RaySearch Laboratories AB

http://www.raysearchlabs.com
+46 (0)8 510 530 00

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About RaySearch Laboratories AB

ADVANCING CANCER TREATMENT

Raysearch is a committed pioneer of oncology software. Since 2000, we have worked in close cooperation with leading centers to improve life and outcomes for patients. We develop all our products from the ground up and continuously revise every aspect, from algorithms to user interface designs. Medical science never stands still, and neither does RaySearch—our relentless drive to do things better leads us to ever-higher performance, accuracy, safety and usability. And this is just the beginning.

We believe software is the driving force for innovation in oncology today. Our systems use groundbreaking automation and machine learning to create new possibilities. RayCare*, the next-generation oncology information system, will enable one workflow for all the oncology disciplines, ensuring fluid coordination of tasks and optimal use of resources. RayStation harmonizes treatment planning, providing one point of control for all planning needs—any equipment, any scale.

* Subject to regulatory clearance in some markets.

 

 

 

MULTI-TECHNOLOGY RADIATION THERAPY FOR PERSONALIZED CARE AT MIAMI CANCER INSTITUTE

EFFICIENT CARE THROUGH THE IMPLEMENTATION OF VMAT AND SCRIPTING WITH RAYSTATION

STREAMLINED TREATMENT PLANNING AT NCCC

A COMPREHENSIVE APPROACH TO LUNG TREATMENT AT TEXAS CENTER FOR PROTON THERAPY

 

RayStation

Machine Learning

RayPlan

RayCare

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RayStation

RayStation is a flexible, innovative treatment planning system, chosen by many of the leading cancer centers worldwide. It combines unique features such as unmatched adaptive therapy capabilities, multi-criteria optimization, market-leading algorithms for IMRT and VMAT optimization with highly accurate dose engines for photon, electron, proton and carbon ion therapy. RayStation supports a wide range of treatment machines, providing one control center for all treatment planning needs and ensuring centers get greater value from existing equipment. RayStation also seamlessly integrates with RayCare, the next-generation oncology information system. By harmonizing the treatment planning, we enable better care for cancer patients worldwide.

* Subject to regulatory clearance in some markets.

RayStation

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RayCare is designed to support the complex logistical challenges of modern oncology clinics. It represents the future of oncology information system technology, supporting the vision of one oncology workflow. Many cancer patients receive a combination of treatment types, and RayCare is designed to reflect that. It will efficiently coordinate activities in radiation therapy, chemotherapy and surgery and will offer advanced features for clinical resource optimization, workflow automation and adaptive radiation therapy. RayCare is being developed with tomorrow’s requirements for advanced analytics and decision support in mind.

RayCare

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RaySearch will implement both classical machine learning techniques as well as deep learning methods, as they are appropriate for different problem settings. For instance, certain deep-learning approaches are very well suited for extracting information from medical images, while classical machine learning approaches have proven to be suitable for predicting dose for automatic plan generation. RaySearch’s machine learning applications will be developed in close collaboration with clinics to incorporate clinical knowledge into the algorithms.

Data analytics and machine learning will be cornerstones of both RayCare and RayStation, empowering the user by presenting relevant information at the right time and enabling the clinics to make use of their data and to build learning models. This has the potential to enable efficient workflows and highly consistent treatments, where every new piece of data contributes to constantly ongoing improvement.

Large amounts of data are generated for every patient who is treated, and machine learning can help support clinicians to make care smoother and more consistent.

This technology means clinicians can spend less time on repetitive tasks and free up time for patient consultations and complex cases. Furthermore, machine learning models could be shared or co-trained between clinics to help disseminate clinical knowledge between hospitals.

Machine Learning

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