Thirty-Day High-Grade Aortic Valve Block Post-Transcatheter Aortic Valve Replacement in Patients Discharged on Heart Rhythm Monitor.

Background: Conduction disease is an important and common complication post-transcatheter aortic valve replacement (TAVR). Previously, we developed a conduction disease risk stratification and management protocol post-TAVR. This study aims to evaluate high-grade aortic valve block (HAVB) incidence and risk factors in a large cohort undergoing ambulatory cardiac monitoring post-TAVR according to conduction risk grouping. Methods: This single-center, retrospective study evaluated all patients discharged on ambulatory cardiac monitoring between 2016 and 2021 and stratified them into 3 groups based on electrocardiogram predictors of HAVB risk (group 1 [low], group 2 [intermediate], and group 3 [high]). HAVB was defined as ≥2 consecutive nonconducted P waves in sinus rhythm or bradycardia <50 beats/minute with a fixed rate for atrial fibrillation/flutter. Descriptive statistics were used to show the incidence and timeline, while logistic regression was utilized to evaluate predictors of HAVB. Results: Five hundred twenty-eight patients were included (median age 80 years [74-85]; 43.8% female). Forty-one patients (7.8%) developed HAVB during ambulatory monitoring (68% were asymptomatic). Over a median follow-up of 2 years (1.3-2.7), the overall mortality rate was 15.0% (30-day mortality rate of 0.57%, n = 3). Risk factors for HAVB were male sex (odds ratio [OR] = 2.46, p = 0.02, 95% CI = 1.21-5.43), baseline right bundle branch block (OR = 2.80, p = 0.01, 95% CI = 1.17-6.19), and post-TAVR QRS >150 â€‹ms (OR = 2.16, p = 0.03, 95% CI = 1.01-4.40). The negative predictive value for patients in groups 1 and 2 for 30-day HAVB was 95.0 and 93.8%, respectively. Conclusions: The risk of 30-day HAVB post-TAVR on ambulatory monitoring post-TAVR varies according to post-TAVR electrocardiogram findings, and a 3-group algorithm effectively identifies groups with a low negative predictive value for HAVB.

Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development.

AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987-2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0-7 years), peripubertal (8-14 years), and postpubertal (15-18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model's discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.

Artificial intelligence-based screening for cardiomyopathy in an obstetric population: A pilot study.

Background: Cardiomyopathy is a leading cause of pregnancy-related mortality and the number one cause of death in the late postpartum period. Delay in diagnosis is associated with severe adverse outcomes. Objective: To evaluate the performance of an artificial intelligence-enhanced electrocardiogram (AI-ECG) and AI-enabled digital stethoscope to detect left ventricular systolic dysfunction in an obstetric population. Methods: We conducted a single-arm prospective study of pregnant and postpartum women enrolled at 3 sites between October 28, 2021, and October 27, 2022. Study participants completed a standard 12-lead ECG, digital stethoscope ECG and phonocardiogram recordings, and a transthoracic echocardiogram within 24 hours. Diagnostic performance was evaluated using the area under the curve (AUC). Results: One hundred women were included in the final analysis. The median age was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, and 21% as Hispanic. Five percent and 6% had left ventricular ejection fraction (LVEF) <45% and <50%, respectively. The AI-ECG model had near-perfect classification performance (AUC: 1.0, 100% sensitivity; 99%-100% specificity) for detection of cardiomyopathy at both LVEF categories. The AI-enabled digital stethoscope had an AUC of 0.98 (95% CI: 0.95, 1.00) and 0.97 (95% CI: 0.93, 1.00), for detection of LVEF <45% and <50%, respectively, with 100% sensitivity and 90% specificity. Conclusion: We demonstrate an AI-ECG and AI-enabled digital stethoscope were effective for detecting cardiac dysfunction in an obstetric population. Larger studies, including an evaluation of the impact of screening on clinical outcomes, are essential next steps.

Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study.

Aims: Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results: A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm's ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910-0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case-control ratio 1:2), the AUC was 0.921 (95% CI 0.909-0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion: The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Complications of Chemical Peels, Lasers, and Energy-Based Device Procedures Performed by Core Cosmetic Physicians: A Retrospective Analysis.

