October 26, 2023European Heart Journal - Cardiovascular Imaging48 citationsOpen Access
View Full PaperAutomatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases
Structured PICO
Does fully automatic AI measurement of LV volumes and EF reduce acquisition and processing time and maintain accuracy compared to manual measurements in patients undergoing echocardiography?
P
Population
2,820 subjects across four scenarios: 50 consecutive patients for real-time scanning, 40 subjects for repeated echocardiographic examinations, 1,881 subjects from an internal research database, and 849 subjects from an external research database.
I
Intervention
Fully automatic measurements of left ventricular (LV) volumes and ejection fraction (EF) by a novel artificial intelligence (AI) software.
C
Comparator
Manual measurements (standard clinical workflow by experts).
O
Outcome
Total acquisition and processing time, test-retest reproducibility, and agreement with reference measurements.surrogate
AI-based automatic measurement of LV volumes and EF significantly reduces echocardiography processing time by 77% while maintaining accuracy and reproducibility comparable to expert manual measurements.
Abstract
AIMS: Echocardiography is a cornerstone in cardiac imaging, and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully automatic measurements of LV volumes and EF both during scanning and in stored recordings. The aim of this study was to evaluate the impact of implementing AI measurements on acquisition and processing time and test-retest reproducibility compared with standard clinical workflow, as well as to study the agreement with reference in large internal and external databases. METHODS AND RESULTS: Fully automatic measurements of LV volumes and EF by a novel AI software were compared with manual measurements in the following clinical scenarios: (i) in real time use during scanning of 50 consecutive patients, (ii) in 40 subjects with repeated echocardiographic examinations and manual measurements by 4 readers, and (iii) in large internal and external research databases of 1881 and 849 subjects, respectively. Real-time AI measurements significantly reduced the total acquisition and processing time by 77% (median 5.3 min, P < 0.001) compared with standard clinical workflow. Test-retest reproducibility of AI measurements was superior in inter-observer scenarios and non-inferior in intra-observer scenarios. AI measurements showed good agreement with reference measurements both in real time and in large research databases. CONCLUSION: The software reduced the time taken to perform and volumetrically analyse routine echocardiograms without a decrease in accuracy compared with experts.