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User experience and image quality influence on performance of automated real-time quantification of left ventricular
Insights
Image quality impacts the feasibility of automated measurements on handheld ultrasound devices for heart failure diagnosis. However, it does not explain the low agreement or reliability of these tools across different user expertise levels.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Echocardiography is crucial for heart failure (HF) diagnosis, yet expert availability is limited.
- Handheld ultrasound devices (HUDs) offer potential for broader HF assessment, but their clinical utility by non-experts requires evaluation.
- Automated quantification tools for Left Ventricular Ejection Fraction (autoEF) and Mitral Annular Plane Systolic Excursion (autoMAPSE) are integrated into HUDs.
Purpose of the Study:
- To investigate the influence of user experience and image quality on the performance of autoEF and autoMAPSE on HUDs.
- To assess how image quality affects the feasibility, agreement, and reliability of these automated tools in patients with suspected HF.
Main Methods:
- Diagnostic accuracy study involving novice (general practitioners), intermediate (cardiac nurses), and expert (cardiologists) users.
- Evaluation of 2543 images by a blinded cardiologist using a standardized five-parameter image quality score (0-6).
Main Results:
- Higher image quality correlated with increased feasibility of autoEF and autoMAPSE across all users.
- Image quality was more critical for autoMAPSE feasibility than autoEF feasibility.
- Image quality did not significantly impact the agreement of autoEF (R² 2%) or autoMAPSE (R² 7%) with reference standards.
- Lower reliability was observed with greater within-patient variability in image quality during repeated recordings (p≤0.005).
Conclusions:
- While image quality is vital for the feasibility of automated cardiac measurements on HUDs, it does not account for the observed low agreement or modest reliability.
- Further research is needed to improve the performance and consistency of automated decision-support software on HUDs for heart failure assessment.
Background And Objectives:
Echocardiography is the cornerstone of heart failure (HF) diagnosis, but expertise is limited. Non-experts using handheld ultrasound devices (HUDs) challenge the clinical yield. Left ventricular (LV) ejection fraction (EF) is used for assessment and grading of HF. Mitral annular plane systolic excursion (MAPSE) reflects LV long-axis shortening. Automatic tools for quantification of EF (autoEF) and MAPSE (autoMAPSE) are available on HUDs. We aimed to explore the importance of user experience and image quality for autoEF and autoMAPSE on HUDs, and how image quality influences the feasibility, agreement and reliability in patients with suspected HF.
Methods:
General practitioners, registered cardiac nurses and cardiologists represented the novice, intermediate and expert users, respectively, in this diagnostic accuracy study. 2543 images were evaluated by an external, blinded cardiologist by a five-parameter, prespecified score (four-chamber view, LV alignment, apical mispositioning, mitral annular assessment and number of visible endocardial segments) graded 0-6.
Results:
Feasibility was higher with increasing image quality. In all recordings, irrespective of user, the average image quality score and the five prespecified scores were associated with the feasibility of autoEF and autoMAPSE (all p<0.001). Image quality was more important for the feasibility of autoMAPSE than autoEF. Image quality was not important for the agreement of autoEF (R2 2%) and autoMAPSE (R2 7%). Combining all user groups, the reliability was lower with larger within-patient variability in image quality of the repeated recordings (p≤0.005). Similar associations were not found in user group specific analyses (p≥0.16). Patients' characteristics were only weakly associated with image quality score (R2≤4%).
Discussion:
Image quality was important for feasibility but does not explain the low agreement with reference or the modest within-patient reliability of automatic decision-support software on HUDs for all user groups in patients with suspected HF.
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