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1000
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Abstract/Summary
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To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 years). The most common indications were known or suspected ischemic heart disease, cardiomyopathies or myocarditis. Fully automated analysis of LV volumes and function was performed using a deep learning-based algorithm. The analysis was subsequently corrected by a senior cardiovascular radiologist. Manual volumetric analysis was performed by two radiology trainees. Volumetric results were compared using Bland-Altman statistics and intra-class correlation coefficient. The frequency of clinically relevant differences was analysed using re-classification rates. The fully automated volumetric analysis was completed in a median of 8 s. With expert review and corrections, the analysis required a median of 110 s. Median time required for manual analysis was 3.5 min for a cardiovascular imaging fellow and 9 min for a radiology resident (p < 0.0001 for all comparisons). The correlation between fully automated results and expert-corrected results was very strong with intra-class correlation coefficients of 0.998 for end-diastolic volume, 0.997 for end-systolic volume, 0.899 for stroke volume, 0.972 for ejection fraction and 0.991 for myocardial mass (all p < 0.001). Clinically meaningful differences between fully automated and expert corrected results occurred in 18% of cases, comparable to the rate between the two manual readers (20%). Deep learning-based fully automated analysis of LV volumes and function is feasible, time-efficient and highly accurate. Clinically relevant corrections are required in a minority of cases.
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1000
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Hinweis
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DeepGreen-ID: b01b615b46444388a502b5fc42749c54 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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