Details

Autor(en) / Beteiligte

• Sveric, Krunoslav Michael
• Ulbrich, Stefan
• Dindane, Zouhir
• Winkler, Anna
• Botan, Roxana
• Mierke, Johannes
• Trausch, Anne
• Heidrich, Felix
• Linke, Axel

Titel

Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging

Ist Teil von

• International journal of cardiology, 2024-01, Vol.394, p.131383-131383, Article 131383

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Left ventricular ejection fraction (LVEF) measurement in echocardiography (Echo) using the recommended modified biplane Simpson (MBS) method is operator-dependent and exhibits variability. We aimed to assess the accuracy of a novel fully automated (Auto) artificial intelligence (AI) in view selection and biplane LVEF calculation compared to MBS-Echo, with cardiac magnetic resonance imaging (CMR) as reference. Each of the 301 consecutive patients underwent CMR and Echo on the same day. LVEF was measured independently by Auto-Echo, MBS-Echo and CMR. Interobserver (n = 40) and test-retest (n = 14) analysis followed. A total of 229 patients (76%) underwent complete analysis. Auto-Echo and MBS-Echo showed high correlations with CMR (R = 0.89 and 0.89) and with each other (R = 0.93). Auto underestimated LVEF (bias: 2.2%; limits of agreement [LOA]: −13.5 to 17.9%), while MBS overestimated it (bias: -2.2%; LOA: 18.6 to 14.1%). Despite comparable areas under the curves of Auto- and MBS-Echo (0.93 and 0.92), 46% (n = 70) of MBS-Echo misclassified LVEF by ≥5% units in patients with a reduced CMR-LVEF <51%. Although LVEF bias variability across different LV function ranges was significant (p < 0.001), Auto-Echo was closer to CMR for patients with reduced LVEF, wall motion abnormalities, and poor image quality than MBS-Echo. The interobserver correlation coefficient of Auto-Echo was excellent compared to MBS-Echo (1.00 vs. <0.91) for different readers. True test-retest variability was higher for MBS-Echo than for Auto-Echo (7.9% vs. 2.5%). The tested AI has the potential to improve the clinical utility of Echo by reducing user-related variability, providing more accurate and reliable results than MBS. [Display omitted] •This comparative study establishes CMR as a reliable reference method for evaluating AI-based LVEF measurements.•The tested AI demonstrates strong feasibility for LV view selection and LVEF measurement in real-world clinical Echo.•High reproducibility in LVEF measurements across temporal datasets indicates robust test-retest reliability of the AI system.•This AI significantly enhances LVEF measurement accuracy compared to human operators using the conventional MBS method.•Reduction in measurement variability highlights AI's capacity to provide consistent and objective LVEF assessments.