Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial

Noseworthy, Peter A.; Attia, Zachi I.; Behnken, Emma; Giblon, Rachel; Bews, Katherine A.; Liu, Sijia; Gosse, Tara A; Linn, Zachery D; Deng, Yihong; Yin, Jun; Gersh, Bernard J.; Graff‐Radford, Jonathan; Rabinstein, Alejandro A.; Siontis, Konstantinos C.; Friedman, Paul A.; Yao, Xiaoxi

doi:10.1016/s0140-6736(22)01637-3

Cited by 119 publications

(54 citation statements)

References 26 publications

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“…Furthermore, the AI-guided assessment took less time for cardiologists to overread and was more consistent with cardiologist assessment from the previous clinical report. Although not the first trial of AI technology in clinical cardiology 21 – 23 , to our knowledge, this study represents the first blinded implementation of a randomized trial in this space.…”

Section: Discussionmentioning

confidence: 99%

Blinded, randomized trial of sonographer versus AI cardiac function assessment

Kwan

Cho

et al. 2023

Nature

103

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Artificial intelligence (AI) has been developed for echocardiography1–3, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpretation workflow. The primary end point was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (more than 5% change). From 3,769 echocardiographic studies screened, 274 studies were excluded owing to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference of −10.4%, 95% confidence interval: −13.2% to −7.7%, P < 0.001 for non-inferiority, P < 0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent previous cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference of −0.96%, 95% confidence interval: −1.34% to −0.54%, P < 0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between the initial assessments by AI versus the sonographer (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was non-inferior to assessment by sonographers.

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Section: Discussionmentioning

confidence: 99%

Blinded, randomized trial of sonographer versus AI cardiac function assessment

Kwan

Cho

et al. 2023

Nature

103

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“…Among these abnormalities, the diagnosis of AF particularly has important medical implications, because it is a leading cardiac cause of stroke, heart failure, and mortality [39]. However, it is challenging to obtain a definitive diagnosis of AF with ECG recordings [30], which is also indicated by the evaluation results as presented in Table 1A.…”

Section: Resultsmentioning

confidence: 99%

Knowledge Discovery with Electrocardiography Using Interpretable Deep Neural Networks

Zhu

Ribeiro

et al. 2022

Preprint

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Despite the potentials of artificial intelligence (AI) in healthcare, very little work focuses on the extraction of clinical information or knowledge discovery from clinical measurements. Here we propose a novel deep learning model to extract characteristics in electrocardiogram (ECG) and explore its usage in knowledge discovery. Utilising a 12-lead ECG dataset (n_ECGs = 2,322,513) collected from unique subjects (n_Subjects = 1,558,772) in primary care, we performed three independent medical tasks with the proposed model: (i) cardiac abnormality diagnosis, (ii) gender identification, and (iii) hypertension screening. We achieved an area under the curve (AUC) score of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (95% CI, 0.963-0.965), and 0.839 (95% CI, 0.837-0.841) for each task, respectively; We provide interpretation of salient morphologies and further identified key ECG leads that achieve similar performance for the three tasks: (i) AVR and V1 leads (AUC=0.990 (95% CI, 0.982-0.995); (ii) V5 lead (AUC=0.900 (95% CI, 0.899-0.902)); and (iii) V1 lead (AUC=0.816 (95% CI, 0.814-0.818)). Using ECGs, our model not only has demonstrated cardiologist-level accuracy in heart diagnosis with interpretability, but also shows its potentials in facilitating clinical knowledge discovery for gender and hypertension detection which are not readily available.

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“…Recent work has shown that artificial intelligence (AI) applied to ECGs can predict incident AF from sinus ECGs [14][15][16] . This work has even been validated in prospective clinical trials 17 , suggesting the value and efficacy of opportunistic screening by AI. Transthoracic echocardiograms (TTEs) are routinely obtained tests in patients with cardiovascular symptoms and individuals who are at high risk for AF.…”

Section: Introductionmentioning

confidence: 81%

Deep Learning Evaluation of Echocardiograms to Identify Occult Atrial Fibrillation

Stein

Duffy

Sandhu

et al. 2023

Preprint

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Background Atrial fibrillation (AF) can often be missed by intermittent screening given its frequently paroxysmal and asymptomatic presentation. Deep learning algorithms have been developed to identify patients with paroxysmal AF from electrocardiograms (ECGs) in sinus rhythm. Transthoracic echocardiograms (TTEs) may provide additional structural information complementary to ECGs that could also be used to help identify occult AF. Objective We sought to determine whether deep learning evaluation of echocardiograms of patients in sinus rhythm could identify occult AF. Methods We identified patients who had TTEs performed between 2004 and 2021. We created a two-stage model that (1) distinguished which TTEs were in sinus rhythm and which were in AF and then (2) predicted which of the TTEs in sinus rhythm were in patients with paroxysmal AF. Models were trained from video-based convolutional neural networks using TTE parasternal long axis (PLAX) videos. The AF prediction performance was compared to prediction using clinical variables, CHADSVASc score, and left atrial (LA) size. Results Our model trained on 111,319 TTE videos distinguished TTEs in AF from those in sinus rhythm with high accuracy (AUC 0.96, 0.95-0.96). A total of 72,181 TTE videos were in sinus rhythm. When tested on a held-out sample, the model predicted the occurrence of concurrent AF with an AUC of 0.71 (0.69-0.73). Using the max F1 threshold, the PPV was 0.20 and the NPV was 0.95. The model performed better than predicting concurrent AF using clinical risk factors (AUC 0.67, 0.65-0.69), LA area (AUC 0.63, 0.62-0.64), and CHADSVASc (AUC 0.61, 0.60-0.62). Conclusion A deep learning model distinguished AF from sinus rhythm TTEs with high accuracy and predicted the presence of AF within 90 days of sinus rhythm TTEs moderately well, better than clinical variables or LA size alone. TTEs may help inform automated opportunistic AF screening efforts.

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Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial

Cited by 119 publications

References 26 publications

Blinded, randomized trial of sonographer versus AI cardiac function assessment

Blinded, randomized trial of sonographer versus AI cardiac function assessment

Knowledge Discovery with Electrocardiography Using Interpretable Deep Neural Networks

Deep Learning Evaluation of Echocardiograms to Identify Occult Atrial Fibrillation

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