Explainable AI for diagnosing and treating cardiovascular disease

Aim of the PhD Project:

Heart disease is number one killer worldwide.
AI models can automatically diagnose disease, but they lack explanatory power.
Project aims to develop AI tool for diagnosis and treatment planning in cardiology that can explain its decisions to cardiologists.
Project Description / Background:

The use of artificial intelligence (AI), and specifically deep learning, for diagnosis and treatment planning in cardiology is an active research area [1,2]. However, whilst deep learning techniques have produced impressive results, a significant problem remains. Often, the techniques that produce the most accurate results lack one important feature that is important for the clinical acceptance of new technology: explanatory power. Put simply, most deep learning models are able to make predictions but are not able to explain in human-interpretable terms how the prediction was arrived at. Without such explanations, in many applications clinicians will be reluctant to base clinical decisions upon recommendations from such "black-box" models.

In this project we focus on deep learning models that take images (and possibly other clinical data) as input. Producing explanations from image-based deep learning models is a significant challenge. In the literature, most attempts at such "interpretable machine learning" or "explainable AI" have focused on one of two approaches: (1) try to visualise the inside of the "black box", e.g. by using "saliency maps" which show areas of the input image that were important in making the decision, (2) train a simpler model which may be more interpretable to some degree. Both of these approaches are likely to be inadequate in many medical applications. For example, in cardiology, which is our focus in this project, an "explanation" that will be acceptable to a cardiologist is likely to require information about pathological processes and/or concepts such as tissue properties and electrical/mechanical activation patterns.

A key challenge will be to find ways of linking the model's automated decision with "higher level" human-interpretable concepts. In this project, we will investigate ways of making these links in the application of patient diagnosis, stratification and treatment planning in heart failure. One promising area that has recently emerged from the computer vision literature is the investigation of ways of querying the importance of human-interpretable concepts to deep learning models [3]. We have recently started to apply these methods in cardiology with highly promising initial results [4]. Other interesting avenues for exploration include methods that incorporate explanations into the training objective [5], as well as ways of putting humans (i.e. clinicians) "in the loop" of the training of deep learning models [6]. This type of approach could be used to encourage the deep learning model to learn features that are clinically meaningful, effectively creating a dialogue between clinicians and deep learning models.

There are many intriguing directions to explore in this field which are relatively untouched in the medical domain, and the potential for novelty is high. Our ultimate aim is to produce a computer aided decision-support tool to assist cardiologists in stratifying patients with heart failure and planning its treatment. The tool would act like a "trusted colleague" or "second reader" that the cardiologist could engage with to find their opinion about difficult cases as well as the reasoning behind this opinion. This is a highly ambitious aim and this project represents the first part of this journey, but if successful the impact could be great.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.