT&#xD;
Thorax CT - SARS-CoV-2 RT-PCR + Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +/-</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Non COVID-19</arm_group_label>
    <description>Patients with viral infection symptoms who is not diagnosed with COVID-19&#xD;
either Thorax CT - SARS-CoV-2 RT-PCR - Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +/-&#xD;
or Thorax CT + SARS-CoV-2 RT-PCR - Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 -</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Thorax CT</intervention_name>
    <description>Subjects in all arms have a Thorax CT and RT-PCR for SARS-CoV-2.</description>
    <arm_group_label>COVID-19 Pneumonia</arm_group_label>
    <arm_group_label>COVID-19, without Pneumonia</arm_group_label>
    <arm_group_label>Non COVID-19</arm_group_label>
    <other_name>RT-PCR</other_name>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Adult patients with a differential diagnosis of COVID-19 and tested for it with Thorax CT&#xD;
        and RT-PCR in Turkey.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Adult patients with a differential diagnosis of COVID-19&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patients who are under 18 year-old&#xD;
&#xD;
          -  Patients who have not either Thorax CT or SARS-CoV-2 RT-PCR&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Özlem Özdemir Kumbasar, Prof Dr</last_name>
    <role>Study Chair</role>
    <affiliation>Ankara University</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Ankara University Faculty of Medicine</name>
      <address>
        <city>Ankara</city>
        <country>Turkey</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>İhsan Doğramacı Bilkent Üniversitesi</name>
      <address>
        <city>Ankara</city>
        <country>Turkey</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Turkey</country>
  </location_countries>
  <reference>
    <citation>Jan B, Farman H, Khan M, Imran M, Islam IU, Ahmad A, et al. Deep learning in big data analytics: a comparative study. Computers &amp; Electrical Engineering. 2019;75:275-87</citation>
  </reference>
  <reference>
    <citation>Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M, Henry TS, Kanne JP, Kligerman S, Ko JP, Litt H. Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA - Secondary Publication. J Thorac Imaging. 2020 Jul;35(4):219-227. doi: 10.1097/RTI.0000000000000524.</citation>
    <PMID>32324653</PMID>
  </reference>
  <reference>
    <citation>Santosh KC. AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. J Med Syst. 2020 Mar 18;44(5):93. doi: 10.1007/s10916-020-01562-1.</citation>
    <PMID>32189081</PMID>
  </reference>
  <reference>
    <citation>Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020 Jul-Aug;14(4):337-339. doi: 10.1016/j.dsx.2020.04.012. Epub 2020 Apr 14.</citation>
    <PMID>32305024</PMID>
  </reference>
  <reference>
    <citation>Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K, Liu D, Wang G, Xu Q, Fang X, Zhang S, Xia J, Xia J. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology. 2020 Aug;296(2):E65-E71. doi: 10.1148/radiol.2020200905. Epub 2020 Mar 19.</citation>
    <PMID>32191588</PMID>
  </reference>
  <reference>
    <citation>Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018 Sep;11045:3-11. doi: 10.1007/978-3-030-00889-5_1. Epub 2018 Sep 20.</citation>
    <PMID>32613207</PMID>
  </reference>
  <reference>
    <citation>Li D, Wang D, Dong J, Wang N, Huang H, Xu H, Xia C. False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases. Korean J Radiol. 2020 Apr;21(4):505-508. doi: 10.3348/kjr.2020.0146. Epub 2020 Mar 5.</citation>
    <PMID>32174053</PMID>
  </reference>
  <reference>
    <citation>Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, Li C. The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia. Invest Radiol. 2020 Jun;55(6):327-331. doi: 10.1097/RLI.0000000000000672.</citation>
    <PMID>32118615</PMID>
  </reference>
  <verification_date>May 2022</verification_date>
  <study_first_submitted>July 16, 2020</study_first_submitted>
  <study_first_submitted_qc>July 19, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 21, 2020</study_first_posted>
  <last_update_submitted>May 15, 2022</last_update_submitted>
  <last_update_submitted_qc>May 15, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">May 17, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Ankara University</investigator_affiliation>
    <investigator_full_name>Dr.Ozlem Ozdemir Kumbasar</investigator_full_name>
    <investigator_title>Prof Dr</investigator_title>
  </responsible_party>
  <keyword>Deep learning</keyword>
  <keyword>RT-PCR</keyword>
  <keyword>Thorax CT</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</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/NCT04479319
org study id: 09081
nct id: NCT04479319
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
COVID-19 is an infectious disease caused by a newly discovered Coronavirus which was first
      identified in Wuhan, China in December 2019. Then the novel coronavirus outbreak was
      described and announced as a pandemic by World Health Organization (WHO) on March 11, 2020.

      Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard test
      for diagnosis of COVID-19. Nevertheless, due to its high false-negative rates (%10-50),
      diagnosis and treatment decisions do not depend on RT-PCR alone. Clinical presentation of
      patient and radiological findings are also important. However, neither clinical presentation
      nor computed tomography (CT) findings are specific for COVID-19. As a consequence of these
      challenges, the diagnosis of the disease and the protection of the community health become
      more difficult. The investigators of this study hypothesized that deep learning-based
      decision support system may help for definitive diagnosis of COVID-19. The aim is to develop
      a deep learning-based decision support system algorithm based on clinical presentation of
      patient, laboratory and CT findings and RT-PCR data. Previously, deep learning algorithms
      with the use of widely known deep neural network architectures such as Inception, UNet,
      ResNet were developed. However all of these studies were based on CT findings. There are not
      any deep learning study in literature combining the clinical, radiological, and laboratory
      findings of patients.

