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What is fungalAi?

fungalAi is a platform technology with a suite of data analytics that can detect and help diagnose fungal infections from data that is readily available in clinical information systems. Natural language processing screens CT reports for the suggestion of fungal disease taking into account the variation in radiologist language; image analysis marks up suspicious features on Chest CT and the expert system integrates radiology, antifungal prescribing and microbiology to improve fungal prediction.

How does it work?

fungalAi uses artificial neural networks to analyse the language of chest CT reports and recognise features of fungal infections on chest CT scans. The expert system uses hand crafted rules and machine learning classifiers to integrate different types of data when uncertainty in fungal prediction prevails.

Why use this approach?

Diagnosis of fungal infections initially relies on chest imaging because the overwhelming majority of patients develop a pneumonia and fungal organisms are difficult to recover from sick patients. However, results of chest imaging are dictated by how do you sift through a deluge of reports to locate a rare disease? Our sensitive analytics can help.

fungalAi achieved a sensitivity at report level of 91%, specificity of 79%, ROC 0.92 and detected 100% of patients with fungal infections. Our deep learning based image analysis of chest CT scans has an AUC of 99.3. Our expert system combining radiology, antifungal drug prescriptions and microbiology maintains a very high sensitivity of 98%, specificity of 75% and halved false notifications.


fungalAi has helped us understand our local epidemiology, patient outcomes and improve our antifungal stewardship practice.

What about you?


Antifungal stewardship
Take a data driven approach to managing the use of antifungal drugs with fungalAi
Antifungal stewardship
Take a data driven approach to managing the use of antifungal drugs with fungalAi. 
Radiologic diagnosis
Deep learning based image recognition can assist radiologists diagnose fungal infections. 
Know your patients & environment
Understand your institutional epidemiology. Monitor trends in real time.
Uncover new risk groups
Which patients are at risk for fungal infections? Challenge your thinking
Clinical trials
A standardised method of fungal diagnosis and detection.
Patient risk & prophylaxis 
How can up to date data better individualise risk and tailor preventative medication?
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