New research published in Nature Communications shows how the machine learning tool, STimage, accurately predicted breast, skin and kidney cancers and a liver immune disease. It found the tool was reliable, low cost and rapidly generated results that were easy for pathologists to interpret.

The breakthrough could help deliver a new era of digital pathology and precision medicine, saving lives through faster and more accurate diagnoses, personalized treatments, and improved access to specialist expertise for patients in regional and remote areas.

“It’s like giving pathologists the super-resolution vision of Superman or Superwoman to scan millions of invisible biomarkers in a tiny tissue sample to find the two or three that are showing signs of cancer. This capability is critical for earlier detection, more precise diagnosis, and better-informed treatment decisions,” said Associate Professor Quan Nguyen, who led development of the tool with QIMR Berghofer’s National Center for Spatial Tissue and AI Research (NCSTAR).