Groundbreaking AI Tool from King’s College London Automates Analysis of Large-Scale Cardiac Scan Databases
Many hospitals maintain their own large CMR databases which, when combined with electronic health records, could provide valuable insights into the effectiveness of treatments and inform future healthcare research and guidelines.
However, databases are often organised differently by separate organisations and may often contain missing or duplicated files, which presents a significant challenge for processing data.
This work would previously have required a significant time investment for manual curation and analysis on the part of healthcare specialists, whereas an AI tool can be taught to efficiently ‘data wrangle’ at large-scale, rapidly assess quality, and translate data into common formats and structures.
Researchers at the School of Biomedical Engineering & Imaging Sciences have trained a generalizable AI algorithm on data comprising over 7000 CMR scans, with initial results yielding human-level accuracy for left ventricle and right ventricle segmentations across all major CMR scanner technologies, and for a wide range of cardiac diseases.
The proposed framework is a fundamental step for the clinical translation of AI algorithms. In addition, it allows for retrospective analysis of large clinical (research) datasets and compared to other works it allows analysis of a much larger set of biomarkers for regional and global systolic and diastolic biventricular function from CMR scans.
Dr Esther Puyol, Visiting Lecturer, Department of Biomedical Engineering
This work addresses the under-exploitation of the large clinical CMR imaging databases that many hospitals worldwide maintain. These are incredibly rich resources, containing historical and longitudinal data from many thousands of patients. When combined with data from electronic health records they could provide valuable insight into the effectiveness of treatments to inform future guidelines.
Dr Andrew King, Reader in Medical Image Analysis, Department of Biomedical Engineering
Future work will seek to extend this framework towards obtaining biomarkers linked to systolic and diastolic function and their potential connections to patient treatments and healthcare outcomes.