<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/NCT03268031</url>
  </required_header>
  <id_info>
    <org_study_id>ProjectAGE</org_study_id>
    <nct_id>NCT03268031</nct_id>
  </id_info>
  <brief_title>Artificial Intelligence-assissted Glaucoma Evaluation</brief_title>
  <acronym>AGE</acronym>
  <official_title>Development of Artificial Intelligence-assissted Diagnostic Program of Glaucoma</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Sun Yat-sen University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Chinese Academy of Sciences</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Sun Yat-sen University</source>
  <oversight_info>
    <has_dmc>Yes</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>
      Glaucoma is currently the second leading cause of irreversible blindness in the world. Our&#xD;
      study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center&#xD;
      with Artificial Intelligence techniques to create programs that can screen and diagnose&#xD;
      glaucoma.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Glaucoma is currently the second leading cause of irreversible blindness in the world, which&#xD;
      brings heavy burden to human society. Compared to other ocular diseases, diagnostic process&#xD;
      of glaucoma is complicated depends on multiple test results, including visual field test,&#xD;
      OCT, etc. How to diagnose glaucoma correctly and fast has always been a hot topic in glaucoma&#xD;
      researches. Artificial intelligence is used to study and develop theories and methods that&#xD;
      can help simulate and extend human intelligence, which has been utilized in a lot of research&#xD;
      fields such as automatic drive and medicine. The study intends to combine clinical data of&#xD;
      glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to&#xD;
      create programs that can screen and diagnose glaucoma.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">August 15, 2017</start_date>
  <completion_date type="Actual">February 1, 2020</completion_date>
  <primary_completion_date type="Actual">December 1, 2019</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>Accuracy of diagnosis by artificial intelligence algorithm</measure>
    <time_frame>from August 2017 to February 2021</time_frame>
    <description>Accuracy of diagnosis by artificial intelligence algorithm and compare this result with glaucoma specialists</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Sensitivity of diagnosis by artificial intelligence algorithm</measure>
    <time_frame>from August 2017 to February 2021</time_frame>
    <description>Sensitivity of diagnosis by artificial intelligence algorithm</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Specificity of diagnosis by artificial intelligence algorithm</measure>
    <time_frame>from August 2017 to February 2021</time_frame>
    <description>Specificity of diagnosis by artificial intelligence algorithm</description>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">10800</enrollment>
  <condition>Artificial Intelligence</condition>
  <condition>Glaucoma</condition>
  <arm_group>
    <arm_group_label>Glaucoma patients</arm_group_label>
    <description>Glaucoma patients will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Non-glaucoma participants</arm_group_label>
    <description>Non-glaucoma participants will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning.</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Visual field and OCT tests</intervention_name>
    <description>Visual field test and OCT are commonly used essential tests to make accurate diagnosis of glaucoma. Algorithms to classify Visual field and OCT tests would both be developed and verified.</description>
    <arm_group_label>Glaucoma patients</arm_group_label>
    <arm_group_label>Non-glaucoma participants</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Anyone who can complete visual field test and have BCVA&gt;0.1 can be enrolled. We will&#xD;
        collect visual field test result and OCT images of both glaucoma and non-glaucoma patients.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. BCVA&gt;0.1&#xD;
&#xD;
          2. able to complete reliable visual field test&#xD;
&#xD;
          3. no history of intraocular surgery or fundus laser&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        1. unable to complete visual field test&#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>
  <overall_official>
    <last_name>Xiulan Zhang, Doctor</last_name>
    <role>Principal Investigator</role>
    <affiliation>Sun Yat-sen University</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Zhongshan Ophthalmic Center</name>
      <address>
        <city>Guangzhou</city>
        <state>Guangdong</state>
        <zip>510000</zip>
        <country>China</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <results_reference>
