"We are using the power of artificial intelligence to address an age-old problem at the intersection of surgery and pathology," said the corresponding author of the study Faisal Mahmood, PhD, of the Division of Computational Pathology at US-based Brigham and Women's Hospital.

"Making a rapid diagnosis from frozen tissue samples is challenging and requires specialized training, but this kind of diagnosis is a critical step in caring for patients during surgery," he added.

To make final diagnoses, pathologists use formalin-fixed and paraffin-embedded (FFPE) tissue samples -- this method preserves tissue in a way that produces high-quality images but is labor-intensive and can take several days, according to the study.

Mahmood and co-authors developed a deep-learning model that can be used to translate between frozen sections and more commonly used FFPE tissue.