First-Ever Breast Cancer AI for Mammography Scans Shows How It Comes To Conclusions

A new artificial intelligence (AI) tool for mammography scans aims to aid rather than replace human decision-making.

Computer engineers and radiologists at Duke University (Durham, NC, USA) have developed an AI platform to analyze potentially cancerous lesions in mammography scans to determine if a patient should receive an invasive biopsy. But unlike its many predecessors, this algorithm is interpretable, meaning it shows physicians exactly how it came to its conclusions.

The researchers trained the AI to locate and evaluate lesions just like an actual radiologist would be trained, rather than allowing it to freely develop its own procedures, giving it several advantages over its “black box” counterparts. It could make for a useful training platform to teach students how to read mammography images. It could also help physicians in sparsely populated regions around the world who do not regularly read mammography scans make better health care decisions.

The researchers trained the new AI with 1,136 images taken from 484 patients at Duke University Health System. They first taught the AI to find the suspicious lesions in question and ignore all of the healthy tissue and other irrelevant data. Then they hired radiologists to carefully label the images to teach the AI to focus on the edges of the lesions, where the potential tumors meet healthy surrounding tissue, and compare those edges to edges in images with known cancerous and benign outcomes. Radiating lines or fuzzy edges, known medically as mass margins, are the best predictor of cancerous breast tumors and the first thing that radiologists look for. This is because cancerous cells replicate and expand so fast that not all of a developing tumor’s edges are easy to see in mammograms.

After training was complete, the researches put the AI to the test. While it did not outperform human radiologists, it did just as well as other black box computer models. When the new AI is wrong, people working with it will be able to recognize that it is wrong and why it made the mistake. Moving forward, the team is working to add other physical characteristics for the AI to consider when making its decisions, such as a lesion’s shape, which is a second feature radiologists learn to look at.

“This is a unique way to train an AI how to look at medical imagery,” said Alina Barnett, a computer science PhD candidate at Duke and first author of the study. “Other AIs are not trying to imitate radiologists; they’re coming up with their own methods for answering the question that are often not helpful or, in some cases, depend on flawed reasoning processes.”

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