Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data
Lavdas , Glocker B, Rueckert D, Taylor SA, Aboagye EO, Rockall AG (2019)
Publication Type: Journal article, Review article
Publication year: 2019
Journal
Book Volume: 74
Pages Range: 346-356
Journal Issue: 5
DOI: 10.1016/j.crad.2019.01.012
Abstract
Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characterisation. We are currently involved in an National Institute of Health Research (NIHR)-funded project, which aims to develop machine learning methods to improve the diagnostic performance and reduce the radiology reading time of whole-body magnetic resonance imaging (MRI) scans, in patients being staged for cancer (MALIBO study). We describe here the main challenges we have encountered during the course of this project. Data quality and uniformity are the two most important data traits to be considered in clinical trials incorporating machine learning. Robust data pre-processing and machine learning pipelines have been employed in MALIBO, a task facilitated by the now freely available machine learning libraries and toolboxes. Another important consideration for achieving the desired clinical outcome in MALIBO, was to effectively host the resulting machine learning output, along with the clinical images, for reading in a clinical environment. Finally, a range of legal, ethical, and clinical acceptance issues should be considered when attempting to incorporate computer-assisting tools into clinical practice. The road from translating computational methods into potentially useful clinical tools involves an analytical, stepwise adaptation approach, as well as engagement of a multidisciplinary team.
Involved external institutions
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
University College London Hospitals (UCLH)
United Kingdom (GB)
United Kingdom (GB)
University College London Hospitals (UCLH)
United Kingdom (GB)
How to cite
APA:
Lavdas, ., Glocker, B., Rueckert, D., Taylor, S.A., Aboagye, E.O., & Rockall, A.G. (2019). Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data. Clinical Radiology, 74(5), 346-356. https://dx.doi.org/10.1016/j.crad.2019.01.012
MLA:
Lavdas, , et al. "Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data." Clinical Radiology 74.5 (2019): 346-356.
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