      The project is based on the available data of COVID-19 patients that will be obtained from
      the Ministry of Health. Then the data will be evaluated for relevance and reliability and
      labeled for the training of machine. Following the anonymization of data, data will be
      processed according to the predetermined inclusion-exclusion criteria. Thorax CT data will be
      labeled as typical / indeterminate / atypical / negative for COVID-19 pneumonia. Also, CT
      images of patients with known non-COVID-19 diseases will be labeled for the training of
      machine. Then, fever, lymphocyte count, neutrophil to lymphocyte ratio, contact information,
      RT-PCR findings will be labeled. Subsequently, the patients will be labeled and the machine
      will be trained with deep learning method with the help of this grouped and labeled data.
      Following the training phase, the algorithm will be tested and if the machine reaches the
      target specificity and sensitivity, the prototype will be tested. And then, the prototype
      will be embedded into the hospital software system. This software and algorithm will serve as
      an early warning system for clinicians and provide a better diagnostic rate especially with
      decreasing false-negative results. The effects of a pandemic cannot be measured by only the
      number of people diagnosed and isolated, or treatment provided. A pandemic affects not only
      community health but also individuals' psychological status, education, teaching methods,
      working models, daily lifestyles, producer/consumer behaviors, supply/demand balance; in
      other words every single area of life. On top of that, a pandemic causes long-term damages
      hard to reverse. The software will increase the diagnostic success rates, help to control the
      pandemic and minimize the collateral damages mentioned above. The investigators believe that,
      the product that will be produced at the end of this project will be of great benefit in
      controlling the secondary wave of COVID-19 expected to occur.
observational model: Case-Only
time perspective: Retrospective
measure: Diagnosing COVID-19
time frame: Through study completion, an average of 1 year
description: Determination of sensitivity and specificity in predicting COVID-19 diagnosis of hybrid decision support system
arm group label: COVID-19 Pneumonia
description: COVID-19 patients who have pneumonia on thorax CT
either Thorax CT + SARS-CoV-2 RT-PCR + Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +/-
or Thorax CT + SARS-CoV-2 RT-PCR - Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +
arm group label: COVID-19, without Pneumonia
description: COVID-19 patients who have not pneumonia on thorax CT
Thorax CT - SARS-CoV-2 RT-PCR + Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +/-
arm group label: Non COVID-19
description: Patients with viral infection symptoms who is not diagnosed with COVID-19
either Thorax CT - SARS-CoV-2 RT-PCR - Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +/-
or Thorax CT + SARS-CoV-2 RT-PCR - Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 -
intervention type: Diagnostic Test
intervention name: Thorax CT
description: Subjects in all arms have a Thorax CT and RT-PCR for SARS-CoV-2.
arm group label: COVID-19 Pneumonia
arm group label: COVID-19, without Pneumonia
arm group label: Non COVID-19
other name: RT-PCR
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Özlem Özdemir Kumbasar, Prof Dr
role: Study Chair
affiliation: Ankara University
facility: 
facility: 
country: Turkey
citation: Jan B, Farman H, Khan M, Imran M, Islam IU, Ahmad A, et al. Deep learning in big data analytics: a comparative study. Computers & Electrical Engineering. 2019;75:275-87
citation: Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M, Henry TS, Kanne JP, Kligerman S, Ko JP, Litt H. Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA - Secondary Publication. J Thorac Imaging. 2020 Jul;35(4):219-227. doi: 10.1097/RTI.0000000000000524.
PMID: 32324653
citation: Santosh KC. AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. J Med Syst. 2020 Mar 18;44(5):93. doi: 10.1007/s10916-020-01562-1.
PMID: 32189081
citation: Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020 Jul-Aug;14(4):337-339. doi: 10.1016/j.dsx.2020.04.012. Epub 2020 Apr 14.
PMID: 32305024
citation: Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K, Liu D, Wang G, Xu Q, Fang X, Zhang S, Xia J, Xia J. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology. 2020 Aug;296(2):E65-E71. doi: 10.1148/radiol.2020200905. Epub 2020 Mar 19.
PMID: 32191588
citation: Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018 Sep;11045:3-11. doi: 10.1007/978-3-030-00889-5_1. Epub 2018 Sep 20.
PMID: 32613207
citation: Li D, Wang D, Dong J, Wang N, Huang H, Xu H, Xia C. False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases. Korean J Radiol. 2020 Apr;21(4):505-508. doi: 10.3348/kjr.2020.0146. Epub 2020 Mar 5.
PMID: 32174053
citation: Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, Li C. The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia. Invest Radiol. 2020 Jun;55(6):327-331. doi: 10.1097/RLI.0000000000000672.
PMID: 32118615
responsible party type: Principal Investigator
investigator affiliation: Ankara University
investigator full name: Dr.Ozlem Ozdemir Kumbasar
investigator title: Prof Dr
mesh term: COVID-19
sharing ipd: Undecided