Automating Radiotherapy Imaging Workflows
Unlocking the power of AI for Radiation Oncology, so you can focus on what matters most – patient care
Acceleration through automation
Mirada's RT solutions add the power of AI automation and optimization of the RT workflows to accelerate RT cancer care.
DLCExpert gives clinics the flexibility to choose from its AI Library of structures covering: head and neck, thorax, breast and prostate. Built using deep learning technology, DLCExpert saves time while providing OAR contouring consistency and accuracy
Quality, speed, and contour choice
Simple, Automated, AI Autocontouring
OAR Contours, drawn automatically using state of the art Deep learning technology, a branch of AI. AI Library of structures – head and neck, thorax, breast, and prostate. DLCExpert contours are built on hundreds of quality datasets, curated by clinical experts according to consensus guidelines. DLCExpert is configured by Mirada's clinical experts to your preferences
DLCExpert exploits the power of AI to empower clinicians
Flexibility to select structure combinations to suit your hospital preferences. Consistency & accuracy to immediately begin your contour quality review & one source of contour consistency across your regional geographical sites. Save time from manually intensive tasks to focus on the complex. Confidence that patient outcomes are at the heart and passion of our work and products.
What we promise and how we deliver
Robust scientific research and clinical validation Passion and commitment – visible through your appointed Clinical Customer Success representatives and through to our product and support teams Promise to work with you as a partner to innovate to accelerate cancer care
We have evaluated the use of DLCExpert models for delineation of head and neck cancer patients for treatment planning. In this study, CT based autocontouring models reduced contouring time by up to 63%, resulting in a significant reduction in workload for our clinicians, enabling them to spend more time with patients. Along with the autocontouring models reducing the time needed for contouring we have also seen a reduction in interobserver variability, improving the standardization of contours as well.
Consultant Clinical Scientist
The Clatterbridge Cancer Center
The collaboration between UMC Groningen and Mirada Medical has produced autocontouring models that encode according to consensus guidelines, established by an international panel of experts for organs at risk. These DLCExpert models are now deployed in our routine clinical workflows, forming a key part of our treatment planning for all patients with cancers in the head and neck or prostate regions and helping deliver improved outcomes.
University Medical Center Groningen (UMCG)
We have had great results with Mirada’s Head and Neck DLC Model. It saves us about 75% of our traditional contouring time
Chief Radiation Dosimetrist
University of New Mexico Cancer Center
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