When a patient undergoes a surgical operation to remove a tumor or treat a disease, the course of surgery is often not predetermined. To decide how much tissue needs to be removed, surgeons must know more about the condition they are treating, including a tumor’s margins, its stage and whether a lesion is malignant or benign— determinations that often hinge upon collecting, analyzing, and diagnosing a disease while the patient is on the operating table. When surgeons send samples to a pathologist for examination, both speed and accuracy are of the essence. The current gold-standard approach for examining tissues often takes too long and a faster approach, which involves freezing tissue, can introduce artifacts that can complicate diagnostics. A new study by investigators from the Mahmood Lab at the Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system, and collaborators from Bogazici University developed a better way; the method leverages artificial intelligence to translate between frozen sections and the gold-standard approach, improving the quality of images to increase the accuracy of rapid diagnostics. Findings are published in Nature Biomedical Engineering.
