<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/NCT04420156</url>
  </required_header>
  <id_info>
    <org_study_id>STUDY21403</org_study_id>
    <secondary_id>R01EY019474</secondary_id>
    <nct_id>NCT04420156</nct_id>
  </id_info>
  <brief_title>Clinical and Genetic Analysis of ROP</brief_title>
  <acronym>(ROP)</acronym>
  <official_title>Clinical and Genetic Analysis of Retinopathy of Prematurity</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Oregon Health and Science University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>National Institutes of Health (NIH)</agency>
      <agency_class>NIH</agency_class>
    </collaborator>
    <collaborator>
      <agency>U.S. National Science Foundation</agency>
      <agency_class>U.S. Fed</agency_class>
    </collaborator>
    <collaborator>
      <agency>Massachusetts General Hospital</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Northeastern University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>William Beaumont Hospitals</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University of Illinois at Chicago</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Stanford University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University of Utah</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>National Eye Institute (NEI)</agency>
      <agency_class>NIH</agency_class>
    </collaborator>
  </sponsors>
  <source>Oregon Health and Science 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>
      Retinopathy of Prematurity (ROP) is a vascular disease affecting the retinas (back of the&#xD;
      eye) of low birth weight infants. Although it can be treated effectively if diagnosed early,&#xD;
      it continues to be a leading cause of childhood blindness in the United States and throughout&#xD;
      the world. The investigators feel that this study will result in specific knowledge discovery&#xD;
      about ROP, as well as general knowledge about how image-based data and genetic data can be&#xD;
      combined to better understand clinical disease.&#xD;
&#xD;
      Participants will be recruited from the neonatal intensive care unit (NICU) at OHSU, along&#xD;
      with 4 collaborating institutions (William Beaumont Hospital, Stanford University, University&#xD;
      of Illinois Chicago and University of Utah). Hospitalized infants who receive ROP screening&#xD;
      examinations for routine care will be eligible for this study, and will be offered the&#xD;
      opportunity to participate. Subjects who provide informed consent will have clinical data&#xD;
      from routine care collected along with demographic characteristics, results from routine ROP&#xD;
      screening examinations, presence of systemic disease or risk factors. Retinal photographs&#xD;
      will be taken during these routine eye exams, using a commercially-available camera that has&#xD;
      been FDA-cleared for taking pictures from retinas of premature infants. These retinal&#xD;
      pictures do not contain any identifiable patient information, and are taken as routine&#xD;
      standard of care.&#xD;
&#xD;
      The long-term goal of this research is to establish a quantitative framework for retinopathy&#xD;
      of prematurity (ROP) care based on clinical, imaging, genetic, and informatics principles.&#xD;
      The investigators have previously recruited and rigorously phenotyped and genotyped a large&#xD;
      study cohort, including implementation of a novel reference standard diagnosis; and built a&#xD;
      world-class research consortium for image, genetic, and bioinformatics analysis.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      This NIH funded multi-center study began July 2011 with 8 study sites approved by their&#xD;
      individual IRBs. Recruitment and data was conducted at the following sites: OHSU, Columbia&#xD;
      Universtiy, Cornell College, William Beaumont Hospital, Children's Hospital LA, University of&#xD;
      Miami, University of Illinois Chicago, Cedars Sinai Medical Center and Asociacion para Evitar&#xD;
      la Ceguera (APEC) in Mexico City. For the competitive renewal of the grant which begins&#xD;
      6-01-20, the recruitment sites have been reduced to 5 which include OHSU, William Beaumont,&#xD;
      University of Illinois Chicago, University of Utah and Stanford University.&#xD;
&#xD;
      This study will aim to develop a quantitative framework for ROP care using artificial&#xD;
      intelligence and analytics to improve clinical disease management. The investigators will&#xD;
      evaluate performance of an artificial intelligence system for ROP diagnosis and screening&#xD;
      prospectively. This will include: (a) recruit a target of over 2000 eye exams including&#xD;
      wide-angle retinal images from 375 subjects at 5 centers, (b) optimize an image quality&#xD;
