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Machine learning is one of the fastest-growing areas of technology today. It has had a key role in advances in many fields, and its significance for the future of healthcare is potentially enormous. Through machine learning, smarter and faster software will be created, and this benefits both RayStation and our next-generation oncology information system (OIS) RayCare. RaySearch already has a strong focus on automation and machine learning brings this to a new level. Through machine learning, smarter and faster software is created and automatic treatment plan generation* and deep-learning organ segmentation* are the first applications.

*Subject to regulatory clearance in some markets.


During the first part of the webinar, Fredrik Löfman, head of machine learning at RaySearch, discuss how RaySearch is pioneering machine learning for smarter and faster oncology software. In the second part, Tom Purdie, medical physicist at Princess Margaret Cancer Centre, talks about automatic treatment planning and machine learning from a clinical perspective.

RaySearch is implementing 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 are developed in close collaboration with clinics to incorporate clinical knowledge into the algorithms.

Data analytics and machine learning are 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.

RaySearch is currently developing machine learning and deep learning prototypes for areas such as treatment plan generation, organ segmentation and target volume estimation. As data is key to learning algorithms, we are also developing techniques for efficient large-scale data extraction and analysis.

The first machine learning applications put into RaySearch’s products for clinical use are automated treatment planning* and automated organ segmentation*. We are also putting effort into developing data models and setting up collaborations with world-leading cancer centers to take this entire field forward and tailor what RaySearch develops to clinical requirements.

In the short term, a lot more automation of routine workflows such as treatment plan generation and organ segmentation will be seen. Another obvious application is in detecting deviations from treatment protocols, which will be very valuable for large clinics to improve treatment consistency. The potential of clinics sharing knowledge through trained machine learning models will be very interesting to follow.

Looking further ahead, machine learning will be an important tool to achieve the goal of personalized medicine, predicting needs and supporting clinicians in delivering oncology treatments tailored to each patient.

We are on the verge of a breakthrough era for oncology innovation. The changes to come in the near future will be far reaching and will bring great benefits for patients and clinicians. RaySearch has a fantastic team of outstandingly dedicated and creative people, which means things get done rapidly and smoothly. Many clinics are keen to collaborate and together with RaySearch they will redefine the possibilities of oncology software.