<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/NCT03793231</url>
  </required_header>
  <id_info>
    <org_study_id>43127/MonH-2018-152967</org_study_id>
    <secondary_id>012018</secondary_id>
    <secondary_id>2015-54</secondary_id>
    <nct_id>NCT03793231</nct_id>
  </id_info>
  <brief_title>fungalAi for Fungal Surveillance &amp; Antifungal Stewardship</brief_title>
  <acronym>fungalAi</acronym>
  <official_title>Innovative Use of fungalAi for Antifungal Stewardship in Haematology-oncology Patients</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Bayside Health</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Monash University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Monash Health</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Eastern Health</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Western Sydney Local Health District</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>SA Health</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Fremantle Hospital and Health Service</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Singhealth Foundation</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Bayside Health</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>
      This national Australian study will validate and implement an effective approach to real-time&#xD;
      electronic surveillance of fungal infections in patients with blood cancers using technology&#xD;
      based on artificial intelligence. It will establish metrics for antifungal stewardship&#xD;
      allowing benchmarking of these programs; provide decision support for radiologist&#xD;
      interpretation of chest imaging and improve reporting, audit and feedback practices in&#xD;
      hospitals where these infections are managed.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Invasive fungal diseases (IFD) are rare infections that cause a life-threatening pneumonia in&#xD;
      patients with weakened immune systems usually due to cancer chemotherapy and transplantation.&#xD;
      Fungal spores are found in air, water and soil making exposure unavoidable in vulnerable&#xD;
      patients. In developed countries, molds like Aspergillus are the most challenging type of IFD&#xD;
      to diagnose and treat. These infections usually manifest as a culture-negative fungal&#xD;
      pneumonia and account for approximately 300K of the 1.9M cases of IFD globally, but estimates&#xD;
      are not accurate due to an absence of surveillance systems in hospitals where these&#xD;
      infections are managed. Hospitals spend millions on antifungal drugs but are unaware of their&#xD;
      patients affected, the effectiveness of their prevention efforts and hospital outbreaks may&#xD;
      go unnoticed because surveillance, audit and feedback of fungal infections is not occurring.&#xD;
&#xD;
      Optimising patient outcomes through timely diagnosis and appropriate prescribing of&#xD;
      antifungal drugs is the goal of antifungal stewardship programs. Antifungal stewardship is of&#xD;
      growing importance to hospitals world-wide because antifungal drugs are few in number,&#xD;
      expensive to use and are associated with significant side-effects and drug interactions.&#xD;
      Surveillance, audit and feedback are the cornerstones of antifungal stewardship programs that&#xD;
      ensure patient care is meeting high standards. However, currently hospitals do not have the&#xD;
      mechanisms to detect rare events like fungal infections because it usually presents as a&#xD;
      pneumonia buried among hundreds of imaging scans.&#xD;
&#xD;
      &quot;fungalAi™&quot; (fungalAi.com) is a technology based on artificial intelligence (Ai) that uses&#xD;
      existing data in hospitals to make real time surveillance of fungal infections possible and&#xD;
      assist radiologist interpretation of diagnostic imaging. fungalAi does this through:&#xD;
&#xD;
        1. Natural language processing, a computational method of understanding human language.&#xD;
&#xD;
        2. Deep learning based image analysis of diagnostic imaging and&#xD;
&#xD;
        3. An expert system that integrates clinical data.&#xD;
&#xD;
      What will be the impact?&#xD;
&#xD;
      This project will provide hospitals with the mechanisms for performing real-time surveillance&#xD;
      and audit of fungal infections in blood cancer patients through the innovative use of Ai.&#xD;
      Strengthening antifungal stewardship through real-time surveillance of fungal diseases will&#xD;