      detection algorithm the investigators have recently developed, and (c) analyze system&#xD;
      accuracy for ROP diagnosis (plus vs. pre-plus vs. normal) and screening (using a novel&#xD;
      quantitative vascular severity scale).&#xD;
&#xD;
      The proposed work will study infants who will receive routine ophthalmoscopic exams and have&#xD;
      retinal images taken at each exam according to the standard of care at each institution. At&#xD;
      least one person at each site is trained to capture wide-angle retinal images using a&#xD;
      commercially-available camera (RetCam; Natus, Pleasanton, CA). This device is FDA-cleared for&#xD;
      premature infants, and has been used throughout the world for 20 years with no known&#xD;
      complications.&#xD;
&#xD;
      All participating infants will undergo retinal photography by trained study personnel for up&#xD;
      to 3 eye exams, or more if clinically indicated and feasible. &quot;Outborn infants,&quot; who were&#xD;
      transferred to the study center for specialized ROP care, will have at least one set of&#xD;
      images taken if this is clinically indicated and feasible. These coded retinal images will be&#xD;
      read and interpreted by remote expert graders using the secure web-based system developed for&#xD;
      this study 9 years ago at OHSU. The de-identified images will be housed indefinitely in an&#xD;
      OHSU IRB-approved repository for possible future research studies or for other educational&#xD;
      purposes.&#xD;
&#xD;
      Most infants recruited from the first 9 years of this study between July 2011 and May 2020&#xD;
      had DNA collected from blood or saliva samples. The coded genetic samples are housed in an&#xD;
      OHSU IRB-approved repository and will be analyzed by outside collaborators for specific aim 3&#xD;
      of this study. Note that this current study does not involve new collection of any blood or&#xD;
      saliva samples.&#xD;
&#xD;
      In recent years, our team has successfully developed competitive image assessment methods to&#xD;
      infer ROP status using (i) engineered image features based on translating descriptive and&#xD;
      visual descriptions related to expert assessment . and (ii) deep-learned features based on&#xD;
      end-to-end training of neural networks for image analysis. To improve model explainability,&#xD;
      the models must not only provide classification (diagnostic) labels or severity scores, but&#xD;
      also supplementary information regarding how a model produces its decisions and what about a&#xD;
      particular image drives the decision. To this end, it is helpful for a model to (i) visualize&#xD;
      its training data; and (ii) illustrate which features of the input image its decision relied&#xD;
      heavily on. Visualization can provide an overarching demonstration of how a model produces&#xD;
      its decisions across a dataset with known clinical and demographic characteristics, which&#xD;
      contributes to overall interpretability of the model's logic. Illustration is essential to&#xD;
      gain trust and to facilitate validation when clinicians rely on the model to assess a&#xD;
      particular instance for various purposes, including training or regulatory approval.&#xD;
&#xD;
      While training the AI system in previous work, the investigators excluded 5% of images that&#xD;
      were rated by the majority of graders as &quot;not acceptable quality&quot;. For real-world use, it&#xD;
      will be important to balance imageability with diagnostic performance. The investigators&#xD;
      propose to evaluate our existing dataset to determine the optimal operating point in the CNN&#xD;
      quality algorithm that balances imageability with diagnostic performance of the i-ROP DL&#xD;
      classifier. The investigators will continue those studies to systematically examine their&#xD;
      impact on improving image quality and diagnostic performance - and maximize rigor and&#xD;
      reproducibility of study design. This operating point will then be &quot;locked&quot;, and the closed&#xD;
      system will be used as below.&#xD;
&#xD;
      Prospective evaluation of i-ROP DL classifier: The investigators propose to calculate the&#xD;
      weighted kappa between the RSD and the i-ROP DL system, along with sensitivity, specificity,&#xD;
      and imageability based on the optimal operating points identified above.&#xD;
&#xD;
      Prospective evaluation of vascular severity score: In a cross sectional analysis, the&#xD;
      investigators will test the hypothesis that the ROP vascular severity score, derived from the&#xD;
      i-ROP DL classifier, may demonstrate high sensitivity both for detection of plus disease and&#xD;
      for identifying treatment-requiring disease in a real-world ROP screening population.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">July 1, 2011</start_date>
