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:
Natural language processing, a computational method of understanding human language.
Deep learning based image analysis of diagnostic imaging and
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:
Improved diagnosis and recognition of fungal infections.
Enhanced prevention.
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.