Artificial intelligence-derived left ventricular strain in echocardiography in patients treated with chemotherapy.
Journal:
The international journal of cardiovascular imaging
PMID:
39042233
Abstract
Global longitudinal strain (GLS) is an echocardiographic measure to detect chemotherapy-related cardiovascular dysfunction. However, its limited availability and the needed expertise may restrict its generalization. Artificial intelligence (AI)-based GLS might overcome these challenges. Our aims are to explore the agreements between AI-based GLS and conventional GLS, and to assess whether the agreements were influenced by expertise levels, cardiac remodeling and cardiovascular diseases/risks. Echocardiographic images in the apical four-chamber view of left ventricle were retrospectively analyzed based on AI-based GLS in patients treated with chemotherapy, and correlation between AI-based GLS (Caas Qardia, Pie Medical Imaging) and conventional GLS (Vivid E9/VividE95, GE Healthcare) were assessed. The agreement between unexperienced physicians ("GLS beginner") and experienced echocardiographer were also assessed. Among 94 patients (mean age 69 ± 12 years, 73% female), mean left ventricular ejection fraction was 64 ± 6%, 14% of patients had left ventricular hypertrophy, and 21% had left atrial enlargement. Mean GLS was - 15.9 ± 3.4% and - 19.0 ± 3.7% for the AI and conventional method, respectively. There was a moderate correlation between these methods (rho = 0.74; p < 0.01), and bias was - 3.1% (95% limits of agreement: -8.1 to 2.0). The reproducibility between GLS beginner and an experienced echocardiographer was numerically better in the AI method than the conventional method (inter-observer agreement = 0.82 vs. 0.68). The agreements were consistent across abnormal cardiac structure and function categories (p-for-interaction > 0.10). In patients treated with chemotherapy. AI-based GLS was moderately correlated with conventional GLS and provided a numerically better reproducibility compared with conventional GLS, regardless of different levels of expertise.
Authors
Keywords
Aged
Aged, 80 and over
Antineoplastic Agents
Artificial Intelligence
Cardiotoxicity
Echocardiography
Female
Heart Ventricles
Humans
Image Interpretation, Computer-Assisted
Male
Middle Aged
Neoplasms
Observer Variation
Predictive Value of Tests
Reproducibility of Results
Retrospective Studies
Stroke Volume
Ventricular Dysfunction, Left
Ventricular Function, Left
Ventricular Remodeling