In the Largest Study, AI Found To Increase Efficiency of Breast Cancer Screening -- When Combined with Human Intelligence

Image credit: Valerii Apetroaiei, iStock

In the largest study of its kind, evaluating mammograms from more than 100,000 women in Germany, including more than 4,400 with identified cancers, Vara evaluates an approach called decision referral. Vara’s approach of integrating artificial intelligence into breast cancer screening could significantly reduce workloads, complementing the work of radiologists and increasing sensitivity and specificity. Using some algorithm configurations, over 70% of the workload could be automatically triaged by the AI  The technology also comes with a safety net, triggered only when AI disagrees with the reader, flagging potentially missed cancers. The study finds that in contrast to traditional computer aided detection (CAD) systems which have struggled with increased false positives, Vara’s decision referral approach may prove to be the optimal use of AI in breast cancer scanning.

The study was a collaboration between breast radiologists Dr. Lale Umutlu of the Essen University Hospital, Dr. Katja Pinker of Memorial Sloan Kettering Cancer Center, and researchers from Vara, led by Machine Learning Director, Dr. Christian Leibig. 

Not a replacement, but an enabler

The study, published in The Lancet Digital Health and commissioned by deeptech firm Vara, evaluated the performance of an AI-based approach using mammograms from more than 100,000 women in Germany, including more than 4,400 with identified cancers. Radiologists and researchers found that when AI is implemented along the screening pathway in a way that complements, rather than replaces human radiologists both sensitivity and specificity of radiologists improve significantly. Different algorithm configurations were evaluated, with some demonstrating that over 70% of the workload could be automatically triaged by the AI.