Singapore-based medtech/healthcare company HistoIndex has entered into a new partnership with Virginia Commonwealth University’s (VCU) Stravitz-Sanyal Institute for Liver Disease and Metabolic Health.
As part of the new collaboration, the two organisations will work together to advance machine learning/artificial intelligence (ML/AI)-based technology to support the study of liver diseases.
According to HistoIndex, one of its imaging systems has also been commissioned at VCU’s Molecular Medicine Research Building in Richmond, Virginia, to commence associated work under this joint initiative.
Referred to as ‘Genesis 200 Second Harmonic Generation/Two-Photon Excitation (SHG/TPE)’, the imaging system and its ML/AI solution is an automated, stain-free, completely quantitative, multi-organ digital pathology platform.
HistoIndex’s stain-free digital pathology system is used for carrying out preclinical studies and clinical trials in different areas, including drug development for treating non-alcoholic steatohepatitis (NASH).
Arun J Sanyal, VCU liver institute director and professor of medicine, said: “SHG/TPE imaging represents an important addition to the digital pathology toolkit for assessment of liver disease.
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“We are excited and keen to further leverage this in combination with emerging spatial transcriptomics and other technologies to better understand the evolution of the liver disease and develop improved tools for disease assessment and precision therapeutics.”
The new collaborative effort will see HistoIndex and VCU’s Stravitz-Sanyal Institute for Liver Disease and Metabolic Health work in coordination with the US Food and Drug Administration to attain in-vitro diagnostic approval for the Genesis SHG/TPE digital pathology platform.
With this approval, the digital pathology system will be eligible for use as an ‘assistive tool’ for the staging of fibrosis in NASH, according to HistoIndex.
For this partnership effort, VCU’s liver institute will act as an international centre for performing SHG/TPE life science research, imaging of unstained tissues from various collaborators worldwide and applying new ML/AI-based technology for these images.