A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF

Abstract

Left ventricular ejection fraction (LVEF) is the most important parameter in the assessment of cardiac function. A machine-learning algorithm was trained to guide ultrasound-novices to acquire diagnostic echocardiography images. The artificial intelligence (AI) algorithm then estimates LVEF from the captured apical-4-chamber (AP4), apical-2-chamber (AP2), and parasternal-long-axis (PLAX) loops. We sought to test this algorithm by having first-year medical students without previous ultrasound knowledge scan real patients. Nineteen echo-naïve first-year medical students were trained in the basics of echocardiography by a 2.5 h online video tutorial. Each student then scanned three patients with the help of the AI. Image quality was graded according to the American College of Emergency Physicians scale. If rated as diagnostic quality, the AI calculated LVEF from the acquired loops (monoplane and also a “best-LVEF” considering all views acquired in the particular patient). These LVEF calculations were compared to images of the same patients captured and read by three experts (ground-truth LVEF [GT-EF]). The novices acquired diagnostic-quality images in 33/57 (58%), 49/57 (86%), and 39/57 (68%) patients in the PLAX, AP4, and AP2, respectively. At least one of the three views was obtained in 91% of the attempts. We found an excellent agreement between the machine’s LVEF calculations from images acquired by the novices with the GT-EF (bias of 3.5% ± 5.6 and r = 0.92, p < 0.001 in the “best-LVEF” algorithm). This pilot study shows first evidence that a machine-learning algorithm can guide ultrasound-novices to acquire diagnostic echo loops and provide an automated LVEF calculation that is in agreement with a human expert.

Artificial intelligence in medical ultrasonography: driving on an unpaved road

“…The software allows clinicians to easily utilize 3D anatomical information diagnostically by using the AI's algorithm to obtain optimal images. In addition, the AI algorithm is capable of recording loops of echo data that allow it to calculate a left ventricular ejection fraction (LVEF) that is in agreement with human experts (1). Similarly, there have been advances in obstetric and gynecologic research pertaining to ultrasound including automatic detection of endometrial thickness and automatic classification of ovarian cysts (2).…”

“…For example, in echocardiography and obstetric pelvic ultrasound, where measurement and visual analysis are required, application of video clip could provide a full set of relevant structured data allowing spatiotemporal analysis maximizing the advantages of deep learning. To this end, in February 2020 a cardiac ultrasound software utilizing AI called Caption Guidance received first FDA approval for its algorithm which can calculate ejection fraction from the best auto-capture three-dimensional video clips (1). The software allows clinicians to easily utilize 3D anatomical information diagnostically by using the AI's algorithm to obtain optimal images.…”

Machine Learning and Precision Medicine in Emergency Medicine: The Basics

“…Significant progress has been made in using DL to guide image acquisition, measure ejection fraction, measure wall thickness, detect wall motion abnormalities, assess ventricular function, and assess valvular function. DL has also been applied to help make more advanced diagnoses including heart failure with preserved ejection fraction, cardiomyopathy, amyloidosis, and pulmonary hypertension [20][21][22][23][24]. [27].…”

Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint

“…However, there has been concern that the level of training of medical staff performing echocardiography in other medical specialties is not sufficient to yield accurate and reliable results. For example, incorrect quantification of left ventricular ejection fraction (LVEF) may lead to inappropriate clinical decisions [ 3 ], which may potentially harm patients and increase healthcare costs [ 4 ]. Moreover, almost all examinations in echocardiography are based on the locations of the heart views.…”

“…In order to obtain a consistent examination of echocardiography, especially in primary and emergency care settings, it is important to reduce dependence on operators [ 4 ]. Artificial intelligence is expected to provide automated analyzing tools [ 6 ].…”