iCAD has showcased its Breast Health Solutions suite at the Radiological Society of North America Annual Meeting annual meeting, held during December 2019. This included the technology ProFound AI for Digital Breast Tomosynthesis (DBT), which is said to be the first artificial intelligence software for DBT to be approved by the U.S. Food and Drug Administration (FDA).
Also on offer at the event were medical software solutions designed for 2D mammography and to assess breast density. During the meeting, the iCAD unveiled its vision for future technologies. This predictive aspect included technologies that should enable clinicians to more easily interpret patients’ earlier images and prospective breast cancer risk assessment to form a clearer picture of the specific patient’s condition.
Clinical data from a large reader study involving ProFound AI for DBT were recently published in the journal Radiology: Artificial Intelligence (“Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis”). The experiential data indicates that ProFound AI for DBT improves radiological sensitivity by 8 percent, and this can lower unnecessary patient recall rates by around 7.2 percent.
According to Michael Klein, Chairman and Chief Executive Officer of iCAD: “ProFound AI has extensive potential in the realm of clinical AI – and in the future it will offer a more holistic clinical approach that can provide clinicians with a broader view of each individual patient’s case, history, and short-term risk.”
He adds that: “ProFound AI offers clinically-proven improvements in sensitivity, specificity, time-savings and recall rates, but our panoramic vision for the future will include innovative tools that allow physicians to correlate findings across time and provide more tailored and personalized patient care.”
In addition, the technology has been shown to cut reading time for radiologists by an average of 52.7 percent. Additionally, ProFound AI has been shown to be capable of reducing medical image reading time by up to 57.4 percent (for women with dense breasts).
These data suggest an improvement in accuracy and reading times for traditional 2D mammography.
The technology is a further sign of developments with technologies designed to provide real-world experiences and clinical perspectives for breast health screening, made possible by artificial intelligence. Such technologies are significantly disrupting the healthcare and medical fields.
