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Volpara Health Surpasses 300 Peer-Reviewed Papers

The need for robust, peer-reviewed validation in mammography AI is essential to its clinical adoption. A review of 100 CE-marked AI products in radiology revealed that 64% lacked peer-reviewed evidence of efficacy. Volpara, in contrast, leads the mammography AI industry with the most peer-reviewed scientific papers, demonstrating its innovative technologies are rigorously tested, and clinically validated. The need for robust, peer-reviewed validation in mammography AI is essential to its...
WELLINGTON, New Zealand, (informazione.it - comunicati stampa - information technology)

The need for robust, peer-reviewed validation in mammography AI is essential to its clinical adoption. A  review of 100 CE-marked AI products in radiology revealed that 64% lacked peer-reviewed evidence of efficacy. Volpara, in contrast, leads the mammography AI industry with the most peer-reviewed scientific papers, demonstrating its innovative technologies are rigorously tested, and clinically validated.

"Reaching 300 studies reflects the deep trust our technology has earned within both the academic and clinical communities," said Teri Thomas , CEO of Volpara Health and CBO of Lunit. "Our commitment to peer validation provides important evidence of our AI solutions' ability to deliver measurable improvements in detection and patient outcomes."

Two recent studies showcase the benefits of Volpara's AI technology.

, shows how ordering mammograms based on breast density can improve radiologists' performance. By arranging mammograms from low to high volumetric breast density (VBD), radiologists reduced reading time and increased specificity from 86% to 89%, leading to greater accuracy in detecting cancer. Commenting in an editorial on the study , Dr. Lars Grimm explained, "Deliberate ordering of screening mammograms could be introduced into workflows very easily because it dovetails with existing batch-reading strategies."

, used Volpara's AI-powered algorithms to determine how breast density affects cancer detection in the UK. The study confirmed that Volpara's technology effectively identifies women at higher risk for missed cancers on mammography, reinforcing the importance of personalized screening strategies.

Melissa Hill , PhD, Senior Imaging Scientist and Global Research Lead at Volpara Health, emphasized the significance of scientific rigor: "Our research collaborations and commitment to innovation have brought us to this important milestone. Each published paper puts our technology to the test, and robust results strengthen the trust clinicians place in Volpara to help improve patient care."

The company's journey from its first to its 300th peer-reviewed paper reflects over 15 years of innovation and investment in research. Volpara remains committed to pushing the boundaries of AI to improve breast health.

For more information about Volpara Health and new research to be presented at RSNA 2024, visit volparahealth.com/rsna2024 .

About Volpara Health

Volpara Health is on a mission to save families from cancer with AI-powered software that helps healthcare providers better understand cancer risk, guide personalized care decisions, and recommend additional imaging and interventions. Used in over 3,500 facilities by more than 5,600 technologists worldwide. Volpara's software impacts nearly 18M patients, supports over 3.6M annual cancer risk assessments, and integrates seamlessly with electronic health records and imaging systems. Volpara helps radiologists quantify dense breast tissue with precision and technologists produce mammograms with optimal positioning, compression, and dose. Volpara software also streamlines operations to ease compliance and accreditation. Volpara, a Lunit company, is headquartered in Wellington, New Zealand , and has an office in Seattle . Volpara is the trusted partner of leading healthcare institutions globally. For more information, visit   www.volparahealth.com .

View original content: https://www.prnewswire.co.uk/news-releases/volpara-health-surpasses-300-peer-reviewed-papers-302286144.html

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