  <completion_date type="Anticipated">May 31, 2030</completion_date>
  <primary_completion_date type="Anticipated">May 31, 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>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Evaluate diagnostic accuracy of an AI system for ROP diagnosis</measure>
    <time_frame>4 years</time_frame>
    <description>Premature babies are examined for retinopathy of prematurity (ROP), a potentially blinding diesese. As a standard of care, retinal images are taken during ROP examinations. This research group has collected a repository of images over the past 9 years and with those images, the investigators have developed an artificial intelligence (AI) system that has the ability to diagnose severe ROP with high accuracy. The primary outcome measure in continuing to recruit subjects for this study is to collect more images to improve the existing AI system and expand the ability to diagnose ROP.</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">2000</enrollment>
  <condition>Retinopathy of Prematurity</condition>
  <arm_group>
    <arm_group_label>i-ROP cohort</arm_group_label>
    <description>Premature infants who are at risk of retinopathy of prematurity(ROP) at participating study sites. As standard of care, babies who are born less than 31 weeks gestational age or less than 1500 grams are routinely screened for ROP. Families are approached to participate in this study where finding from babies' eye exams and associated retinal images along with demographic and other health data are collected and coded with unique identifier. No intervention is administered. The ROP exams and images obtained are done as a standard of care and would be performed even if there is no consent provided.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>No intervention administered.</intervention_name>
    <description>Eye exams are standard of care and would be performed regardless of participation in this study.</description>
    <arm_group_label>i-ROP cohort</arm_group_label>
  </intervention>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      Blood or saliva samples were taken over the first 9 years of this study and are currently&#xD;
      being analyzed. The study is no longer consenting subjects for dna collection.&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        Premature babies that are born earlier than 31 weeks gestational age or less than 1500&#xD;
        grams and are hospitalized at one of the 5 participating recruitment sites.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  All infants hospitalized at participating Neonatal Intensive Care Units will be&#xD;
             eligible for the study if they meet plublished criteria for requiring ROP screening&#xD;
             examination, or if they are transferred to the study center for specialized ophthalmic&#xD;
             care. These eligibility criteria are identical at each study center, and match what is&#xD;
             done in standard clinical practice according to national guidelines published jointly&#xD;
             by the American Academy of Pediatrics, American Academy of Ophthalmology, and American&#xD;
             Associatioin for Pediatric Ophthalmology and Strabismus (AAP-AAO, Pediatrics, 2013).&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patients will be excluded if they have structural ocular anomalies, or if they are&#xD;
             considered unstable for examintion by their attending neonatologist.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>1 Year</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>John P Campbell, M.D.</last_name>
    <role>Principal Investigator</role>
    <affiliation>Oregon Health and Science University</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>John P Campbell, M.D.</last_name>
    <phone>503-494-7891</phone>
    <email>campbelp@ohsu.edu</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Susan R Ostmo, M.S.</last_name>
    <phone>503-494-7450</phone>
    <email>ostmo@ohsu.edu</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Stanford University</name>
      <address>
        <city>Palo Alto</city>
        <state>California</state>
        <zip>94303</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Not yet recruiting</status>
    <contact>
      <last_name>Darius Moshfeghi, M.D.</last_name>
      <phone>650-723-6995</phone>
      <email>dariusm@stanford.edu</email>
    </contact>
    <contact_backup>
      <last_name>Mariana Nunez</last_name>
      <phone>650-723-6995</phone>
      <email>mnunez1@stanford.edu</email>
    </contact_backup>
  </location>
  <location>
    <facility>
      <name>University of Illinois Chicago</name>
      <address>
        <city>Chicago</city>
        <state>Illinois</state>
