Many hospitals maintain their own large cardiac magnetic resonance (CMR) databases which, when combined with electronic health records, could provide valuable insights into the effectiveness of treatments and inform future health care research and guidelines.
However, databases are often organized differently by separate organizations 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 health care 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 more than 7,000 CMR scans, with initial results yielding human-level accuracy for
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
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