The Use of Artificial Intelligence to Help Reduce False Positives

According to statistics from breastcancer.org, about 1 in 8 women in the US (around 13%) will develop invasive breast cancer over the course of her lifetime. In 2022 alone, an estimated 287,850 new cases of invasive breast cancer are expected to be diagnosed in women in the US, along with 51,400 new cases of non-invasive (in situ) breast cancer. About 43,250 of these women are expected to die from breast cancer. As for men, about 2,710 new cases of invasive breast cancer are expected to be diagnosed in 2022. A man’s lifetime risk of breast cancer is about 1 in 833.

The American Cancer Society reports that since 2007, breast cancer death rates have been steady in women younger than 50, but have continued to decrease in older women. From 2013 to 2018, the death rate went down by 1% per year, which is thought to be the result of earlier cancer screening detection and technological improvements. This number shows the power of early detection through screening, early diagnosis and treatment. It is no surprise that artificial intelligence (AI) is starting to be implemented into digital breast imaging technology and it shows promise in both interpretive and noninterpretive tasks.

Findings from a clinical trial that used artificial intelligence in an effort to reduce false positives on breast ultrasound were presented by Linda Moy, MD, Center for Advanced Imaging Innovation and Research with NYU Langone Health Center for Advanced Imaging Innovation and Research (CAI2R), during RSNA 2021. Researchers working on an initiative supported by the US National Science Foundation trained AI to identify breast cancer using data obtained from previously conducted ultrasounds. The AI tool significantly increased accurate diagnoses. The findings, and an analysis of this important study, can be found in the article “Artificial Intelligence System Reduces False-Positive Finding in the Interpretation of Breast Ultrasound Exams” on page 30 of this issue. The sidebar to this article also summarizes how, in a published study, AI decision support decreased unnecessary biopsies and follow-up exams because the system could be harnessed to support decision-making where there are shortages of radiologists.

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