        <zip>60607</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Robison Vernon P Chan, M.D.</last_name>
      <phone>312-996-6660</phone>
      <email>rvpchan@uic.edu</email>
    </contact>
    <contact_backup>
      <last_name>Bhavana Kolli</last_name>
      <phone>630-863-1487</phone>
      <email>bkolli2@uic.edu</email>
    </contact_backup>
  </location>
  <location>
    <facility>
      <name>William Beaumont Hospital</name>
      <address>
        <city>Royal Oak</city>
        <state>Michigan</state>
        <zip>48073</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Kimberly Drenser, M.D.</last_name>
      <phone>248-288-2280</phone>
      <email>kdrenser@arcpc.net</email>
    </contact>
    <contact_backup>
      <last_name>Mary Zajechowski</last_name>
      <phone>248-330-7649</phone>
      <email>mary.zajechowski@beaumont.org</email>
    </contact_backup>
  </location>
  <location>
    <facility>
      <name>Oregon Health &amp; Science University</name>
      <address>
        <city>Portland</city>
        <state>Oregon</state>
        <zip>97239</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>John P Campbell, M.D.</last_name>
      <phone>503-494-7891</phone>
      <email>campbelp@ohsu.edu</email>
    </contact>
    <contact_backup>
      <last_name>Susan R Osmto, M.S.</last_name>
      <phone>503-494-7450</phone>
      <email>ostmo@ohsu.edu</email>
    </contact_backup>
    <investigator>
      <last_name>Aaron Coyner, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Kemal Sonmez, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location>
    <facility>
      <name>University of Utah</name>
      <address>
        <city>Salt Lake City</city>
        <state>Utah</state>
        <zip>84132</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Not yet recruiting</status>
    <contact>
      <last_name>Mary E Hartnett, M.D.</last_name>
      <phone>801-581-2352</phone>
      <email>me.hartnett@hsc.utah.edu</email>
    </contact>
    <contact_backup>
      <last_name>Deborah Harrison</last_name>
      <phone>801-581-2352</phone>
      <email>deborah.harrison@hsc.utah.edu</email>
    </contact_backup>
    <investigator>
      <last_name>Leah Owen, M.D.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>April 2022</verification_date>
  <study_first_submitted>May 20, 2020</study_first_submitted>
  <study_first_submitted_qc>June 4, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 9, 2020</study_first_posted>
  <last_update_submitted>April 18, 2022</last_update_submitted>
  <last_update_submitted_qc>April 18, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">April 20, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Oregon Health and Science University</investigator_affiliation>
    <investigator_full_name>Michael Chiang</investigator_full_name>
    <investigator_title>Principal Investigator</investigator_title>
  </responsible_party>
  <keyword>ophthalmology</keyword>
  <keyword>neonatology</keyword>
  <keyword>prematurity</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Retinal Diseases</mesh_term>
    <mesh_term>Retinopathy of Prematurity</mesh_term>
    <mesh_term>Premature Birth</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
    <ipd_description>All study subject data will be assigned a unique study code and recorded in a format OHSU-####, where: (a) OHSU refers to the study center that subject was recruited from (to distinguish from subjects recruited from other centers), (b) #### is a sequential number. The key to the unique study code linking the identity of the subjects will be kept in secure password protected files accessible only to authorized study personnel. De-identified data from outside sites and coded data from OHSU will be stored indefinitely in a secure IRB approved repository (7775) and may be used for future research studies. Only de-identified data will be shared with outside research collaborators.</ipd_description>
    <ipd_info_type>Study Protocol</ipd_info_type>
    <ipd_info_type>Informed Consent Form (ICF)</ipd_info_type>
    <ipd_time_frame>Unknown at this time.</ipd_time_frame>
    <ipd_access_criteria>Undetermined at this time.</ipd_access_criteria>
  </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/NCT04420156
org study id: STUDY21403
secondary id: R01EY019474
nct id: NCT04420156
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
Retinopathy of Prematurity (ROP) is a vascular disease affecting the retinas (back of the
      eye) of low birth weight infants. Although it can be treated effectively if diagnosed early,
      it continues to be a leading cause of childhood blindness in the United States and throughout
      the world. The investigators feel that this study will result in specific knowledge discovery
      about ROP, as well as general knowledge about how image-based data and genetic data can be
      combined to better understand clinical disease.

