Hologic Unveils Revolutionary AI Research at ECR 2024
Hologic, a global leader in women’s health, is set to showcase groundbreaking research and educational initiatives at the European Congress of Radiology (ECR) 2024 in Vienna, Austria. The event, scheduled from February 28 to March 2, will feature the latest advancements in artificial intelligence (AI) aimed at enhancing workflow efficiency and patient care.
The company, renowned for its pioneering work in 3D mammography, will unveil new data demonstrating the efficacy of its Genius AI Detection solution in aiding breast cancer detection and streamlining radiologists’ workflow. Dr. Sarah M. Friedewald, a distinguished breast imaging specialist, will present the research findings, highlighting the solution’s ability to accurately match Regions of Interest (ROIs) across different views.
Dr. Friedewald expressed optimism about the transformative potential of AI in radiology, emphasizing its role in improving patient outcomes and driving advancements in breast health care. The research underscores the value of AI technology in complementing radiologists’ workflow and enhancing diagnostic accuracy.
In addition to the AI research, Hologic will co-sponsor a symposium on contrast-enhanced mammography (CEM) with Bayer, featuring prominent experts in breast imaging. The symposium aims to educate radiologists on the effective implementation of CEM in daily practice, further enhancing diagnostic capabilities and patient care.
Hologic’s commitment to innovation and collaboration underscores its dedication to supporting healthcare providers in delivering high-quality patient care. The company’s Genius AI Detection technology, already available in the U.S., is poised for global expansion, with plans for rollout in Europe, Canada, and Asia by the end of 2024.
Stay tuned for more updates on Hologic’s groundbreaking research and advancements in women’s health at ECR 2024!
References:
- Correlating breast lesions in tomosynthesis CC and MLO views using artificial intelligence (AI).
- Evaluating performance of an artificial intelligence (AI) detection system on prior screening tomosynthesis studies of breast cancer patients.
- Performance of a traditional machine learning computer-aided detection (CADe) algorithm versus a deep learning artificial intelligence (AI) algorithm on digital breast tomosynthesis (DBT) studies.
