IMPORTANCE Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.OBJECTIVE To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software. DESIGN, SETTING, AND PARTICIPANTSThis prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance.INTERVENTIONS Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3-trained echocardiographers independently and blindly evaluated each acquisition.MAIN OUTCOMES AND MEASURES Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers.RESULTS A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters.CONCLUSIONS AND RELEVANCE This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.

Background: We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. Methods: Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%–73%), mildly-to-moderately (30%–52%), or severely reduced (<30%). Additionally, LV function was visually estimated for each view by 10 experienced physicians. Accuracy of the detection of reduced LV function (EF<53%) by the automated classification and physicians’ interpretation was assessed against the reference classification. In protocol 2, we tested the new machine-learning algorithm in the POC setting on images acquired by nurses using a portable imaging system. Results: Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86–0.95), with biases <2%. Machine-learning classification of LV function showed similar accuracy to that by physicians in most views, with only 10% to 15% cases where it was less accurate. Protocol 2: the agreement with the reference values was excellent (intraclass correlation=0.84) with a minimal bias of 2.5±6.4%. Conclusions: The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.

Background A novel, recently FDA-authorized software uses deep learning (DL) to provide prescriptive transthoracic echocardiography (TTE) guidance, allowing novices to acquire standard TTE views. The DL model was trained by &gt;5,000,000 observations of the impact of probe motion on image orientation/quality. This study evaluated whether novice-acquired TTE images guided by this software were of diagnostic quality in patients with and without implanted electrophysiological (EP) devices, focusing on RV size and function, which were thought to be sensitive to EP devices. Some aspects of the study have previously been presented. Methods 240 patients (61±16 years old, 58% male, 33% BMI &gt;30 kg/m2, 91% with cardiac pathology) were recruited. 8 nurses without echo experience each acquired 10 view TTEs in 30 patients guided by the software. 235 of the patients were also scanned by a trained sonographer without assistance from the software. 5 Level 3 echocardiographers independently assessed the diagnostic quality of the TTEs acquired by the nurses and sonographers to evaluate the effect of EP devices on DL software performance. Results Nurses using the AI-guided acquisition software acquired TTEs of sufficient quality to make qualitative assessments of right ventricular (RV) size and function in greater than 80% of cases for patients with and without implanted EP devices (Table). There was no significant difference between nurse- and sonographer-acquired scans. Conclusion These results indicate that new DL software can guide novices to obtain TTEs that enable qualitative assessment of RV size even in the presence of implanted EP devices. The results of the comparison to sonographer-acquired exams indicate the software performance is robust to presence of pacemaker/ICD leads visible in the images (Figure). Nurse-acquired TTE with visible ICD lead Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Caption Health, Inc.

Funding Acknowledgements Bay Labs, Inc; San Francisco, CA Background/Introduction: When used by experienced examiners, the utility of point-of-care (POC) ultrasound for assessing cardiac anatomy and function has been well established. However, in some clinical circumstances (Primary Care offices, Intensive Care Unit, some Emergency Rooms, or in remote settings) in which a rapid assessment of cardiac anatomy and dynamics can facilitate patient care, an examiner experienced at POC scanning may not be immediately available. Purpose To help novice users acquire clinically useful standard cardiac views using novel machine learning (ML) software. Methods We used an investigational device that employs ML software to provide real-time adaptive guidance of transducer position and orientation to help novice users acquire tomographic views of the heart. We tested the utility of this approach when 4 nurses with no prior training in sonography performed POC studies on 16 subjects (10 healthy, 6 with cardiac abnormalities; 9 men; body mass index normal in 6, overweight in 6, and obese in 4 subjects). Each nurse underwent didactic training and 4 hours of supervised practice using the ML program. Each nurse scanned each study subject using a scanner equipped with ML software to acquire 10 digital two-dimensional image clips, including: parasternal long axis, short axis at the aortic valve, mitral valve, and mid-left ventricle (LV), apical 2-, 4-, and 5-chamber, subcostal 4-chamber, and longitudinal views of the inferior vena cava (IVC). All video clips (n = 640) were later reviewed independently by 5 level 3-trained cardiologists who were blinded to subject, scanner, and each other"s assessments. The expert readers reviewed each set of 10 clips to determine if the following variables could be assessed qualitatively: LV size and function; right ventricular (RV) size and function; aortic, mitral and tricuspid valves; pericardial effusion; left atrial size; IVC size. Results The majority of expert readers concurred, independently, that the sets of images acquired by nurses using ML guidance allowed qualitative assessment of LV size and function in 98%, pericardial effusion in 98%, RV size and function in 92%, and aortic and mitral valve anatomy and dynamics in 94-97% of cases. Qualitative assessment of LA size was feasible in 95%. Images of the IVC were judged as adequate for assessment in 58%. Conclusion This preliminary study suggests the potential value of novel ML software by demonstrating that nurses with limited training can acquire tomographic images useful for qualitative assessment of the cardiac chambers and valves in more than 90% of the subjects examined. This approach might be useful when timely POC cardiac assessment is indicated in settings where an experienced examiner is not available. Further refinements in the guiding software are needed to improve the success rate of IVC imaging, since IVC size can be a useful indicator of volume status.

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