Researchers from New York University (NYU) and NYU Abu Dhabi (NYUAD) report that they have developed a novel artificial intelligence (AI) system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images.
Breast cancer is the second most common cancer among women in the United States; as of January 2021, there are more than 3.8 million women with a history of breast cancer in the United States. Doctors often use ultrasound, mammograms, MRI, or biopsy to find or diagnose breast cancer.
Their findings are published in the journal Nature Communications and was led by Farah Shamout, PhD, NYUAD assistant professor emerging scholar of computer engineering and colleagues.
“Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates,” the researchers wrote. “In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images.”
In addition, to classifying the images, the researchers report the AI system also localizes the lesions in a weakly supervised manner.
“The AI system was developed and evaluated using the NYU Breast Ultrasound Dataset41 consisting of 5,442,907 images within 288,767 breast exams (including both screening and diagnostic exams) collected from 143,203 patients examined between 2012 and 2019 at NYU Langone Health in New York,” noted the researchers.
The primary goal of the AI system is to reduce the frequency of false-positive findings. It can detect cancer by assigning a probability for malignancy and highlight parts of ultrasound images that are 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.
“Our findings highlight the potential of AI to improve the accuracy, consistency, and efficiency of breast ultrasound diagnosis,” explained Shamout. “Importantly, AI is not a replacement for the expertise of clinicians. However, the powerful, complementary role that AI systems can play as a decision support tool leads us to believe that they should and will be increasingly translated into clinical practice.”
“In conclusion, we examined the potential of AI in U.S. exam evaluation. We demonstrated in a reader study that deep learning models trained with a sufficiently large amount of data are able to produce diagnoses as accurate as experienced radiologists. We further showed that the collaboration between AI and radiologists can significantly improve their specificity and obviate 27.8% of requested biopsies. We believe this research could supplement future approaches to breast cancer diagnosis,” the researchers wrote.