      improve patient care by revealing gaps in practice, new patient groups at risk for fungal&#xD;
      infections and reduce inappropriate prescribing of antifungal medications through timely&#xD;
      audit and feedback. The impact of this project will be:&#xD;
&#xD;
        1. Improved diagnosis and recognition of fungal infections.&#xD;
&#xD;
        2. Enhanced prevention.&#xD;
&#xD;
        3. More appropriate use of antifungal medications.&#xD;
&#xD;
      FungalAi is a scalable technology that will be validated against active manual surveillance&#xD;
      of fungal infections in a multi-centre Australian clinical trial. The inclusive approach of&#xD;
      fungalAi means that it is of value to many vulnerable patients including neglected groups&#xD;
      like children who are included in this project. FungalAi is tuned for detection of fungal&#xD;
      pneumonia caused by molds because these infections are more diagnostically challenging than&#xD;
      other types of fungal infections. As a result, fungalAi leverages chest computed tomography&#xD;
      imaging because it is a critical diagnostic test that is widely available and performed more&#xD;
      frequently than invasive tests like lung washings or biopsy. Hence fungalAi natural language&#xD;
      processing may miss very rare manifestations like brain infections. Nevertheless, automating&#xD;
      detection of fungal pneumonia and improving radiologist recognition of a rare disease using a&#xD;
      self-improving system based on neural networks is an important step towards improving the&#xD;
      supportive care of patients with cancer. Improving outcomes in cancer is not only about&#xD;
      finding a cure. Reducing the impact of infectious threats like fungal diseases is just as&#xD;
      important and this can now be achieved by integrating artificial intelligence into patient&#xD;
      care.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Enrolling by invitation</last_known_status>
  <start_date type="Actual">January 1, 2019</start_date>
  <completion_date type="Anticipated">December 30, 2020</completion_date>
  <primary_completion_date type="Anticipated">December 30, 2020</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>Accuracy of electronic surveillance using fungalAi natural language processing compared to active manual methods for detection of fungal pneumonia</measure>
    <time_frame>12 months</time_frame>
    <description>Sensitivity, specificity, ROC, Area under precision-recall curve of Ai assisted surveillance for fungal pneumonia using natural language processing of imaging reports compared to active manual surveillance</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Accuracy of disease classification of deep learning based image analysis for fungal pneumonia at scan level.</measure>
    <time_frame>12 months</time_frame>
    <description>Sensitivity, specificity, ROC of deep learning based image analysis at scan level compared to active manual surveillance.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Accuracy of feature detection of fungal pneumonia using deep learning based image analysis of chest CT compared to radiologist expertise.</measure>
    <time_frame>12 months</time_frame>
    <description>Sensitivity, error rate (false positives, false negatives) at pixel level of deep learning based image analysis compared to radiologist labels.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Accuracy of disease classification of an expert system integrating microbiology and antifungal drug prescriptions with text and image analysis compared to active manual surveillance.</measure>
    <time_frame>12 months</time_frame>
    <description>Sensitivity, specificity, ROC, Area under precision-recall curve of Ai assisted surveillance compared to active manual surveillance that will only be performed at Alfred Health.</description>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">1000</enrollment>
  <condition>Fungal Infections</condition>
  <arm_group>
    <arm_group_label>Fungal Cases</arm_group_label>
    <description>Patients with confirmed invasive fungal infections according to internationally accepted criteria identified by active manual surveillance. Clinical data will be sent to fungalAi platform technology for disease classification.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Control patients</arm_group_label>
    <description>Patients without invasive fungal infections.Clinical data will be sent to fungalAi platform technology for disease classification.</description>
