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INTELLIGENT
TREATMENT PLANNING

Machine learning for intelligent treatment planning

Cancer treatment represents one of the most exciting applications of data analytics and machine learning technologies. Machine learning already supports the identification of diseases and diagnosis, through to treatment planning and aftercare. By analyzing thousands of different data points, advanced algorithms in RayStation* can help clinics save time and increase consistency by automating plan generation and organ segmentation.

*Subject to regulatory clearance in some markets.

Key features

  • Deep learning segmentation of CT structures
  • Deep learning based dose prediction for automated treatment plan generation
  • Deep learning solutions are integrated into RayStation
  • Validated models are released with RayStation releases
  • Spend less time on repetitive tasks
  • Have more time for patient consultations and complex cases

Deep Learning Segmentation is included in RayStation*

RaySearch is continuously improving the released models and will release new or updated Deep Learning* Segmentation (DLS) models regularly, with RayStation releases.

Deep learning capabilities in RayStation help make image segmentation quicker and more consistent. A high-speed GPU-powered algorithm is capable of producing consistent segmentation results using segmentation models that have been trained and evaluated on clinical data for different body sites. A deep learning model can segment multiple structures in less than one minute.

 

*Applies for RayStation 11B and later versions

DEEP LEARNING SEGMENTATION CATALOGUE

Click here to view this model catalogue

Deep learning segmentation in RayStation

In this webinar you learn more about how deep learning segmentation can be implemented at your clinic. Our senior application specialist Grietsje Schregardus-Abma will demonstrate the latest release of segmentation models and talk about how to go live with DLS. Sigrun Saur, St Olavs Hospital, will talk about training, validation and clinical implementation of a DLS model for breast cancer, a joint venture together with RaySearch.

 
 

DEEP LEARNING DOSE PREDICTION MODELS CATALOGUE

View Here: Subject to regulatory clearance in some markets. Not for marketing in the USA or Canada

The world’s first machine learning plan generation module

RaySearch partnered with Princess Margaret Cancer Center in Canada to develop the world’s first machine learning treatment plan generation module. In May 2019, patients with localized prostate cancer were treated using machine learning treatment plans generated in RayStation as part of a compensative evaluation study. See the study here

RaySearch received 510(k) clearance from the U.S. Food and Drug Administration for RayStation 8B, which was the first machine learning applications in a treatment planning system on the radiation oncology market today.

Clinics can get personalized treatment plans in RayStation in minutes.

WEBINAR: Deep learning planning in RayStation

In this webinar we presented our latest release of deep learning planning models, machine learning news in RayStation 11B and how deep learning planning can be implemented at your clinic. Demonstrating how your clinic can configure and commission a released and validated model for your protocol, planning trade-offs and treatment machines.

 
 

What are the key benefits of Machine Learning in RayStation?

RaySearch’s Muqeem Qayyum discusses the key differentiators and clinical benefits of machine learning in RayStation.

 
 

Testimonial from Tom Purdie, PMH

“Machine learning is a natural fit for automating the complex treatment planning process. We expect it will enable us to generate highly personalized radiation treatment plans more efficiently, thereby allowing clinical resources or specialist technical staff to dedicate more time to patient care. Deemed clinically acceptable by experts around the world, RayStation algorithms generate high-quality treatment plans that are preferred or deemed equivalent to clinical plans.”

Tom Purdie
Medical Physicist, Princess Margaret Cancer Center

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