Resonance Health Ltd (ASX: RHT) has received CE mark for HepaFat-AI, the company’s fully automated artificial intelligence (AI) software that assesses liver fat.
CE marking signifies that the Device is compliant with the requirements of Medical Device Directive 93/42/EEC for CE Marking and is registered for sale within the European Economic Area (EEA).
The company obtained United States Food & Drug Administration (FDA) clearance for the Device in December 2020 and Australian Therapeutic Goods Administration (TGA) approval in February 2021.
Collectively, these clearances allow the company to lawfully distribute and supply the Device into Australia, the EEA, and the United States of America (and their territories).
HepaFat-AI automatically analyses magnetic resonance imaging (MRI) datasets to assess liver fat in patients, providing doctors with a comprehensive, multi-metric solution for use in the assessment of individuals with confirmed or suspected fatty liver disease.
HepaFat-AI assesses these images and provides critical information on the resultant patient report. The treating physician can then use this information to: monitor patients undergoing weight loss management; to screen the livers of live donors for transplant suitability; monitor patients with or suspected to have NAFLD or the more serious subtype, non-alcoholic steatohepatitis (NASH); drug induced fatty liver; pancreatic insufficiency.
The company will market HepaFat-AI to radiologists and physicians involved in the routine clinical diagnosis and management of patients with confirmed or suspected fatty liver disease. Clinicians and radiologists will soon be able to access HepaFat-AI via Resonance Health’s own cloud-based portal. The company is also assessing the use of radiology-based channel partners for direct route to market.
HepaFat-AI will also be marketed to pharmaceutical companies engaged in NASH drug development due to the highly standardised and reproducible nature of the AI solution. As HepaFat-AI is validated for all the major MRI scanner makes and models, it is ideally suited for these purposes, particularly in NASH multi-center trials which require standardised workflows to ensure clinically meaningful data is generated.
Moreover, as HepaFat-AI can deliver data in near real-time, investigators can respond rapidly if adaptive trial protocols are implemented, which is particularly advantageous in recently designed NASH studies. Additionally, HepaFat-AI’s unique capability of reporting a ‘pathologist’s equivalent’ steatosis grade could help bridge the gap caused by the reportedly high screening failure rates (up to 50%) as evidenced by liver biopsy in patients recruited to NASH trials.
