首页|University of Sheffield Reports Findings in Artificial Intelligence (Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging)
University of Sheffield Reports Findings in Artificial Intelligence (Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging)
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New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Sheffield, United Kingdom, by NewsRx correspondents, research stated, “Segmentation of cardiac structures is an important step in evaluation of the heart on imaging. There has been growing interest in how artificial intelligence (AI) methods-particularly deep learning (DL)-can be used to automate this process.” Our news editors obtained a quote from the research from the University of Sheffield, “Existing AI approaches to cardiac segmentation have mostly focused on cardiac MRI. This systematic review aimed to appraise the performance and quality of supervised DL tools for the segmentation of cardiac structures on CT. Embase and Medline databases were searched to identify related studies from January 1, 2013 to December 4, 2023. Original research studies published in peer-reviewed journals after January 1, 2013 were eligible for inclusion if they presented supervised DL-based tools for the segmentation of cardiac structures and non-coronary great vessels on CT. The data extracted from eligible studies included information about cardiac structure(s) being segmented, study location, DL architectures and reported performance metrics such as the Dice similarity coefficient (DSC). The quality of the included studies was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). 18 studies published after 2020 were included. The DSC scores median achieved for the most commonly segmented structures were left atrium (0.88, IQR 0.83-0.91), left ventricle (0.91, IQR 0.89-0.94), left ventricle myocardium (0.83, IQR 0.82- 0.92), right atrium (0.88, IQR 0.83-0.90), right ventricle (0.91, IQR 0.85-0.92), and pulmonary artery (0.92, IQR 0.87-0.93). Compliance of studies with CLwas variable. In particular, only 58% of studies showed compliance with dataset description criteria and most of the studies did not test or validate their models on external data (81%). Supervised DL has been applied to the segmentation of various cardiac structures on CT. Most showed similar performance as measured by DSC values.”
SheffieldUnited KingdomEuropeArtificial IntelligenceCardiologyEmerging TechnologiesHealth and MedicineMachine Learning
NETL
NSTL
Frontiers Media Sa