      Participants will be recruited from the neonatal intensive care unit (NICU) at OHSU, along
      with 4 collaborating institutions (William Beaumont Hospital, Stanford University, University
      of Illinois Chicago and University of Utah). Hospitalized infants who receive ROP screening
      examinations for routine care will be eligible for this study, and will be offered the
      opportunity to participate. Subjects who provide informed consent will have clinical data
      from routine care collected along with demographic characteristics, results from routine ROP
      screening examinations, presence of systemic disease or risk factors. Retinal photographs
      will be taken during these routine eye exams, using a commercially-available camera that has
      been FDA-cleared for taking pictures from retinas of premature infants. These retinal
      pictures do not contain any identifiable patient information, and are taken as routine
      standard of care.

      The long-term goal of this research is to establish a quantitative framework for retinopathy
      of prematurity (ROP) care based on clinical, imaging, genetic, and informatics principles.
      The investigators have previously recruited and rigorously phenotyped and genotyped a large
      study cohort, including implementation of a novel reference standard diagnosis; and built a
      world-class research consortium for image, genetic, and bioinformatics analysis.
This NIH funded multi-center study began July 2011 with 8 study sites approved by their
      individual IRBs. Recruitment and data was conducted at the following sites: OHSU, Columbia
      Universtiy, Cornell College, William Beaumont Hospital, Children's Hospital LA, University of
      Miami, University of Illinois Chicago, Cedars Sinai Medical Center and Asociacion para Evitar
      la Ceguera (APEC) in Mexico City. For the competitive renewal of the grant which begins
      6-01-20, the recruitment sites have been reduced to 5 which include OHSU, William Beaumont,
      University of Illinois Chicago, University of Utah and Stanford University.

      This study will aim to develop a quantitative framework for ROP care using artificial
      intelligence and analytics to improve clinical disease management. The investigators will
      evaluate performance of an artificial intelligence system for ROP diagnosis and screening
      prospectively. This will include: (a) recruit a target of over 2000 eye exams including
      wide-angle retinal images from 375 subjects at 5 centers, (b) optimize an image quality
      detection algorithm the investigators have recently developed, and (c) analyze system
      accuracy for ROP diagnosis (plus vs. pre-plus vs. normal) and screening (using a novel
      quantitative vascular severity scale).

      The proposed work will study infants who will receive routine ophthalmoscopic exams and have
      retinal images taken at each exam according to the standard of care at each institution. At
      least one person at each site is trained to capture wide-angle retinal images using a
      commercially-available camera (RetCam; Natus, Pleasanton, CA). This device is FDA-cleared for
      premature infants, and has been used throughout the world for 20 years with no known
      complications.

      All participating infants will undergo retinal photography by trained study personnel for up
      to 3 eye exams, or more if clinically indicated and feasible. "Outborn infants," who were
      transferred to the study center for specialized ROP care, will have at least one set of
      images taken if this is clinically indicated and feasible. These coded retinal images will be
      read and interpreted by remote expert graders using the secure web-based system developed for
      this study 9 years ago at OHSU. The de-identified images will be housed indefinitely in an
      OHSU IRB-approved repository for possible future research studies or for other educational
      purposes.

      Most infants recruited from the first 9 years of this study between July 2011 and May 2020
      had DNA collected from blood or saliva samples. The coded genetic samples are housed in an
      OHSU IRB-approved repository and will be analyzed by outside collaborators for specific aim 3
      of this study. Note that this current study does not involve new collection of any blood or
      saliva samples.

      In recent years, our team has successfully developed competitive image assessment methods to
      infer ROP status using (i) engineered image features based on translating descriptive and
      visual descriptions related to expert assessment . and (ii) deep-learned features based on
      end-to-end training of neural networks for image analysis. To improve model explainability,
      the models must not only provide classification (diagnostic) labels or severity scores, but
      also supplementary information regarding how a model produces its decisions and what about a
      particular image drives the decision. To this end, it is helpful for a model to (i) visualize
      its training data; and (ii) illustrate which features of the input image its decision relied
      heavily on. Visualization can provide an overarching demonstration of how a model produces
      its decisions across a dataset with known clinical and demographic characteristics, which
      contributes to overall interpretability of the model's logic. Illustration is essential to
      gain trust and to facilitate validation when clinicians rely on the model to assess a
      particular instance for various purposes, including training or regulatory approval.

