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  4. Machine learning

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

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WHITE PAPER: DEEP LEARNING SEGMENTATION

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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 guideline-based segmentation models that have been trained and evaluated on curated data for different body sites. 

 

*Applies for RayStation 11B and later versions

*Subject to regulatory clearance in some markets.

DEEP LEARNING SEGMENTATION CATALOGUE

Click here to view this model catalogue

Webinar: Deep learning segmentation in RayStation

  • DLS implementation and experience at RMH (The Royal Marsden NHS Trust).
  • Clinical implementation of automatic AI-segmentation for radiotherapy planning (Skåne University Hospital, Lund) 
  • The Ottawa Hospital experience with implementation of DLS for auto contouring multiple treatment sites (Ottawa Hospital Cancer Centre) 
 
 

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The Gift of Deep Learning Segmentation Freeing Up Time to Plan

Sarah Glazebrook , M.S. DABR, Lead Medical Physicist
Mary Balce, Senior Medical Dosimetrist
Sharp Memorial Outpatient Pavilion, USA

 
 

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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.

 
 

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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.

 
 

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WHITE PAPER: DEEP LEARNING PLANNING

Click here to download and view

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

Spotlight Series, Episode 2: Shaping the future of cancer treament planning by leveraging AI

 

Explore the transformative power of AI-driven automation in cancer treatment planning and care. Join Fredrik Löfman, Head of Machine Learning at RaySearch Laboratories, as he discusses how AI enhances patient and practitioner experiences by saving time, boosting efficiency and increasing treatment accuracy.

In Episode 2, Fredrik delves into AI’s vital role in helping healthcare professionals define optimal cancer treatment. With RaySearch innovations, clinicians can leverage AI to analyze patient and treatment data, delivering knowledge-based treatment alternatives through deep learning*.

Looking ahead, consider making more informed decisions that weigh radiotherapy against other treatment modalities, or treatment plans based on real-world evidence that aggregate patient data, treatment data, outcome data from clinics worldwide.

*Subject to regulatory clearance in some markets

See more from the RaySearch Spotlight series here.

 
 

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