AI for healthcare

Artificial intelligence (AI) offers a real opportunity in healthcare, not only to automate some of the problem-solving carried out by doctors and other medical professionals, but also to make quicker and better decisions and apply problem-solving techniques that humans alone could not.

“This will improve the cost of care, and improve outcomes, simply because things will happen earlier, faster, and better,” says Dr Aldo Faisal, who leads Imperial’s UKRI Centre for Doctoral Training in AI for Healthcare. “Rather than replacing people with machines, creating unemployment, we foresee this as a way of dealing with the growing unmet need for clinical care.”

The challenges involved touch on some of the hottest topics in AI research. For example, when you are dealing with people’s health, the explainability of AI – our ability to understand why systems have made certain decisions – becomes much more important. This means dealing with human–machine interaction, trust and the security of AI systems, and questions about autonomy. It also means operating in settings where there is tight regulation.

“AI for healthcare embodies everything that makes AI in general interesting” says Dr Faisal. He divides AI for healthcare into two broad categories. Perceptual AI replicates the ability of healthcare professionals to perceive disease, a skill that goes to the heart of diagnosis and monitoring. And intervention AI addresses decisions about how patients should be treated. Imperial College London is active in both domains.

Following the heart

Professor Declan O’Regan, a clinical radiologist at the MRC London Institute of Medical Sciences at Imperial, has been working with Professor Rueckert to apply machine learning to magnetic resonance imaging (MRI) of the heart.

The standard method of analysing these images is to draw contours on them by hand and so calculate simple measures such as heart mass and volume.

“But there is so much more information in those images, particularly about the early signs of heart failure, that may be difficult for people to appreciate unaided,” he says. “The images may also help us understand some of the complex genetic effects on the structure and function of the heart.”

Machine learning has two roles here. First, it drives computer vision techniques that can track the motion of the heart and build models that capture its structure and function in 3D. Then, together with conventional statistical models, AI can help predict outcomes, for example stratifying patients according to their risk of heart failure and likely response to therapy. This approach is currently the subject of a multicentre trial, which should report by the end of 2020.

Companies are also interested, and Professor O’Regan is leading a project for Bayer Pharmaceuticals that applies this approach. “We are using machine learning to analyse MRI data, then integrating genetic and other health information to identify pathways that could be potential drug targets,” he says. “The idea is to accelerate drug discovery, to find potential new ways of treating serious heart conditions.”

Sharing AI solutions

Meanwhile, Dr Ben Glocker in BioMedIA is working with another company interested in cardiac imaging. The company, HeartFlow, uses routine computed tomography scans to create a digital model of the arteries in the heart and simulate where blockages may be restricting blood flow. “It’s a very promising non-invasive diagnostic tool, but it’s computationally demanding and this means throughput is limited,” Dr Glocker says. “Some steps in the analytical pipeline are already done with AI-type algorithms, so we are doing research with the company to see if we can make improvements.”

This might mean speeding up processes, increasing accuracy so the need for human verification is reduced, or enabling the system to work on a wider range of image qualities. “These are all research questions where we need to look at new methods and new algorithms.”

The novelty of this collaboration is that, in addition to funding two PhD students, HeartFlow has sent two of its research scientists – one directly from its offices in California – to work within BioMedIA. “They work on problems relevant to HeartFlow, but also discuss and exchange ideas with people here who do all kinds of research on medical imaging,” says Dr Glocker. “So far it is working really well, with benefits in both directions.”

Detecting brain tumour changes

Movement is also the key to BrainWear, a system for assessing the progress of brain tumours, which is being developed and trialled by Dr Matthew Williams. He leads the Computational Oncology Group at Imperial, which straddles the Departments of Computing, and Surgery and Cancer.

“The underlying idea is that there is a close link in the brain between location and function,” he explains. “As a brain tumour gets bigger, it is likely to affect function in different ways. Some tumours might affect speech, some might affect walking, some might affect other functions.”

