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The Fondation Brocher is an essential player in this vital thinking process: one which will help make us aware of the real challenges in using our resources for maximum impact on the health of the people of the world.

 

 

Professor Daniel Wikler, Harvard University

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November 15 - 17, 2018

Sharing medical images for Big Data

Organizers:

Introduction:

A three-days’ workshop focused on the emerging legal and ethical frameworks that could facilitate the collection and generation of large databanks of medical images obtained through donations or general consent of patients. The workshop will focus on current and future legal laws and regulation applicable in Switzerland and debate on the application and practical impact of these regulations in the development of Big-Data collections of images while complying with fundamental ethical principles.

Medical imaging is becoming a major component of the data required in every medical decision in diagnostic, assessment of treatment response, follow-up of disease recurrence but also in support for surgery, as well as non-invasive treatments. Images are the perfect objective biomarker of the expression and evolution of diseases. Billions of images are being generated every day in every hospital and every healthcare facility. For any given disease or medical episode several examinations are often required to answer a given question and make the appropriate decision in patient management. The combinations of several imaging modalities such as X-rays, ultrasound, CT scans, MRI or nuclear medicine images are often necessary. The wealth of data acquired in every case is overwhelming and has not been apprehended yet. The heterogeneity and the complexity of data embedded in these imaging modalities is far from being mastered yet. The rapid evolution of machine learning and deep data mining tools applicable to very large collections of data can potentially open the road to new generation of image analytics and feature extractions from hundreds of image features also known under the new domain of “radiomics” analysis tools.

The main limiting factor of the development of these new analysis techniques is the lack of sufficiently large sets of structured and well-documented imaging data. Besides the problem of the heterogeneity and lack of consistency between imaging protocols and image quality, there is also major difficulties in the ability to collect these large sets of imaging data. Primarily because the overwhelming regulatory constraints and data protection rules that prevent the usage and exploitation of existing imaging data without formal patient approval. Informed consent principles also require that the patient being informed of the purpose and goals of the research performed with the data which defeats the basic principle of deep learning and Big-data analytics which looks for random patterns and correlations without a specific pre-established hypothesis. To overcome this dilemma, regulatory bodies, government agencies and academic experts in ethics have promoted new concepts of disruptive legal frameworks that will allow and regulate the ability of each individual to contribute to the development of Big-Data collections of images while complying with fundamental ethical principles. Different trends are being explored ranging from a concept of a “general” consent with some string attached to a completely unrestricted “donation” of the data.

The purpose of our workshop is to examine these new concepts of data sharing and to bring together experts from different disciplines to discuss and confront the different concepts that are being proposed. The specific application to medical images is pivotal because it addresses a type of data that is extremely rich in content and exploitable for a very large scope of applications. Besides, the psychological and subjective impression that it is easier to donate images to science that other more personal data such as genomic profile or biological biomarkers is also of interest. Therefore, we would like to focus our project on the specific issue of gathering and collecting medical images for the development of large Big-Data repositories for scientific research.