      While training the AI system in previous work, the investigators excluded 5% of images that
      were rated by the majority of graders as "not acceptable quality". For real-world use, it
      will be important to balance imageability with diagnostic performance. The investigators
      propose to evaluate our existing dataset to determine the optimal operating point in the CNN
      quality algorithm that balances imageability with diagnostic performance of the i-ROP DL
      classifier. The investigators will continue those studies to systematically examine their
      impact on improving image quality and diagnostic performance - and maximize rigor and
      reproducibility of study design. This operating point will then be "locked", and the closed
      system will be used as below.

      Prospective evaluation of i-ROP DL classifier: The investigators propose to calculate the
      weighted kappa between the RSD and the i-ROP DL system, along with sensitivity, specificity,
      and imageability based on the optimal operating points identified above.

      Prospective evaluation of vascular severity score: In a cross sectional analysis, the
      investigators will test the hypothesis that the ROP vascular severity score, derived from the
      i-ROP DL classifier, may demonstrate high sensitivity both for detection of plus disease and
      for identifying treatment-requiring disease in a real-world ROP screening population.
observational model: Cohort
time perspective: Prospective
measure: Evaluate diagnostic accuracy of an AI system for ROP diagnosis
time frame: 4 years
description: Premature babies are examined for retinopathy of prematurity (ROP), a potentially blinding diesese. As a standard of care, retinal images are taken during ROP examinations. This research group has collected a repository of images over the past 9 years and with those images, the investigators have developed an artificial intelligence (AI) system that has the ability to diagnose severe ROP with high accuracy. The primary outcome measure in continuing to recruit subjects for this study is to collect more images to improve the existing AI system and expand the ability to diagnose ROP.
arm group label: i-ROP cohort
description: Premature infants who are at risk of retinopathy of prematurity(ROP) at participating study sites. As standard of care, babies who are born less than 31 weeks gestational age or less than 1500 grams are routinely screened for ROP. Families are approached to participate in this study where finding from babies' eye exams and associated retinal images along with demographic and other health data are collected and coded with unique identifier. No intervention is administered. The ROP exams and images obtained are done as a standard of care and would be performed even if there is no consent provided.
intervention type: Other
intervention name: No intervention administered.
description: Eye exams are standard of care and would be performed regardless of participation in this study.
arm group label: i-ROP cohort
Blood or saliva samples were taken over the first 9 years of this study and are currently
      being analyzed. The study is no longer consenting subjects for dna collection.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: 1 Year
healthy volunteers: Accepts Healthy Volunteers
last name: John P Campbell, M.D.
role: Principal Investigator
affiliation: Oregon Health and Science University
last name: John P Campbell, M.D.
phone: 503-494-7891
email: campbelp@ohsu.edu
last name: Susan R Ostmo, M.S.
phone: 503-494-7450
email: ostmo@ohsu.edu
facility: 
status: Not yet recruiting
contact: 
contact backup: 
facility: 
status: Recruiting
contact: 
contact backup: 
facility: 
status: Recruiting
contact: 
contact backup: 
facility: 
status: Recruiting
contact: 
contact backup: 
investigator: 
investigator: 
facility: 
status: Not yet recruiting
contact: 
contact backup: 
investigator: 
country: United States
responsible party type: Principal Investigator
investigator affiliation: Oregon Health and Science University
investigator full name: Michael Chiang
investigator title: Principal Investigator
mesh term: Retinal Diseases
mesh term: Retinopathy of Prematurity
mesh term: Premature Birth
sharing ipd: Yes
ipd description: All study subject data will be assigned a unique study code and recorded in a format OHSU-####, where: (a) OHSU refers to the study center that subject was recruited from (to distinguish from subjects recruited from other centers), (b) #### is a sequential number. The key to the unique study code linking the identity of the subjects will be kept in secure password protected files accessible only to authorized study personnel. De-identified data from outside sites and coded data from OHSU will be stored indefinitely in a secure IRB approved repository (7775) and may be used for future research studies. Only de-identified data will be shared with outside research collaborators.
ipd info type: Study Protocol
ipd info type: Informed Consent Form (ICF)
ipd time frame: Unknown at this time.
ipd access criteria: Undetermined at this time.