After calculating the model’s accuracy in predicting pathologic margins, researchers compared that data to the typical accuracy of human interpretation and discovered that the AI model performed as well as humans, if not better.

“It is interesting to think about how AI models can support doctor’s and surgeon’s decision making in the operating room using computer vision,” said first author Kevin Chen, MD, general surgery resident in the Department of Surgery. “We found that the AI model matched or slightly surpassed humans in identifying positive margins.”


According to Gallagher, the model can be especially helpful in discerning margins in patients that have higher breast density. On mammograms, higher density breast tissue and tumors appear as a bright white color, making it difficult to discern where the cancer ends and the healthy breast tissue begins.

Similar models could also be especially helpful for hospitals with fewer resources, which may not have the specialist surgeons, radiologists, or pathologists on hand to make a quick, informed decision in the operating room.

“It is like putting an extra layer of support in hospitals that maybe wouldn’t have that expertise readily available,” said Shawn Gomez, EngScD, professor of biomedical engineering and pharmacology and co-senior author on the paper. “Instead of having to make a best guess, surgeons could have the support of a model trained on hundreds or thousands of images and get immediate feedback on their surgery to make a more informed decision.”

Since the model is still in its early stages, researchers will keep adding more pictures taken by more patients and different surgeons. The model will need to be validated in further studies before it can be used clinically. Researchers anticipate that the accuracy of their models will increase over time as they learn more about the appearance of normal tissue, tumors, and margins.

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