Clinical trial NCT03793231: fungalAi for Fungal Surveillance & Antifungal Stewardship
Official title: Innovative Use of fungalAi for Antifungal Stewardship in Haematology-oncology Patients
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Fungal Infections
Keywords: Fungal infections
Deep learning
Electronic surveillance
Antifungal stewardship
Image classification
Lead sponsor: Bayside Health
Sponsor class: OTHER_GOV
Collaborators: name: Monash University
name: Monash Health
name: Eastern Health
name: Western Sydney Local Health District
name: SA Health
name: Fremantle Hospital and Health Service
name: Singhealth Foundation
First posted: 2019-01-04
Enrollment: 1000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adults and children with blood cancers under the haematology service at participating sites inclusive of inpatient and ambulatory care patients.

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

* Adults and children
* Under the haematology service at participating sites
* Inpatient and ambulatory patients.

Exclusion Criteria: No exclusion criteria

Primary outcomes:
measure: Accuracy of electronic surveillance using fungalAi natural language processing compared to active manual methods for detection of fungal pneumonia
timeFrame: 12 months
Secondary outcomes:
measure: Accuracy of disease classification of deep learning based image analysis for fungal pneumonia at scan level.
timeFrame: 12 months
measure: Accuracy of feature detection of fungal pneumonia using deep learning based image analysis of chest CT compared to radiologist expertise.
timeFrame: 12 months
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.
timeFrame: 12 months
Other outcomes:
Locations:
city: Melbourne
country: Australia
facility: Alfred Health
state: Victoria
Study officials:
affiliation: The Alfred
name: Michelle Dr Ananda-Rajah
role: PRINCIPAL_INVESTIGATOR
References: