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Elements Spine SRS

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What is Ultromics?

The EchoGo Heart Failure platform is transforming the identification of heart failure by utilizing artificial intelligence to improve the accuracy of detecting HFpEF through echocardiograms. In collaboration with the Mayo Clinic, this groundbreaking technology streamlines the detection process, achieving impressive precision from a single apical 4-chamber view, thereby overcoming longstanding challenges in diagnosing this condition. As the first AI-centric platform specifically focused on HFpEF detection in echocardiography, Ultromics establishes itself as a pioneer in the integration of AI for cardiac imaging solutions. Our cutting-edge technology not only facilitates earlier and more effective identification of heart failure but also aspires to enhance patients' overall quality of life. By offering a user-friendly and secure cloud-based Software-as-a-Service (SaaS) platform, Ultromics effortlessly integrates with current echocardiography systems, equipping labs with essential tools for accurate heart failure detection and efficient risk assessment. This innovation holds the potential to substantially reduce hospitalization rates associated with heart failure, providing significant advantages for both healthcare systems and patients. As we strive to push the boundaries of technology, our unwavering dedication is to advance cardiac care and improve patient outcomes. Furthermore, we aim to continually refine our services to meet the evolving needs of healthcare professionals and enhance patient experiences.

What is Elements Spine SRS?

Brainlab's Elements Spine Stereotactic Radiosurgery (SRS) is an advanced software platform designed to optimize the treatment of spinal metastases. The workflow is characterized by its automation in every stage, which includes detailed anatomical mapping, adjustments for spinal curvature, and precise target identification, ensuring exceptional accuracy and consistency at submillimeter precision. A unique algorithm effectively addresses differences in spinal curvature, enhancing the reliability of image fusion. The system utilizes a patented synthetic tissue model for automatic segmentation, allowing for the accurate identification and labeling of various spinal levels essential for dose calculations. Additionally, tools for defining Gross Tumor Volume (GTV) contours are provided, along with automated recommendations for Clinical Target Volume (CTV) and a cropped spinal canal object that complies with International Spine Consortium Guidelines. The incorporation of AI-driven contouring features facilitates the rapid and accurate delineation of more than 200 anatomical structures, including lymph nodes. This technological development not only simplifies the treatment workflow but also significantly improves patient outcomes in the management of spinal cancer, reflecting a major leap forward in oncological care. As a result, healthcare professionals can deliver more tailored and effective treatment strategies for patients grappling with this challenging condition.