The aim of this work package is to ensure the conduct of an efficient and productive consortium project by facilitating cohesiveness across the consortium through a high level of interaction. This is meant to maximise the value of public/private partnership. This work package will also ensure that the governance of the project is followed and that the consortium adheres to ethics principles and manages the IP produced.
WP2 - Patient empowerment through decision support systems
The main objective of this work package is to enable the empowerment of the public to be a societal level resource that works together with other stakeholders to help diagnose and prognosticate to optimise care. It is also about empowering the public to participate in collection of data to improve clinical care and enable the development of precision medicine approaches that will increase the usefulness of the many new therapies under development. Patient empowerment is a process to help people gain control, including people taking the initiative, solving problems, and making decisions. As such, this work package is where the outputs of the other work packages are collected together into a multistakeholder decision support system among others aimed at patient empowerment.
WP3: Rapid scalable radiological diagnosis
In summary, we will deliver a CE marked software suite as a medical device based upon existing solutions currently successfully deployed in China. We will (re)train and validate machine learning models that learn from European data, from approximately 500 coronavirus (PCR positive) Chinese patients, with varying quality and acquisition protocols.The models will then be refined using British, Dutch and Italian datasets that are, or will be, available in the short- term. The trained model will be generalizable, with minimum effort, to data/patients from other regions.
WP 4: Rapid scalable radiological prognosis prediction
WP4 aims to analyse the “in-hospital” longitudinal imaging data (both CXR and CT), together with other clinical parameters, to predict rapid disease progression within the next 72 hours. This is of importance in planning emergency healthcare services such as ICU space and ventilating equipment, as well as of epidemiological value such as fatality prediction. Using CT as a frontline diagnostic tool during the breakout, as rolled out in China, will be evaluated in this WP. Machine learning based prediction tools will be developed to assist decision making and resource prioritisation. In addition, combining information extracted from sequential CXRs in their first 24-48 hours following admission with their CT scan data during model training will improve prognostic capabilities of our machine learning model further.
WP5: Privacy-preserved and self-adaptive imaging biomarker extraction
In this WP, we will investigate how to optimally adapt image quality and image information content for machine/radiomics use (as compared to visual inspection by a human). An iterative process of development, testing, assessment will be followed to deliver a validated and refined model closely linked to the aims for data harmonisation for the multicentre and multi-scanner studies of the CXR/CT images acquired for the coronavirus patients. Based on the consensus clinical protocol recommendation for image acquisition and synthetically harmonised images, image acquisition will be iteratively tailored to each grading of the coronavirus patients to facilitate the most important quantitative imaging biomarkers feature extraction. To this end, image acquisition will be systematically evaluated and varied to approach the optimal acquisition scenario for the dominant radiomics feature, leading to improved standardisation of the input images to the repository of the project.
WP6: Multifactorial analysis
The primary aim is to conduct a multi-factorial analysis to improve diagnosis of at-risk patients and to enable precision medicine approaches to patient care and new therapy development. Combining risk factors from multiple types of data (e.g., demographic, laboratory results, imaging) using advanced AI-enabled analysis, maximal value will be extracted from available patient information. This work package is also about developing mechanistic insights into the clinical course of the disease, the conduct of immunological analyses, and molecular profiling. This type of data is essential for targeting new therapies to subpopulations mostly likely to benefit and for providing mechanistic insights if a clinical trial fails.
WP7: Technical development of federated machine learning system
OncoRadiomics has developed DistriM (distributed machine learning) for radiology in oncology purposes. The purpose of DistriM is to continuously extract and apply updated knowledge from routine clinical care data rather than be exclusively ‘locked’ to the original clinical trial evidence. DistriM achieves this in line with the recommendations outlined by the FDA white paper on AI software as a medical device. Specifically in this project, the objective is to set up and optimise infrastructure for federated machine Learning for radiology in respiratory / coronavirus. DistriM delivers individual privacy-by-design for data management / processing and is transparently auditable by a blockchain. This WP is completed successfully once the federated machine learning infrastructure is set up, optimised, validated, and clinically deployed. DistriM will be populated with data from hospitals, research institutes, databases and continuously improved models are learned from these data on a regular basis. Specifically, an implementation supporting distributed learning for standardised imaging biomarker extraction, and for facilitating distributed radiomics and deep learning in medical image analysis will be developed, which will be utilized and integrated in all other WPs.
WP8: Fast track clinical studies
Linking into other coronavirus IMI (or other) projects. With the knowledge and tools generated in the previous work packages provide a platform for broad usage in clinical trials, testing an aerosol sampling device, and providing samples for the precision medicine approach.
WP9: Accelerated regulatory approvals
This work package details the work necessary to rapidly develop, certify, and deploy clinically viable fit for purpose solutions (such as a patient decision aid App or an AI model prospectively validated that can automatically detect and classify coronavirus patients in a variety of imaging settings).
WP10: Dissemination and communication
To ensure that the impact of DRAGON is maximized, this work package is exclusively focused on dissemination of the results to its end-users as well as communication to a wider public. ERS and ELF will lead these activities. However, it will require the engagement of all partners not only for generating the communication material but as well to ensure appropriate dissemination through the necessary channels. This will be instrumental to ensure that DRAGON’s results and tools are used in clinical practice.
This work package will work to develop a plan to sustain the collaboration and the resulting assets after the funding period. The longer-term impact will be substantially increased if this project leads to a sustained effort that continues to build over time.