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Leeson Group - Developing new echocardiography diagnostics with AI

  • Paul Leeson

ABOUT THE RESEARCH

Echocardiography is the most commonly used imaging modality in cardiology and is performed many times more frequently than any other imaging test. Echocardiography is both cheap and portable allowing it to be used in most health settings globally. The Division of Cardiovascular Medicine, within the Radcliffe Department of Medicine, has some of the largest curated, multi-centre, research echocardiography databases in the world. These include the EVAREST/BSE-NSTEP cohort, which includes 10,000s of echo collected from >30 hospitals across the UK, and the EchoVision project that has access to 100,000s of routinely collected echocardiography datapoints. In addition, global collaborations allow access to echocardiography databases in a range of diverse countries including India and South Africa. 

The world-leading artificial intelligence and machine learning expertise within the research group, and through collaborations within Engineering and Computer Science groups within the University and commerically, has meant research in progress is changing how we think about using echocardiography within clinical practice. This project will be based within the Division of Cardiovascular Medicine and build on our ‘blue skies thinking’ approach to identify new ways to acquire, extract and make sense of information contained within echocardiography images. This will include images acquired on all types of system from legacy, hospital, ‘cart’ systems through to handheld consumer devices. The aim will be to develop an original approach to image interpretation and/or diagnosis.

 

TRAINING OPPORTUNITIES

This project can provide training in imaging, cardiovascular disease development and machine learning using a range of techniques appropriate to the project. The project will be developed bespoke for the student based on their background expertise and interests. Therefore, for example, more clinically-focused students can build projects around clinical studies and trials of AI devices, whereas for engineering or computational-focused students, the project can build directly on imaging handling and processing. The group is highly multi-disciplinary and therefore students with complementary expertise will work together on particular projects.  The student would also learn about regulatory issues surrounding clinical studies and randomised trials such as ethics, hospital R&D and GCP and attend relevant courses. In addition, links with centres within the University allow for development of knowledge within the ethics of AI and how to develop sustainable and trustworthy AI in healthcare. Finally, opportunities could be available to develop further experience in the translational pathway and how to take innovation into clinical practice.

Students are encouraged to attend the MRC Weatherall Institute of Molecular Medicine DPhil Course, which takes place in the autumn of their first year. Running over several days, this course helps students to develop basic research and presentation skills, as well as introducing them to a wide-range of scientific techniques and principles, ensuring that students have the opportunity to build a broad-based understanding of differing research methodologies.

Generic skills training is offered through the Medical Sciences Division's Skills Training Programme. This programme offers a comprehensive range of courses covering many important areas of researcher development: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence and impact. Students are actively encouraged to take advantage of the training opportunities available to them.

As well as the specific training detailed above, students will have access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford.

The Department has a successful mentoring scheme, open to graduate students, which provides an additional possible channel for personal and professional development outside the regular supervisory framework. We hold an Athena SWAN Silver Award in recognition of our efforts to build a happy and rewarding environment where all staff and students are supported to achieve their full potential.

 

PUBLICATIONS

1

Upton R, Mumith A, Beqiri A, Parker A, Hawkes W, Gao S, Porumb M, Sarwar R, Marques P, Markham D, Kenworthy J, O'Driscoll JM, Hassanali N, Groves K, Dockerill C, Woodward W, Alsharqi M, McCourt A, Wilkes EH, Heitner SB, Yadava M, Stojanovski D, Lamata P, Woodward G, Leeson P. Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence. JACC Cardiovasc Imaging. 2022 May;15(5):715-727. doi: 10.1016/j.jcmg.2021.10.013. Epub 2021 Dec 15. PMID: 34922865.

For

2

Alsharqi M, Lapidaire W, Iturria-Medina Y, Xiong Z, Williamson W, Mohamed A, Tan CMJ, Kitt J, Burchert H, Fletcher A, Whitworth P, Lewandowski AJ, Leeson P. A machine learning-based score for precise echocardiographic assessment of cardiac remodelling in hypertensive young adults. Eur Heart J Imaging Methods Pract. 2023 Sep 27;1(2):qyad029. doi: 10.1093/ehjimp/qyad029. PMID: 37818310; PMCID: PMC10562347.

3

O'Driscoll JM, Hawkes W, Beqiri A, Mumith A, Parker A, Upton R, McCourt A, Woodward W, Dockerill C, Sabharwal N, Kardos A, Augustine DX, Balkhausen K, Chandrasekaran B, Firoozan S, Marciniak A, Heitner S, Yadava M, Kaul S, Sarwar R, Sharma R, Woodward G, Leeson P. Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography. Eur Heart J Open. 2022 Sep 21;2(5):oeac059. doi: 10.1093/ehjopen/oeac059. PMID: 36284642; PMCID: PMC9580364.

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4

Woodward G, Bajre M, Bhattacharyya S, Breen M, Chiocchia V, Dawes H, Dehbi HM, Descamps T, Frangou E, Fazakarley CA, Harris V, Hawkes W, Hewer O, Johnson CL, Krasner S, Laidlaw L, Lau J, Marwick T, Petersen SE, Piotrowska H, Ridgeway G, Ripley DP, Sanderson E, Savage N, Sarwar R, Tetlow L, Thompson B, Thulborn S, Williamson V, Woodward W, Upton R, Leeson P. PROTEUS Study: A Prospective Randomized Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiography. Am Heart J. 2023 Sep;263:123-132. doi: 10.1016/j.ahj.2023.05.003. Epub 2023 May 14. PMID: 37192698.

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5

Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, Marwick TH. Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. J Am Coll Cardiol. 2019 Mar 26;73(11):1317-1335. doi: 10.1016/j.jacc.2018.12.054. PMID: 30898208; PMCID: PMC6474254.

6

Fletcher AJ, Johnson CL, Leeson P. Artificial intelligence and innovation of clinical care: the need for evidence in the real world. Eur Heart J. 2023 Sep 6:ehad553. doi: 10.1093/eurheartj/ehad553. Epub ahead of print. PMID: 37670406.