Artificial intelligence-guided image acquisition on patients with implanted electrophysiological devices: results from a pivotal prospective multi-center clinical trial

Abstract

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 >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 >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.

Use of Artificial Intelligence to Assess Cardiac Function by Echocardiography: Systematic Review of the State of the Art

“…Much of the literature focuses on AI interpretation or analysis of already acquired images, but there is growing evidence regarding the use of AI technology for image acquisition by non-experts. 5,[19][20][21][22][23][24] Automated technology can guide a non-expert examiner by recognizing incorrect images and the need for additional views or enhancements. It can also help standardize image acquisition and measurements.…”

“…1,6 In this way, it would be a tool used to optimize training and echocardiographic studies, 5,25,26 with the possibility of acquiring images of diagnostic quality comparable to those acquired manually by experts. 5,19,20,[22][23][24] The technology is applicable in clinical practice, in emergencies, and in remote areas with scarce resources and few experienced professionals. 5,21,25,27 Integration with clinical data…”

Uso da Inteligência Artificial para Avaliação da Função Cardíaca pela Ecocardiografia: Revisão Sistemática do Estado da Arte

“…Grande parte da literatura se concentra na interpretação ou análise pela IA de imagens já adquiridas, mas há evidências crescentes sobre o uso da tecnologia de IA para aquisição de imagens por não especialistas. 5,[19][20][21][22][23][24] A tecnologia automatizada pode orientar um examinador não especialista, reconhecendo…”

“…1,6 Dessa forma, seria uma ferramenta utilizada para otimizar o treinamento e os estudos ecocardiográficos, 5,25,26 sendo possível adquirir imagens de qualidade diagnóstica comparáveis às adquiridas manualmente por especialistas. 5,19,20,[22][23][24] A tecnologia é aplicável na prática clínica, em emergências e em áreas remotas com recursos escassos e poucos profissionais experientes. 5,21,25,27…”

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

“…Attia et al ( 57 ) conducted a prospective study to validate a DL algorithm that detected left ventricular systolic dysfunction. Another pivotal prospective multicentre trial was launched to demonstrate the feasibility of a ML-powered image guided acquisition software that helps novices to perform transthoracic echocardiography studies ( 58 ). Lastly, a validation study was performed to prove the feasibility of using DL to automatically segment and quantify the ventricular volumes in cardiac MRI ( 35 ).…”