What specific challenges does Contour + address in the field of radiotherapy , and how does its AI-powered automation contribute to overcoming these challenges ?

The largest challenge in cancer radiotherapy is the shortage of experts , including oncologists . For this reason , they have too little time to create a thorough treatment plan for each individual patient , which translates into two challenges . First , is the increase in the patient waiting times and therefore increasing patient backlog and second , is the non-optimal quality of work . Contour + addresses both challenges . The first challenge is addressed by the significant time-savings obtained and the second challenge is addressed by a data-centric AI development process , whereby the AI guides the experts towards best practice industry standards .

How does Contour + leverage AI technology to automate organ-at-risk and lymph node volume delineation in radiotherapy planning ?

We use and train Deep Learning algorithms that are very powerful in image processing . The key element is to select the most appropriate model architecture for the task , combined with the data-centric approach . Every AI algorithm is only as good as the data it is given on which to learn from . At MVision AI , we have built a comprehensive pipeline to generate training data that complies with the industry standards pertaining to the approved contouring guidelines .

Could you elaborate on the standardisation aspect of Contour + and how it aligns with industry contouring guidelines such as ESTRO , EPTN , RTOG , and the UK SABR Consortium ?

After their recent adoption by Cork University Hospital , we speak to Jarkko Niemelä , CEO and Co-founder of MVision AI , about the power and potential of their solutions in revolutionising oncology , cancer radiotherapy and decreasing medical backlogs .

Contour + has been developed to follow and comply with the industry standards which are the contouring guidelines . This is not possible with clinical datasets obtained from clinics due to the inherent human or expert variation contained within them . With MVision ’ s combined data-centric and peer-review approach by Oncologists , we enforce our AI to be guidelinecompliant . Contour + generates guideline-compliant contours for all patients including the mentioned ESTRO , EPTN , RTOG and the UK SABR Consortium . Consequently , we believe , our approach will enhance the quality of future conclusions relating to best practice due to the variation from these standards being minimised . The development of any AI-based tools carries a responsibility for the companies who develop them . The increased awareness of AI in the media is driving governments around the world to form governing bodies and / or departments that will be overseeing this industry ,

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