Artificial Intelligence more accurately identified breast cancer

An artificial intelligence (AI) tool—trained on roughly a million screening mammography images—identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists, a new study finds.

“AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI”

— Krzysztof J. Geras, PhD, Senior study author

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 programme, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images.

An AI tool learned to predict which lesions were likely malignant (red “heatmap”) or likely benign (green heatmap), with potential to aid radiologists in the diagnosis of breast cancer.

“Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa,” says senior study author Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Langone.

Image result for breast cancer

“AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI,” adds Dr. Geras, also an affiliated faculty member at the NYU Center for Data Science. “The ultimate goal of our work is to augment, not replace, human radiologists.”

A New Tool to Identify Breast Cancer

Modern AI approaches, inspired by the human brain, use complex circuits to process information in layers, with each step feeding information into the next, and assigning more or less importance to each piece of information along the way.

“The transition to AI support in diagnostic radiology should proceed like the adoption of self-driving cars”

— Nan Wu, a doctoral candidate at the NYU Center for Data Science

In addition, the researchers designed the study AI model to first consider very small patches of the full resolution image separately to create a heat map, a statistical picture of disease likelihood. Then the programme considers the entire breast for structural features linked to cancer, paying closer attention to the areas flagged in the pixel-level heat map.

Image result for breast cancer check

“The transition to AI support in diagnostic radiology should proceed like the adoption of self-driving cars—slowly and carefully, building trust, and improving systems along the way with a focus on safety,” says first author Nan Wu, a doctoral candidate at the NYU Center for Data Science.