Use of artificial intelligence for the automatic assessment of left ventricular ejection fraction by oncology staff in chemotherapy patients

Abstract Funding Acknowledgements Type of funding sources: None. Background The calculation of LV ejection fraction (LVEF) by transthoracic echocardiography is pivotal in detecting cancer therapy–related cardiac dysfunction. Referrals for LVEF estimation pre- and post-chemotherapy occupy significant amount of resources of echocardiography laboratories and increase service deliverance. Novel handheld ultrasound devices (HUDs) can provide echocardiographic images at the point of care with diagnostic image quality. Recently, artificial intelligence (AI) technology enabled the development of algorithms for the real-time guidance of ultrasound probe to acquire optimal images of the heart and calculate LVEF automatically. Purpose To evaluate the feasibility and accuracy of LVEF calculation by oncology staff using an AI enabled HUD. Methods We studied 115 oncology patients referred for echocardiographic LVEF estimation. All patients were scanned by a cardiologist using standard echocardiography (SE) systems and biplane Simpson’s rule was used as reference standard. A brief training on echocardiography basics and use of HUD was provided to the oncology staff before the study. Then, each patient was scanned independently by a cardiologist, a senior oncologist, an oncology resident, and an oncology nurse using the AUTO-GUIDANCE and AUTO-GRADING AI applications of the HUD (Figure 1) to acquire apical 4-chamber and 2-chamber views of the heart. The LVEF was automatically calculated by the device autoEF algorithm. Method agreement was assessed using Pearson’s correlation and Bland-Altman analysis. The diagnostic accuracy for detection of impaired LVEF