BACKGROUND: There has been a proliferation of physicians of different levels of experience and training offering nonsurgical cosmetic procedures. Rising demand, compounded by increasing utilization of new and existing technologies by numerous physician specialties, compels discussion of adequate standardized training and patient safety. METHODS: A retrospective chart review of patients who presented to our single site dermatology clinic for managment of complications following chemical peel, laser or energy-based device treatments performed by core cosmetic physicians between the years of 2013 and 2024 was conducted. Core cosmetic physicians included plastic surgery, facial surgery/otolaryngology, oculoplastic surgery, and dermatology. Charts were reviewed for documentation of the type of complication, procedure causing the complication, and physician credentials, and referral source. RESULTS: Twenty-five patients were identified as having complications from chemical peeling, laser treatment or energy-based devices. Devices implicated included CO2 laser (fractional or fully ablative), chemical peels, 1064 nm long-pulsed Nd:YAG laser, 1320 nm Nd:YAG laser, intense pulsed light, 595 nm pulsed dye laser, Q-switched Nd:YAG laser, radiofrequency with and without microneedling, and 1550 nm erbium-doped fiber laser. Complications included hypertrophic scarring, atrophic scarring, post-inflammatory erythema, post-inflammatory hyperpigmentation, and post-inflammatory hypopigmentation. CONCLUSIONS: Even in experienced hands, complications can arise. It is imperative that all physicians offering cosmetic treatments are equipped to recognize clinical endpoints, identify and manage complications, or make a timely referral to decrease the risk of a permanent and potentially devastating esthetic outcome for patients.

Efficacy of 785-nm Picosecond Titanium Sapphire Laser for Treatment of Brown Nevus of Ota Lesions in FST II-V: A Retrospective Analysis.

BACKGROUND: Nevus of Ota (NOTA) is a dermal melanocytosis acquired in early childhood or pregnancy. Given their color variability, NOTA often require a combination of wavelengths for successful treatment. Quality-switched lasers have consistently shown efficacy in targeting dermal pigment, while picosecond lasers (PSLs) are an emerging technology for pigmentary disorders. OBJECTIVE: To further elucidate its efficacy, the authors conducted a retrospective review of 17 patients with NOTA treated with a 785-nm PSL for brown NOTA lesions between 2021 and 2023. MATERIALS AND METHODS: The primary end point analyzed clinical improvement based on before and after photography reviewed by 3 board-certified dermatologists using a five-point visual analog scale. RESULTS: Seventeen patients of Fitzpatrick skin types (FSTs) II to V, ranging from ages 14 to 38 years, were included in this study. Patients were treated for an average of 3.2 sessions in 2 to 3-month intervals. Visual analog scale scores demonstrated a mean clearance of 51% to 75%. No pigmentary alterations were noted. CONCLUSION: Because NOTA is common in higher FSTs, the authors believe that the 785-nm PSL is an excellent treatment option for brown NOTA in these skin types. This study highlights the need for further investigation to determine optimal treatment parameters for the color-based laser treatment approach for NOTA.

Validation of Non-invasive Detection of Hyperkalemia by Artificial Intelligence Enhanced Electrocardiography in High Acuity Settings.

BACKGROUND: Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. METHODS: An emergency department (ED) cohort (February-August 2021) and a mixed intensive care unit (ICU) cohort (August 2017-February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within four hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads I and II of the 12 lead ECGs to screen for hyperkalemia (potassium > 6.0 mEq/L). RESULTS: The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-ECG to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of, 80%, 80%, 3%, 99.8% and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.83, 86%, 60%, 15%, 98% and 2.2, respectively, in the ED cohort. The ICU cohort (N=2,636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 82%, 82%, 14%, 99% and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.85, 88%, 67%, 29%, 97% and 2.7, respectively in the ICU cohort. CONCLUSION: The AI-ECG algorithm demonstrated a high negative predictive value, suggesting that it is useful for ruling out hyperkalemia, but a low positive predictive value, suggesting that it is insufficient for treating hyperkalemia.

Ten-Year Trend of Oral Anticoagulation Use in Postoperative and Nonpostoperative Atrial Fibrillation in Routine Clinical Practice.