  </arm_group>
  <intervention>
    <intervention_type>Combination Product</intervention_type>
    <intervention_name>fungalAi platform technology</intervention_name>
    <description>Electronic surveillance and radiologic diagnosis of invasive fungal infections using fungalAi and associated methodologies.</description>
    <arm_group_label>Control patients</arm_group_label>
    <arm_group_label>Fungal Cases</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Adults and children with blood cancers under the haematology service at participating sites&#xD;
        inclusive of inpatient and ambulatory care patients.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Adults and children&#xD;
&#xD;
          -  Under the haematology service at participating sites&#xD;
&#xD;
          -  Inpatient and ambulatory patients.&#xD;
&#xD;
        Exclusion Criteria: No exclusion criteria&#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>Michelle Dr Ananda-Rajah</last_name>
    <role>Principal Investigator</role>
    <affiliation>The Alfred</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Alfred Health</name>
      <address>
        <city>Melbourne</city>
        <state>Victoria</state>
        <country>Australia</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Australia</country>
  </location_countries>
  <verification_date>August 2020</verification_date>
  <study_first_submitted>December 15, 2018</study_first_submitted>
  <study_first_submitted_qc>January 2, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">January 4, 2019</study_first_posted>
  <last_update_submitted>October 22, 2020</last_update_submitted>
  <last_update_submitted_qc>October 22, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">October 23, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Fungal infections</keyword>
  <keyword>Deep learning</keyword>
  <keyword>Electronic surveillance</keyword>
  <keyword>Antifungal stewardship</keyword>
  <keyword>Image classification</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Mycoses</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
    <ipd_description>De-identified individual participant data will be made available in aggregate form for reports, presentations and publications.</ipd_description>
    <ipd_info_type>Study Protocol</ipd_info_type>
    <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
    <ipd_info_type>Clinical Study Report (CSR)</ipd_info_type>
    <ipd_time_frame>Data will be made available within 6-12 months after study completion.</ipd_time_frame>
    <ipd_access_criteria>Access to study protocol, SAP, CSR will be made publicly available. Access to IPD including individual labelled data will be reviewed by an external independent review panel to ensure that all ethical issues have been met.</ipd_access_criteria>
    <ipd_url>http://fungalai.com</ipd_url>
  </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/NCT03793231
org study id: 43127/MonH-2018-152967
secondary id: 012018
secondary id: 2015-54
nct id: NCT03793231
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
This national Australian study will validate and implement an effective approach to real-time
      electronic surveillance of fungal infections in patients with blood cancers using technology
      based on artificial intelligence. It will establish metrics for antifungal stewardship
      allowing benchmarking of these programs; provide decision support for radiologist
      interpretation of chest imaging and improve reporting, audit and feedback practices in
      hospitals where these infections are managed.
Invasive fungal diseases (IFD) are rare infections that cause a life-threatening pneumonia in
      patients with weakened immune systems usually due to cancer chemotherapy and transplantation.
      Fungal spores are found in air, water and soil making exposure unavoidable in vulnerable
      patients. In developed countries, molds like Aspergillus are the most challenging type of IFD
      to diagnose and treat. These infections usually manifest as a culture-negative fungal
      pneumonia and account for approximately 300K of the 1.9M cases of IFD globally, but estimates
      are not accurate due to an absence of surveillance systems in hospitals where these
      infections are managed. Hospitals spend millions on antifungal drugs but are unaware of their
      patients affected, the effectiveness of their prevention efforts and hospital outbreaks may
      go unnoticed because surveillance, audit and feedback of fungal infections is not occurring.

