21 Oct ACG 2022 Annual Scientific Meeting, Charlotte, North Carolina

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New Artificial Intelligence Tool for Automated Assessment of Liver Fat

21 November, 2019

Resonance Health is pleased to announce that it has successfully trained a neural network (artificial intelligence, ‘AI’) for the automated assessment of liver fat from MRI data.

The neural network, which has been trained and validated using in-house HepaFat-Scan® datasets, is intended to provide a fully automated solution for the assessment of liver fat, and to be integrated directly into existing radiology workflows.  

The new AI tool uses standardised magnetic resonance images (acquired using the HepaFat-Scan® protocol) and performs a fully automated analysis to deliver results in near to real time.  The newAI assessment tool has high diagnostic ability at all clinical thresholds, as demonstrated in an unpublished validation study recently completed by the Company.   

The new AI tool (yet to be named by the Company) should be suitable to assess liver fat in patients with Non-alcoholic Fatty Liver Disease (NAFLD) and Nonalcoholic Steatohepatitis (NASH).  It is believed that NAFLD affects 10-30% of the global population of 7.6 billion people. Of those affected people, it is estimated that around 25% will develop liver inflammation (NASH), and around 5% will develop cirrhosis.

It is predicted that by 2020, NAFLD will be the leading cause of liver transplants globally.  In these instances, the Company’s new AI tool could be used by clinicians as an aid to diagnose and manage the treatment of these conditions.

The next step in the commercialisation of the new AI tool is the preparation of a dossier for regulatory clearances.  The Company has commenced work on this and hopes to be in a position to submit an application for regulatory approval by the end of the first quarter of 2020. 

Additional work in the application of AI to other organs and disease conditions continues, with the Company’s focus on developing and deploying cutting-edge assistance tools for clinicians and radiologists to aid with the diagnosis and management of patients.

Further updates will be provided as work progresses.