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The three biggest takeaways from this article are? Journal Article Editor’s Choice Automated detection of low ejection fraction from a one-lead electrocardiogram: application of an AI algorithm to an electrocardiogram-enabled Digital Stethoscope , , , , , , , , , … Show more Author Notes European Heart Journal – Digital Health, Volume 3, Issue 3, September 2022, Pages 373-379, https://doi.org/10.1093/ehjdh/ztac030 Published: 23 May 2022 Article history PDF Split View Cite Permissions Icon Permissions Share Icon Share Abstract Aims Electrocardiogram (ECG)-enabled stethoscope (ECG-Scope) acquires a single-lead ECGs during cardiac auscultation and may facilitate real-time screening for pathologies not routinely identified by cardiac auscultation alone. We previously demonstrated an artificial intelligence (AI) algorithm can identify left ventricular dysfunction (LVSD) [defined as ejection fraction (EF) ≤ 40%] with an area under the curve (AUC) of 0.91 using a 12-lead ECG. Methods and results One hundred patients referred for clinically indicated echocardiography were prospectively recruited. ECG-Scope recordings with the patient supine and sitting were obtained in multiple electrode locations at the time of the echocardiogram. The AI algorithm for the detection of LVSD was retrained using single leads from ECG-12 and validated against ECG-Scope to determine accuracy for low EF detection (≤35%, <40%, or <50%). We evaluated the algorithm with respect to body position and lead location. Amongst 100 patients (aged 61.3 ±â€‰13.8; 61% male, BMI: 30.0 ±â€‰5.4), eight had EF≤40%, and six had EF 40-50%. The best single recording position was V2 with the patient supine [AUC: 0.88 (CI: 0.80-0.97) for EF≤35%, 0.85 (CI: 0.75-0.95) for EF≤40%, and 0.81 (CI: 0.71-0.90) for EF < 50%]. When using an AI model to select the recording automatically, AUC was 0.91 (CI: 0.84-0.97) for EF≤35%, 0.89 (CI: 0.83-0.96) for EF≤40%, and 0.84 (CI: 0.73-0.94) for EF < 50%. Conclusion An AI algorithm applied to an ECG-enabled stethoscope recording in standard auscultation positions reliably detected the presence of a low EF in this prospective study of patients referred for echocardiography. The ability to screen patients with a possible low EF during routine physical examination may facilitate rapid detection of LVSD. Graphical Abstract Open in new tabDownload slide Artificial intelligence, Electrocardiogram, Stethoscope, Ejection fraction, Heart failure Topic: electrocardiogram echocardiography artificial intelligence stethoscopes ejection fraction Issue Section: Original Article Introduction Since the invention of the stethoscope in 1819, it has become well recognized that the stethoscope is an essential component of any diagnostician's armamentarium—that the science of auscultation can serve the well-trained physician in not only identifying diseases early in their course but to also augment findings obtained via other, often more costly tests. However, while auscultation may offer one perspective on cardiac health, the electrocardiogram (ECG) may offer additional value. Recent data has suggested that, by applying algorithms derived out of large, well-annotated data sets using various artificial intelligence (AI) techniques, it is possible to determine potassium values, patient age or sex, or the presence of a low ejection fraction (EF) from the ECG alone.1-3 In particular, in the case of a low EF, whereas there are well-recognized treatments to reduce associated morbidity and mortality, around 8% of the population may be otherwise asymptomatic and go undiagnosed.4 Thus, as a low EF may not always be obvious during routine clinical examination, the ability of the ECG to automatically suggest the presence of a low EF and to drive the clinician to refer for confirmatory testing (e.g. echocardiography) is clear. However, widespread acquisition of routine 12-lead ECGs in ostensibly healthy patients is neither cost-effective nor efficient. A recent technology has demonstrated the ability to acquire a one-lead, digital ECG during routine cardiac auscultation.5 Applying AI-enabled, ECG-derived clinical diagnostic algorithms to the signals acquired by such a device may allow for early identification of clinical pathology during the routine physical examination. Thus, we sought to evaluate the predictive accuracy of using such an ECG-enabled digital stethoscope in automatically identifying patients with a low EF via a previously reported AI algorithm derived from 12-lead ECGs. Methods According to a Mayo Clinic Institutional Review Board (IRB) approved protocol, 100 consecutive patients were approached for inclusion in the study. All patients were referred for outpatient transthoracic echocardiography for any indication to the Mayo Echocardiography Laboratory. Informed consent was obtained immediately before their echocardiogram. Electrocardiogram/heart sound acquisition After obtaining patient informed consent, the ECG-enabled stethoscope (Eko DUO, Eko Devices, Inc; Oakland, CA) was applied to the patient's chest in a variety of locations (Figure 1) both supine and sitting by a single operator (J.D.), ECG was recorded in 500 Hz for 15 s in each position. The AI algorithm (described below) was applied to all single-lead ECGs obtained to determine a probability score (0-1) of the patients having a low EF (defined as ≤35%, ≤40%, or <50%). Figure 1 ECG positions obtained using digital stethoscope. Shown are the various positions in which ECGs were obtained with the patient both supine and sitting. A total of five ECG positions as labeled in the figure (Lead I with fingers from either hand against each electrode; a modified V5, modified V2, an angled position in the left upper sternal border, and a horizontal position at the level of the clavicle) were obtained for a total of 10 single-lead ECGs. Open in new tabDownload slide Construction of neural network for use on single-lead electrocardiogram The development, validation, and network architecture for the 12-lead ECG EF prediction algorithm has been previously published.3 To retool the network for use on a single-lead ECG, a lead agnostic training method to allow the 12-lead AI-ECG model to function off of a single lead was performed. This consisted of retraining the model to use every individual lead as a unique, independent lead for the purposes of identifying a low EF similar to the methods described previously.3 This was carried out using the same cohort used to develop the original mode, containing 35 970 patients (of those 3894 patients had an EF≤40%) used for seeding the network weights (training set), and 8989 patients (of those 990 patients had an EF≤40%) used for internal validation, by changing the model architecture described in3 from a convolutional neural networks with an input of (12 × 5000) to (1 × 5000), as the original model was operating on each lead separately in all layers except for one that used to combined the features from the different leads (the 'spatial block' in the original manuscript), we removed the 'spatial block' and retrained the model, during training we fed each of the leads from the original 12 leads as an independent sample after normalizing it to have an maximum absolute amplitude of 1 (au), that is not affected from the ECG polarity. For the testing set from the original derivation cohort, the area under the curve (AUC) from averaging the scores for all 12 leads tested independently, generating a per-patient score, was 0.9 for detection of EF≤35%. Owing to the variability in ECGs recorded using a mobile form factor placed on the patient chest in different locations, we normalized all data sets used in this study to have a maximum amplitude of 1 unit. In addition, when training and testing the algorithm, we used one version in which the ECG was fed to the network as-is, and one version when the ECG amplitude was multiplied by '−1' to mimic a situation when the device electrodes are in opposite orientation. In the testing stage, the score of both versions was averaged to make the model invariant to electrode reversal.

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