      Optimising patient outcomes through timely diagnosis and appropriate prescribing of
      antifungal drugs is the goal of antifungal stewardship programs. Antifungal stewardship is of
      growing importance to hospitals world-wide because antifungal drugs are few in number,
      expensive to use and are associated with significant side-effects and drug interactions.
      Surveillance, audit and feedback are the cornerstones of antifungal stewardship programs that
      ensure patient care is meeting high standards. However, currently hospitals do not have the
      mechanisms to detect rare events like fungal infections because it usually presents as a
      pneumonia buried among hundreds of imaging scans.

      "fungalAi™" (fungalAi.com) is a technology based on artificial intelligence (Ai) that uses
      existing data in hospitals to make real time surveillance of fungal infections possible and
      assist radiologist interpretation of diagnostic imaging. fungalAi does this through:

        1. Natural language processing, a computational method of understanding human language.

        2. Deep learning based image analysis of diagnostic imaging and

        3. An expert system that integrates clinical data.

      What will be the impact?

      This project will provide hospitals with the mechanisms for performing real-time surveillance
      and audit of fungal infections in blood cancer patients through the innovative use of Ai.
      Strengthening antifungal stewardship through real-time surveillance of fungal diseases will
      improve patient care by revealing gaps in practice, new patient groups at risk for fungal
      infections and reduce inappropriate prescribing of antifungal medications through timely
      audit and feedback. The impact of this project will be:

        1. Improved diagnosis and recognition of fungal infections.

        2. Enhanced prevention.

        3. More appropriate use of antifungal medications.

      FungalAi is a scalable technology that will be validated against active manual surveillance
      of fungal infections in a multi-centre Australian clinical trial. The inclusive approach of
      fungalAi means that it is of value to many vulnerable patients including neglected groups
      like children who are included in this project. FungalAi is tuned for detection of fungal
      pneumonia caused by molds because these infections are more diagnostically challenging than
      other types of fungal infections. As a result, fungalAi leverages chest computed tomography
      imaging because it is a critical diagnostic test that is widely available and performed more
      frequently than invasive tests like lung washings or biopsy. Hence fungalAi natural language
      processing may miss very rare manifestations like brain infections. Nevertheless, automating
      detection of fungal pneumonia and improving radiologist recognition of a rare disease using a
      self-improving system based on neural networks is an important step towards improving the
      supportive care of patients with cancer. Improving outcomes in cancer is not only about
      finding a cure. Reducing the impact of infectious threats like fungal diseases is just as
      important and this can now be achieved by integrating artificial intelligence into patient
      care.
observational model: Cohort
time perspective: Prospective
measure: Accuracy of electronic surveillance using fungalAi natural language processing compared to active manual methods for detection of fungal pneumonia
time frame: 12 months
description: Sensitivity, specificity, ROC, Area under precision-recall curve of Ai assisted surveillance for fungal pneumonia using natural language processing of imaging reports compared to active manual surveillance
measure: Accuracy of disease classification of deep learning based image analysis for fungal pneumonia at scan level.
time frame: 12 months
description: Sensitivity, specificity, ROC of deep learning based image analysis at scan level compared to active manual surveillance.
measure: Accuracy of feature detection of fungal pneumonia using deep learning based image analysis of chest CT compared to radiologist expertise.
time frame: 12 months
description: Sensitivity, error rate (false positives, false negatives) at pixel level of deep learning based image analysis compared to radiologist labels.
measure: Accuracy of disease classification of an expert system integrating microbiology and antifungal drug prescriptions with text and image analysis compared to active manual surveillance.
time frame: 12 months
description: Sensitivity, specificity, ROC, Area under precision-recall curve of Ai assisted surveillance compared to active manual surveillance that will only be performed at Alfred Health.
arm group label: Fungal Cases
description: Patients with confirmed invasive fungal infections according to internationally accepted criteria identified by active manual surveillance. Clinical data will be sent to fungalAi platform technology for disease classification.
arm group label: Control patients
description: Patients without invasive fungal infections.Clinical data will be sent to fungalAi platform technology for disease classification.
intervention type: Combination Product
intervention name: fungalAi platform technology
description: Electronic surveillance and radiologic diagnosis of invasive fungal infections using fungalAi and associated methodologies.
arm group label: Control patients
arm group label: Fungal Cases
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: Michelle Dr Ananda-Rajah
role: Principal Investigator
affiliation: The Alfred
facility: 
country: Australia
responsible party type: Sponsor
mesh term: Mycoses
sharing ipd: Yes
ipd description: De-identified individual participant data will be made available in aggregate form for reports, presentations and publications.
ipd info type: Study Protocol
ipd info type: Statistical Analysis Plan (SAP)
ipd info type: Clinical Study Report (CSR)
ipd time frame: Data will be made available within 6-12 months after study completion.
ipd access criteria: Access to study protocol, SAP, CSR will be made publicly available. Access to IPD including individual labelled data will be reviewed by an external independent review panel to ensure that all ethical issues have been met.
ipd url: http://fungalai.com