Abstract Body (Do not enter title and authors here): Background: Global longitudinal strain (GLS) is considered the most sensitive echocardiographic marker for detecting cardiotoxicity and subclinical changes in cardiac function in patients receiving cardiotoxic therapies. Despite literature that has shown less inter-observer variability for GLS compared to left ventricular ejection fraction, there remains significant variability in GLS measurement across observers and vendors. US2.AI is a novel, machine learning-based artificial intelligence (AI) algorithm for automated echocardiographic interpretation. We aim to demonstrate the feasibility of AI-based GLS assessment and hypothesize that AI-based GLS measurement is more reproducible compared to human-based measurement

Methods: A total of 27 transthoracic echocardiograms were selected at random from a population of female cardio-oncology breast cancer patients and were run through the US2.AI algorithm twice to calculate GLS. The same echocardiograms were then analyzed by two board-certified cardiologists in a blinded fashion to calculate GLS using QLAB software (Philips). The two methods of GLS measurement (AI versus humans) were compared using linear regression, as assessed by the Pearson correlation coefficient. Reproducibility for each method was assessed using the intra-class correlation coefficient (ICC).

Results: A total of 27 studies were analyzed with a mean age of 55.6 ± 11.3 and all patients were female. The feasibility of US2.AI for the assessment of GLS was demonstrated with a strong correlation between AI-based and human reads (r = 0.80, p < 0.00001). The reproducibility of AI-based GLS assessment was superior to that of human readers as assessed by an ICC of 1.0 versus 0.52, respectively.

Conclusion: AI-based assessment of GLS was shown to be both feasible and more reproducible compared to expert human readers in a population of breast cancer patients receiving cardiotoxic chemotherapy. Given the important clinical implications of GLS in cardio-oncology patients, more widespread implementation of AI-based strain assessment has the potential to significantly improve the reproducibility of GLS measurement in this unique patient population.