Movement is measured with a wrist accelerometer gathering data in three dimensions, 100 times a second. Unlike a commercial fitness tracker, which reduces motion data to a simple measure such as the number of steps taken, the BrainWear monitor produces a huge amount of raw data for the AI to work with.

“We don’t just want to measure how much you are walking, but how you are walking,” Dr Williams says. “So we apply deep learning to that data to pick out significant features of someone’s gait, to establish what is normal and recognise changes that are down to the disease.”

Meanwhile, factoring in the patient’s treatment should make it possible to rule out changes due to ongoing therapy. “We are now at the stage of collecting data from patients and carers and looking at what that can tell us.” Initial results are expected by the end of 2019. “But the real challenge comes in integrating the different sources of data – fatigue, quality of life and activity – to provide a coherent picture of the patient over time. That is the ultimate goal.”

BrainWear is unusual in bringing together engineering and data science to solve a problem in cancer management. This is an approach championed by the Cancer Research UK Convergence Science Centre, established at Imperial and the Institute of Cancer Research. “We started our work before the Centre came along, and we are a good example of what it hopes to achieve,” says Dr Williams.

Consulting an AI clinician

In the area of intervention AI, Professor Aldo Faisal and his colleagues have been developing an AI Clinician. So far this has been applied to diabetes management, neurological disorders, and most recently to intensive care.

Here the AI is told to consider the monitoring data routinely collected in intensive care and to maximise the patient’s chances of survival. Conventionally, an AI would approach this kind of task by trial and error, but this is not something that can be done with a patient. So a sleight of hand is required.

“We fool our algorithm into believing that the doctors’ interventions from the past were its own interventions,” explains Professor Faisal. “Based on these real interactions, which the AI only hypothetically experienced, it learns to become better than the average doctor. Better, in fact, than 99.8% of all clinicians in intensive care.” In practice, the AI would not be allowed to take decisions on its own, but only give advice to the doctor or other carers. This makes explainability of paramount importance. “The machine has to say why it is making a recommendation and convince the doctor why it is the right thing to do,” Professor Faisal says.

This also turns out to be a skill that the AI can learn, informed by input from neuroscience and psychology. “We’ve put a lot of thought into how AI deals with clinicians as people – interacting with them and making recommendations. At the same time, we’ve worked on how to make this a regulatable, acceptable way of interacting.” The system is currently being installed at St Mary’s Hospital, London, in preparation for a practical test.

Breaking down barriers for healthcare AI 

One of the important challenges in developing AI for healthcare is eliminating divisions between the disciplines involved. One way Imperial is doing this is with the UKRI Centre for Doctoral Training in Artificial Intelligence for Healthcare, which opened in October 2019 and now has almost 40 students. Each PhD project in the Centre has two supervisors, one with a background in AI, the other with a background in healthcare. The students come from science and engineering, or medicine, and will do much of their training together. At the same time, there are courses that demystify AI for the clinicians, and explain the clinic to engineers and scientists.

“In this way the engineers and scientists understand what the patient journey looks like, they meet patients and patient organisations, so that from day one students really understand what it means to deliver care,” says Dr Faisal.

Meanwhile a close understanding of medical regulation will help move PhD projects with commercial potential closer to market. “We also have a number of incubators on board that provide opportunities for students to launch a startup during their studies.” Above all, there must be a connection across the disciplines. “The most important thing we can instil in these students is how important mutual respect is for understanding other people’s disciplines and concerns.”

The range of disciplines involved in healthcare AI is clear from Professor Aboagye’s initiative on ovarian cancer, which included oncologists, radiologists, geneticists, bioinformaticians, pharmacologists and computer scientists. All were vital to debate the questions thrown up by the research. “Imperial has all these people,” he says. “If you have the right questions, then you can bring these diverse researchers together, in an environment that supports and encourages this kind of research.”