Breast Cancer Screening Machine Learning Software Receives CE Mark

Deep learning uses powerful artificial neural networks and high performance computing to analyze complex medical images with precision. Kheiron’s breast cancer screening software aims to facilitate more efficient and accurate breast cancer detection at an earlier stage of the disease and reduce the number of women incorrectly subjected to invasive biopsies, unnecessary radiation and detect more cancers at the crucial early stages. In an independent multi-center clinical study to evaluate its performance prior to submission for CE approval, the deep learning- software demonstrated indications of performance above the average national benchmarks for breast screening radiologists.

With the CE regulatory approval, health care providers in Europe will now be able to use Kheiron’s breast cancer screening software as a second reader of mammographic images in a breast cancer screening setting. Clinicians will be able to receive results within seconds, directly into their existing workflows, incorporating case-wise recall decision support and lesion localization. By using the software as a second reader, the screening workload of overstretched clinical staff in all screening settings will potentially reduce, allowing them to focus on other more complex modalities and tasks. Additionally, the CE marking will allow for intelligent triaging of imaging studies prior to review, enabling radiologists to prioritize studies based on the algorithm’s findings.

“With UK radiology workforce shortages in the spotlight, regulatory approval could actually position the UK and Europe as the world’s earliest adopters of AI at scale,” said Kheiron’s Chief Medical Officer, Dr. Christopher Austin. “European national breast screening programs can now take the lead and demonstrate how to leverage high quality, proven, and safe machine learning technologies to run screening programs better, smarter and more cost-effectively for millions of women.”

“These are exciting times for the breast imaging community and in particular breast screening,” said Dr Nisha Sharma, Consultant Breast Radiologist, Director of Breast Screening & Clinical Lead for Breast Imaging at Leeds University Hospitals Trust, and Secretary for the British Society of Breast Imaging. “Software such as this will have a significant impact in managing the global workforce shortage in mammography by augmenting single reading and ensuring that the sensitivity and specificity of double reading are at least maintained and potentially improved. This would reinforce the aim that deep learning tools are there to support the clinician and work alongside them.”

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