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The company’s innovations focus on imaging solutions and artificial intelligence.
November 26, 2024
By: Rachel Klemovitch
Assistant Editor
Hologic will reveal new technologies for breast health at the 110th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA) in Chicago on December 1-5.
“There is tremendous excitement surrounding our latest breast health innovations, and our team is looking forward to showcasing the many ways we’re advancing solutions across the breast health continuum at this year’s RSNA,” said Erik Anderson, President of Breast and Skeletal Health Solutions at Hologic. “Through strong customer relationships, continuous improvement, and the latest research, we continue to take our leading technologies to the next level to support healthcare providers in improving the patient experience and providing excellent care.”
On the show floor and during multiple workshops, Hologic will showcase AI-powered breast imaging advancements designed to assess breast density, enhance workflows, and improve cancer detection.
RSNA attendees can experience the Envision Mammography Platform, offering patients a high-speed Hologic 3D mammogram with an industry-leading 2.5-second scan time.1* This is the first FDA-approved mammography technology of its kind.3 The Envision platform enhances the detection of subtle objects, minimizes focal spot blur, and improves 3D image sharpness.4,5,6*
This next-generation technology provides a more comfortable exam with tilt positioning, adapting to patients.2
Hologic will also introduce its next-generation Genius AI Detection PRO solution for use in the United States. This cancer screening technology expands on Hologic’s Genius AI Detection 2.0 solution by providing even greater accuracy and efficiency,7 giving radiologists more confidence and helping to reduce false positives.8
“With Genius AI Detection PRO, Hologic continues to lead the way in innovation for AI in breast imaging and cancer detection,” Anderson added. “By harnessing the latest advances in artificial intelligence and transformative workflow features, we continue to push the boundaries of science to revolutionize mammography and the future of women’s healthcare.”
Genius AI Detection PRO includes a deep learning 2D and 3D algorithm that improves specificity by using a patient’s prior exam in the current exam’s analysis. It also has an all-in-one interface featuring an intuitive 1-10 case and lesion scoring, automated lesion correlation, breast density scoring, and image quality checks.
The AI assistant optimizes the entire reading process, with the ability to read priors, and allows radiologists to read mammography exams more efficiently by reviewing key case information and pre-populating exam reports.9
The research, Performance of a Digital Breast Tomosynthesis AI Detection Algorithm in Common U.S. Racial/Ethnic Groups, evaluated the performance of Hologic’s Genius AI Detection 2.0 Solution from more than 7500 digital breast tomosynthesis (DBT) cases from women who identified as Asian, Black, Hispanic or White. Notably, researchers found that the measured performance of the algorithm was similar for all race/ethnicity cohorts that were evaluated.10
* Compared to the 3Dimensions TM Mammography System (3.7sec). DHM-05051_002.
1 Hologic data on file: VER-12082 (1.0).
2 Hologic data on file: DHM-16430.
3 FDA PMA Approval P080003/S009, 2024.
4 Hologic data on file: DHM-16158.
5 Hologic data on file: DHM-14517.
6 Smith, A. Tushita, P. Improving Tomosynthesis Image Quality using Advanced X-ray Technologies, Hologic WP-00300 (2024).
7 S. Pacilè, et al. (2024). Evaluation of a multi-instant multi-modal AI system supporting interpretive and noninterpretive functions. Accepted for publication in the Journal of Breast Imaging, https://doi.org/10.1093/jbi/wbae062.
8 K240301 510(k) summary distributed by Hologic, Inc.
9 S. Pacilè, et al. (2024). Evaluation of a multi-instant multi-modal AI system supporting interpretive and noninterpretive functions. Accepted for publication in the Journal of Breast Imaging, https://doi.org/10.1093/jbi/wbae062
10 S. M. Friedewald, et. al. (2024) Performance of a digital breast tomosynthesis AI detection algorithm in common US racial/ethnic groups
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