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AI improves breast cancer detection and reduces false positives

NYU researchers develop an artificial intelligence tool to improve the accuracy of breast cancer imaging

Doctors often use mammograms, MRIs, ultrasound, or biopsy to find or diagnose breast cancer but these methods have a high rate of false-positive findings. Researchers from NYU and NYU Abu Dhabi (NYUAD) have developed a novel AI that’ll improve breast cancer detection in ultrasound images, achieving radiologist-level accuracy. 

The study titled “Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams,” was published in Nature Communications. The study was led by Farah Shamout, Ph.D., NYUAD assistant professor and emerging scholar of computer engineering and colleagues.

Breast ultrasound has high false-positive rates. The AI system developed by researchers achieves radiologist-level accuracy in classifying the images and identifying breast cancer in ultrasound images. This AI model will also reduce the frequency of false-positive findings. It also localizes the lesions in a weakly supervised manner.

Read more: AI-aided Surveillance Cameras under the ‘Safe Kerala’ Project

NYU Breast Ultrasound Dataset41 was used to develop and evaluate 5,442,907 images within 288,767 breast exams. The images included screening and diagnostic exams collected from 143,203 patients examined between 2012 and 2019 at NYU Langone Health in New York.

This AI detects breast cancer in women by assigning a probability for malignancy and highlighting parts of ultrasound images associated with its predictions. When the researchers conducted a reader study to compare its diagnostic accuracy with board-certified breast radiologists, the system achieved higher accuracy than the ten radiologists on average.

However, a hybrid model that aggregated the predictions of the AI system and radiologists achieved the best results in accurately detecting cancer in patients.

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Osheen Jain
Osheen is an experienced content writer who is interested in the intersection of robotics and Embodied Cognition. She has a PGD in Cognitive Science, a Master's in Philosophy, and enjoys reading immensely.

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