Evaluation of Interpretable Deep Learning Approaches for Neuroimaging in Dementia using Stroke Classification as a Ground Truth for Brain Pathology

Brief description of the context of the research including potential impact

Dementia and Alzheimer's disease were the leading causes of death in 2018, with a growing prevalence in the UK that is expected to rise to 1 million by 2025. Deep learning models have shown great promise in aiding early diagnosis and prognosis. However, their complexity and lack of transparency hinders the likelihood of their adoption into clinical practice. This is particularly a problem in clinical routines where stakes are high, and the consequences of misdiagnosis can be life-threatening. In these areas interpretable approaches can provide a way of assessing, and often visualising, the behaviour of the network to better understand why a particular outcome is obtained. However, recent approaches have been limited due to lack of validation and the absence of a ground truth.
By taking advantages of the latest technologies from the rapidly developing field of interpretable ML, this project aims to compare alternative interpretability methods in reference to 'ground truth' neurological conditions like stroke before applying them to neurodegenerative disease and dementia.

Aims and Objectives

This research aims to critically validate the effectiveness of several interpretation methods for dementia diagnosis and prognosis using stroke classification as a ground truth for brain pathology. To begin with, we will aim to build a network that can classify stroke patients from controls with near-perfect accuracy in order to ensure that the attention maps are useful and informative. This will allow us to explore the generalisability of these models to site and/or data quality, voxel dimension and data normalisation. Other technical considerations we will consider include the effect of lesion size or atrophy, and clinical characteristics on model performance and interpretability. Having used the ground-truth case of stroke to assess interpretability approaches, the optimal approaches will then be applied to dementia during
the PhD phase. The resulting attention maps from these models will be assessed qualitatively and semi-quantitatively for clinical utility by a sample of radiologists (from UCL and collaborating sites) with expertise in dementia.

Novelty of Research Methodology

Interpretable medical imaging, a subfield of 'explainable AI', is an important topic for translational research. The planned research on neuroimage interpretability in dementia will be novel and provide both cohort and patient level attention maps, essential for delivering precision medicine for dementia. To our knowledge, comparative assessment of interpretability methods using stroke as a ground truth has not previously been reported. This research includes working closely with radiologists, to provide clinical expertise and data on clinical utility, making it a truly interdisciplinary translational project.

Alignment to EPSRC's strategies and research areas

Our research focus aligns with EPSRC's strategy for enabling earlier and more effective diagnosis (1) and integration of additional information from clinical data and images using machine learning (3). In addition, collaborating with clinicians aligns with the focus in strong engagement with relevant stakeholders (7). This work falls within the artificial intelligence technologies and image and vision computing research areas.

Any companies or collaborators involved

The project involves collaboration with the UCL Dementia Research Centre (DRC) and the NIHR Biomedical Research Centre at University College London Hospital (UCLH).

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.