While the algorithm did not outperform human radiologists, it did just as well as other black box computer models. The difference was when the algorithm was wrong, people using it were able to recognize its error and see why it made a mistake.

The team is working to make the platform take into account other physical characteristics in its decision-making. One is lesion shape, which is the second feature that radiologists look for. They also are beginning to expand its use to CT imaging.


“This AI platform should be applicable to most imaging tasks, thanks to its explainability,” said co-author Dr. Fides Regina Schwartz, research fellow in radiology at Duke. “There are many valuable targets but ideally, we would next apply it to other entities that have clear classification systems already in place (e.g., prostate cancer with PI-RADS, lung cancer with LUNG-RADS, liver cancer with LI-RADS).”

The university has provided a Duke MEDx High-Risk High-Impact Award to continue development of the algorithm and to conduct a radiologist reader study on its clinical performance and confidence.

Supporting the research are the National Institutes of Health/National Cancer Institute; MIT Lincoln Laboratory; Duke TRIPODS; and the Duke Incubation Fund.

The findings were published in Nature Machine Intelligence.

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