AMICUS: AI in Medical Imaging for novel Cancer User Support | RVO.nl | Rijksdienst

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AMICUS: AI in Medical Imaging for novel Cancer User Support

pResearch Summary. The research aim of AMICUS is to develop and utilize technology for distributed deep learning on medical images in the Netherlands and beyond using the Personal Health Train approach and to show in compelling examples its value to cancer care organizations and patients. Medical imaging is the cornerstone of screening, detection, diagnosis, staging, treatment and follow-up in almost every cancer patient and without a doubt is Big Data. However the processing and interpretation of images is still mainly a human task. With AMICUS we will develop technology that supports radiologists oncologists and other stakeholders including the patient in extracting relevant information from images. The technology used for extracting information from images in AMICUS is deep learning, a form of artificial intelligence which has proven to be a breakthrough in the field, but which requires large volumes of imaging data to be successful. However, getting access to imaging data is a problem as it is dispersed across hospitals and is very privacy sensitive. In AMICUS we will develop technology that allows deep learning from these distributed imaging datasets without the need for these data to leave the hospital. For this, we will leverage the previously developed Personal Health Train approach which allows privacy-preserving learning from distributed FAIR (Findable Accessible Interoperable and Reusable) clinical data and which AMICUS will extend with FAIR imaging data and deep learning. The work plan of AMICUS consists of four work packages. WP1 and WP2 will focus on developing technology to make imaging and related data FAIR and to perform distributed deep learning in a way which requires only minimal computing resources at each hospital. WP3 and WP4 will focus on creating and demonstrating the value of AMICUS technology by implementing compelling solutions for care organizations to improve cancer care processes and for patients to improve cancer outcomes. 2.2. Utilisation Summary AMICUS offers a responsible and scalable way to create value out of the petabytes of imaging data of cancer patients stored in hospital archives. Since imaging is part of all phases of the cancer care path, the developed technology from AMICUS will create value for all cancer types and in all phases of the care path. Besides cancer care, imaging plays an important role in almost all health related diseases e.g. cardiovascular, neurology, orthopedy, gynecology, internal medicine etc. All these disciplines could benefit from the technology, as the use of FAIR methodologies and the accompanying deep learning platform developed within AMICUS will be applicable to different diseases and medical contexts. Hence AMICUS will increase the value of (imaging) data for better disease management. We have already identified more than 20 potential users of AMICUS technology. Our utilization plan is based on an extensive and proven track record supported by experienced technology transfer officers. It is tailored towards the needs of different categories of potential users incl. Cancer patients; Infrastructure and data services providers; Medical device vendors; Pharmaceutical companies; Hospitals; Government; Health insurance providers and Scientific researchers./p

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