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| Artificial Intelligence learned which lesions were likely malignant (red) and which were likely benign (green) – Credit NYU School of Medicine |
Led by researchers from NYU School of Medicine and the NYU Center for Data Science, the study examined the ability of a type of AI, a machine learning computer program, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images.
"AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI," study author Dr. Geras said in a press release. "The ultimate goal of our work is to augment, not replace, human radiologists."
The research team used seven years of analyzed images that had been collected as part of routine clinical care at NYU Langone Health, sifting through the collected data and connecting the images with biopsy results. The result was an extraordinarily large dataset for their AI tool to train on consisting of 229,426 digital screening mammography exams and 1,001,093 images.
In the new study, published online recently by the journal IEEE Transactions on Medical Imaging, the research team designed statistical techniques that let their program "learn" how to get better at identifying anomalies without being told exactly how. Rather than have the researchers identify image features for their AI to search for, the tool is discovering on its own which image features increase prediction accuracy.
Moving forward, the team plans to further increase this accuracy by training the AI program on more data, perhaps even identifying changes in breast tissue that are not yet cancerous but have the potential to be.
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