1. To introduce the potential of AI to accelerate the introduction of imaging biomarkers.
2. To introduce the challenges of managing biomarkers with AI systems.
3. To introduce the speakers.
This session will focus on the use of artificial intelligence for selecting and managing imaging biomarkers. Clarification around the use of terms such as ʽartificial intelligenceʼ and ʽbiomarkersʼ will be discussed. The strengths and limitations of the technologies used will be considered. The development of researcher-driven science and technology networks through the EU COST (Co-operation in Science and Technology) action initiative will be showcased using renal biomarkers as an exemplar. Finally, the selection of combinations of biomarkers from hybrid imaging technologies using AI will be addressed.
1. To learn about available supervised vs unsupervised machine learning techniques.
2. To learn about deep-learning methods to discover biomarkers.
3. To understand the strength, but also limits and pitfalls, of machine learning methods.
In recent years, Deep Neural Networks (DNN) have achieved unprecedented performances in many domains, especially with the analysis of images. Major results have been announced for several applications, including skin lesions, pneumonia, pathology and so forth. Several algorithms have even been approved by regulatory agencies, e.g. for the diagnosis of diabetic retinopathy in specific circumstances. It now makes no doubt that artificial intelligence algorithms will be part of the medical experts' toolboxes. In this presentation, we will explore the basic principles behind artificial intelligence and neural networks: supervised and unsupervised algorithms, neurons, activation functions, and the overall architectures of networks. We will discuss more specific classes of DNN used today for the exploration of images, namely the convolutional network and the autoencoder-denoiser, and how they can be used to identify new biomarkers. We will emphasise the crucial role played by expert annotations on images, and explore how experts annotations are used to build models. Finally, we will discuss the implications of the use of deep neural networks in medicine and radiology, and briefly explore new risks (for example adversarial attacks) linked with the use of such technologies.
1. To learn about how COST actions work and the outputs generated.
2. To appreciate examples of a successful COST action for selecting imaging biomarkers.
3. To understand difficulties in selecting biomarkers for specific indications.
COST actions are EU-funded, open, and growing pan-European networking tools aimed at building, bridging and expanding interdisciplinary research communities. A successful example is the COST action PARENCHYMA (Magnetic Resonance Imaging Biomarkers for Chronic Kidney Disease - www.renalmri.org), which coordinates the research of leading European groups working on renal MRI. The rising prevalence of Chronic Kidney Disease (CKD) poses a major public health challenge, and an alarming number of negative clinical trials on CKD progression point out the urgent need for better biomarkers to identify patients that are at risk of progression or are likely to respond to candidate therapeutics. MRI biomarkers have shown a high potential to help fill this gap, as they are non-invasive and sensitive to CKD pathophysiology. Based on this rationale, PARENCHYMA is trying to unlock renal MRI biomarker potential by improving their standardisation and availability, and by generating strong multicentre clinical evidence of biological validity and clinical utility. As different imaging biomarkers provide complementary information on pathophysiology, they need to be combined to reach the highest sensitivity. Artificial intelligence could help to identify best disease-specific combinations.
1. To learn how AI can improve imaging biomarkers from multiple imaging modalities.
2. To appreciate the challenges "big data" pose to regulators, whether from imaging biomarkers or from the more conventional biospecimen (genomic, proteomic) biomarkers.
3. To understand how best to utilise AI for imaging biomarkers and to avoid the pitfalls.