BACKGROUND: The study aimed to describe the patterns and trends of initiation, discontinuation, and adherence of oral anticoagulation (OAC) in patients with new-onset postoperative atrial fibrillation (POAF), and compare with patients newly diagnosed with non-POAF. METHODS AND RESULTS: This retrospective cohort study identified patients newly diagnosed with atrial fibrillation or flutter between 2012 and 2021 using administrative claims data from OptumLabs Data Warehouse. The POAF cohort included 118 366 patients newly diagnosed with atrial fibrillation or flutter within 30 days after surgery. The non-POAF cohort included the remaining 315 832 patients who were newly diagnosed with atrial fibrillation or flutter but not within 30 days after a surgery. OAC initiation increased from 28.9% to 44.0% from 2012 to 2021 in POAF, and 37.8% to 59.9% in non-POAF; 12-month medication adherence increased from 47.0% to 61.8% in POAF, and 59.7% to 70.4% in non-POAF. The median time to OAC discontinuation was 177 days for POAF, and 242 days for non-POAF. Patients who saw a cardiologist within 90 days of the first atrial fibrillation or flutter diagnosis, regardless of POAF or non-POAF, were more likely to initiate OAC (odds ratio, 2.92 [95% CI, 2.87-2.98]; P <0.0001), adhere to OAC (odds ratio, 1.08 [95% CI, 1.04-1.13]; P <0.0001), and less likely to discontinue (odds ratio, 0.83 [95% CI, 0.82-0.85]; P <0.0001) than patients who saw a surgeon or other specialties. CONCLUSIONS: The use of and adherence to OAC were higher in non-POAF patients than in POAF patients, but they increased over time in both groups. Patients managed by cardiologists were more likely to use and adhere to OAC, regardless of POAF or non-POAF.

An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension.

BACKGROUND: Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG. METHODS: The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. RESULTS: Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. CONCLUSION: The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.

Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators.

BACKGROUND: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable. OBJECTIVE: The study aimed to apply machine learning (ML) to intracardiac electrograms (IEGMs) recorded by ICDs as a unique biomarker for predicting impending VAs. METHODS: The study included 13,516 patients who received Biotronik ICDs and enrolled in the CERTITUDE registry between January 1, 2010, and December 31, 2020. Database extraction included IEGMs from standard quarterly transmissions and VA event episodes. The processed IEGM data were pulled from device transmissions stored in a centralized Home Monitoring Service Center and reformatted into an analyzable format. Long-range (baseline or first scheduled remote recording), mid-range (scheduled remote recording every 90 days), or short-range predictions (IEGM within 5 seconds before the VA onset) were used to determine whether ML-processed IEGMs predicted impending VA events. Convolutional neural network classifiers using ResNet architecture were employed. RESULTS: Of 13,516 patients (male, 72%; age, 67.5 ± 11.9 years), 301,647 IEGM recordings were collected; 27,845 episodes of sustained ventricular tachycardia or ventricular fibrillation were observed in 4467 patients (33.0%). Neural networks based on convolutional neural networks using ResNet-like architectures on far-field IEGMs yielded an area under the curve of 0.83 with a 95% confidence interval of 0.79-0.87 in the short term, whereas the long-range and mid-range analyses had minimal predictive value for VA events. CONCLUSION: In this study, applying ML to ICD-acquired IEGMs predicted impending ventricular tachycardia or ventricular fibrillation events seconds before they occurred, whereas midterm to long-term predictions were not successful. This could have important implications for future device therapies.

Histological analysis of laser-enabled tissue coring with a novel 2910 nm erbium-doped fluoride glass fiber laser.

OBJECTIVES: There remains an unmet need for a laser-enabled tissue coring device that can effectively improve face and neck skin laxity and rhytides. We investigate a novel 2910 nm erbium-doped fluoride glass fiber laser (2910 nm fiber laser) (UltraClear; Acclaro Medical) for laser-coring of submental tissue. METHODS: Five subjects, Glogau scale III-IV, were treated with a single pulse of the laser-coring mode of the 2910 nm fiber laser in the submentum. A 4 mm punch biopsy was immediately performed. Biopsy specimens were sectioned and stained with hematoxylin and eosin and placed on glass slides. All sections were reviewed, and sections containing the center of the transected core were analyzed for depth and diameter of the ablative microchannel and width of the surrounding zone of coagulation. RESULTS: A total of 15 intact micro-cores were analyzed. Histological analysis revealed an average ± standard deviation microchannel diameter of 242.5 ± 65.2 µm, an average ablative depth of 980 ± 318.8 µm, and an average zone of coagulation of 104 ± 32 µm. CONCLUSIONS: Laser-enabled tissue coring with a novel 2910 nm fiber laser can safely achieve a wider microchannel diameter with ablative depth extending to the mid and deep dermis, which has the potential for collagen contraction and tissue tightening. Laser-coring to this ablation diameter and depth and with the surrounding zone of coagulation was found to be safe without adverse effects of post-inflammatory erythema or scarring in our study.

Atrial Fibrillation Substrate and Catheter Ablation Outcomes in MYBPC3- and MYH7-Mediated Hypertrophic Cardiomyopathy.

