Artificial Intelligence-assisted evaluation of cardiac function by oncology staff in chemotherapy patients

Abstract

Left ventricular ejection fraction (LVEF) calculation by echocardiography is pivotal in evaluating cancer patients’ cardiac function. Artificial intelligence (AI) can facilitate acquisition of optimal images and automated LVEF calculation. We sought to evaluate the feasibility and accuracy of LVEF calculation by oncology staff using an AI-enabled handheld ultrasound device (HUD). We studied 115 patients referred for echocardiographic LVEF estimation. All patients were scanned by a cardiologist using standard echocardiography (SE) and biplane Simpson’s LVEF was the reference standard. Hands-on training using the Kosmos HUD was provided to the oncology staff before the study. Each patient was scanned by a cardiologist, a senior oncologist, an oncology resident, and a nurse using the TRIO AI and KOSMOS EF deep learning algorithms to obtain automated LVEF (autoEF). The correlation between autoEF and SE-EF was excellent for the cardiologist (r = 0.90), the junior oncologist (r = 0.82) and the nurse (r = 0.84), and good for the senior oncologist (r = 0.79). The Bland-Altman analysis showed small underestimation by autoEF compared to SE-EF. Detection of impaired LVEF < 50% was feasible with sensitivity 95% and specificity 94% for the cardiologist; sensitivity 86% and specificity 93% for the senior oncologist; sensitivity 95% and specificity 91% for the junior oncologist; sensitivity 94% and specificity 87% for the nurse. Automated LVEF calculation by oncology staff was feasible using AI-enabled HUD in a selected patient population. Detection of LVEF < 50% was possible with good accuracy. These findings show potential to expedite clinical workflow of cancer patients and speed up referral when necessary.