    <citation>Diprose W, Buist N. Artificial intelligence in medicine: humans need not apply? N Z Med J. 2016 May 6;129(1434):73-6.</citation>
    <PMID>27349266</PMID>
  </results_reference>
  <results_reference>
    <citation>Quigley HA. Glaucoma. Lancet. 2011 Apr 16;377(9774):1367-77. doi: 10.1016/S0140-6736(10)61423-7. Epub 2011 Mar 30.</citation>
    <PMID>21453963</PMID>
  </results_reference>
  <results_reference>
    <citation>Asaoka R, Murata H, Iwase A, Araie M. Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier. Ophthalmology. 2016 Sep;123(9):1974-80. doi: 10.1016/j.ophtha.2016.05.029. Epub 2016 Jul 7.</citation>
    <PMID>27395766</PMID>
  </results_reference>
  <verification_date>October 2020</verification_date>
  <study_first_submitted>August 29, 2017</study_first_submitted>
  <study_first_submitted_qc>August 30, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 31, 2017</study_first_posted>
  <last_update_submitted>October 19, 2020</last_update_submitted>
  <last_update_submitted_qc>October 19, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">October 22, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Sun Yat-sen University</investigator_affiliation>
    <investigator_full_name>Xiulan Zhang</investigator_full_name>
    <investigator_title>Director of Clinical Research Center</investigator_title>
  </responsible_party>
  <keyword>artificial intelligence</keyword>
  <keyword>glaucoma</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Glaucoma</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</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/NCT03268031
org study id: ProjectAGE
nct id: NCT03268031
lead sponsor: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
Glaucoma is currently the second leading cause of irreversible blindness in the world. Our
      study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center
      with Artificial Intelligence techniques to create programs that can screen and diagnose
      glaucoma.
Glaucoma is currently the second leading cause of irreversible blindness in the world, which
      brings heavy burden to human society. Compared to other ocular diseases, diagnostic process
      of glaucoma is complicated depends on multiple test results, including visual field test,
      OCT, etc. How to diagnose glaucoma correctly and fast has always been a hot topic in glaucoma
      researches. Artificial intelligence is used to study and develop theories and methods that
      can help simulate and extend human intelligence, which has been utilized in a lot of research
      fields such as automatic drive and medicine. The study intends to combine clinical data of
      glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to
      create programs that can screen and diagnose glaucoma.
observational model: Case-Only
time perspective: Retrospective
measure: Accuracy of diagnosis by artificial intelligence algorithm
time frame: from August 2017 to February 2021
description: Accuracy of diagnosis by artificial intelligence algorithm and compare this result with glaucoma specialists
measure: Sensitivity of diagnosis by artificial intelligence algorithm
time frame: from August 2017 to February 2021
description: Sensitivity of diagnosis by artificial intelligence algorithm
measure: Specificity of diagnosis by artificial intelligence algorithm
time frame: from August 2017 to February 2021
description: Specificity of diagnosis by artificial intelligence algorithm
arm group label: Glaucoma patients
description: Glaucoma patients will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning.
arm group label: Non-glaucoma participants
description: Non-glaucoma participants will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning.
intervention type: Diagnostic Test
intervention name: Visual field and OCT tests
description: Visual field test and OCT are commonly used essential tests to make accurate diagnosis of glaucoma. Algorithms to classify Visual field and OCT tests would both be developed and verified.
arm group label: Glaucoma patients
arm group label: Non-glaucoma participants
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Xiulan Zhang, Doctor
role: Principal Investigator
affiliation: Sun Yat-sen University
facility: 
country: China
citation: Diprose W, Buist N. Artificial intelligence in medicine: humans need not apply? N Z Med J. 2016 May 6;129(1434):73-6.
PMID: 27349266
citation: Quigley HA. Glaucoma. Lancet. 2011 Apr 16;377(9774):1367-77. doi: 10.1016/S0140-6736(10)61423-7. Epub 2011 Mar 30.
PMID: 21453963
citation: Asaoka R, Murata H, Iwase A, Araie M. Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier. Ophthalmology. 2016 Sep;123(9):1974-80. doi: 10.1016/j.ophtha.2016.05.029. Epub 2016 Jul 7.
PMID: 27395766
responsible party type: Principal Investigator
investigator affiliation: Sun Yat-sen University
investigator full name: Xiulan Zhang
investigator title: Director of Clinical Research Center
mesh term: Glaucoma
sharing ipd: No