BACKGROUND: The effects of disease-causing MYBPC3 or MYH7 genetic variants on atrial myopathy, atrial fibrillation (AF) clinical course, and catheter ablation efficacy remain unclear. OBJECTIVES: The aim of this study was to characterize the atrial substrate of patients with MYBPC3- or MYH7-mediated hypertrophic cardiomyopathy (HCM) and its impact on catheter ablation outcomes. METHODS: A retrospective single-center study of patients with HCM who underwent genetic testing and catheter ablation for AF was performed. Patients with MYBPC3- or MYH7-mediated HCM formed the gene-positive cohort; those without disease-causative genetic variants formed the control cohort. High-density electroanatomical mapping was performed using a 3-dimensional mapping system, followed by radiofrequency ablation. RESULTS: Twelve patients were included in the gene-positive cohort (mean age 55.6 ± 9.9 years, 83% men, 50% MYBPC3, 50% MYH7, mean ejection fraction 59.3% ± 13.7%, mean left atrial [LA] volume index 51.7 ± 13.1 mL/m2, mean LA pressure 20.2 ± 5.4 mm Hg) and 15 patients in the control arm (mean age 61.5 ± 12.6 years, 60% men, mean ejection fraction 64.9% ± 5.1%, mean LA volume index 54.1 ± 12.8 mL/m2, mean LA pressure 19.6 ± 5.41 mm Hg). Electroanatomical mapping demonstrated normal voltage in 87.7% ± 5.03% of the LA in the gene-positive cohort and 94.3% ± 3.58% of the LA in the control cohort (P < 0.001). Of the abnormal regions, intermediate scar (0.1-0.5 mV) accounted for 6.33% ± 1.97% in the gene-positive cohort and 3.07% ± 2.46% in the control cohort (P < 0.01). Dense scar (<0.1 mV) accounted for 5.93% ± 3.20% in the gene-positive cohort and 2.61% ± 2.19% in the control cohort (P < 0.01). Freedom from AF at 12 months was similar between the gene-positive (75%) and control (73%) cohorts (P = 0.92), though a greater number of procedures were required in the gene-positive cohort. CONCLUSIONS: Patients with MYBPC3- or MYH7-mediated HCM undergoing AF ablation have appreciably more low-amplitude LA signals, suggestive of fibrosis. However, catheter ablation remains an effective rhythm-control strategy.

A Modular Communicative Leadless Pacing-Defibrillator System.

BACKGROUND: The subcutaneous implantable cardioverter-defibrillator (ICD) is associated with fewer lead-related complications than a transvenous ICD; however, the subcutaneous ICD cannot provide bradycardia and antitachycardia pacing. Whether a modular pacing-defibrillator system comprising a leadless pacemaker in wireless communication with a subcutaneous ICD to provide antitachycardia and bradycardia pacing is safe remains unknown. METHODS: We conducted a multinational, single-group study that enrolled patients at risk for sudden death from ventricular arrhythmias and followed them for 6 months after implantation of a modular pacemaker-defibrillator system. The safety end point was freedom from leadless pacemaker-related major complications, evaluated against a performance goal of 86%. The two primary performance end points were successful communication between the pacemaker and the ICD (performance goal, 88%) and a pacing threshold of up to 2.0 V at a 0.4-msec pulse width (performance goal, 80%). RESULTS: We enrolled 293 patients, 162 of whom were in the 6-month end-point cohort and 151 of whom completed the 6-month follow-up period. The mean age of the patients was 60 years, 16.7% were women, and the mean (±SD) left ventricular ejection fraction was 33.1±12.6%. The percentage of patients who were free from leadless pacemaker-related major complications was 97.5%, which exceeded the prespecified performance goal. Wireless-device communication was successful in 98.8% of communication tests, which exceeded the prespecified goal. Of 151 patients, 147 (97.4%) had pacing thresholds of 2.0 V or less, which exceeded the prespecified goal. The percentage of episodes of arrhythmia that were successfully terminated by antitachycardia pacing was 61.3%, and there were no episodes for which antitachycardia pacing was not delivered owing to communication failure. Of 162 patients, 8 died (4.9%); none of the deaths were deemed to be related to arrhythmias or the implantation procedure. CONCLUSIONS: The leadless pacemaker in wireless communication with a subcutaneous ICD exceeded performance goals for freedom from major complications related to the leadless pacemaker, for communication between the leadless pacemaker and subcutaneous ICD, and for the percentage of patients with a pacing threshold up to 2.0 V at a 0.4-msec pulse width at 6 months. (Funded by Boston Scientific; MODULAR ATP ClinicalTrials.gov NCT04798768.).

Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Aims: Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion: The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Artificial intelligence-based classification of echocardiographic views.

Aims: Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results: We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion: An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.