An Automated View Classification Model for Pediatric Echocardiography Using Artificial Intelligence

Gearhart, Addison; Goto, Shinichi; Deo, Rahul C.; Powell, A.

doi:10.1016/j.echo.2022.08.009

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…It has been demonstrated that machine learning algorithms can be trained to identify standard TTE views from labeled datasets. 18,19,24 Subsequent studies were able to take advantage of the standard clinical workflow for transthoracic imaging, which incorporates anatomic tracings and quantitative measurements, in order to streamline segmentation and classification tasks. 4,7 It has also been shown that machine learning algorithms trained on TTE videos are able to recognize cardiac structures, approximate cardiac function, make accurate diagnoses, identify phenotypic information that is otherwise not easily recognized by a human observer, and predict clinical outcomes.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Transesophageal Echocardiography View Classification

Steffner

Christensen

Gill

et al. 2023

Preprint

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Transesophageal echocardiography (TEE) imaging is a vital monitoring and diagnostic tool used during all major cardiac surgeries, guiding perioperative diagnoses, surgical decision-making, and hemodynamic evaluation in real-time. A key limitation to the automated evaluation of TEE data is the complexity and unstructured nature of the images, which demonstrate significant heterogeneity across varied views in the evaluation of different cardiac structures. In this study, we describe the first machine learning model for TEE view classification. We trained a convolutional neural network (CNN) to predict standardized TEE views using labeled intraoperative and intraprocedural TEE videos from Cedars-Sinai Medical Center (CSMC). We externally validated our model on intraoperative TEE videos from Stanford University Medical Center (SUMC). Accuracy of our model was high across all labeled views. The highest performance was achieved for the Trans-Gastric Left Ventricular Short Axis View (area under the receiver operating curve [AUC] = 0.971 at CSMC, 0.957 at SUMC), the Mid-Esophageal Long Axis View (AUC = 0.954 at CSMC, 0.905 at SUMC), the Mid-Esophageal Aortic Valve Short Axis View (AUC = 0.946 at CSMC, 0.898 at SUMC), and the Mid-Esophageal 4-Chamber View (AUC = 0.939 at CSMC, 0.902 at SUMC). Ultimately, we demonstrate that our unique deep learning model can accurately classify standardized TEE views, which will facilitate further downstream analyses for intraoperative TEE imaging.

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Transesophageal Echocardiography View Classification

Steffner

Christensen

Gill

et al. 2023

Preprint

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“…34,35 To date, AI has been used in pediatric cardiology primarily for image-based deep learning applications. [36][37][38][39] Analysis of ECG waveforms provides a rapid, easy-to-implement, and cost-effective application for artificial intelligence. Its use in adults has been wideranging, including prediction of ventricular dysfunction, [3][4][5][6][7] ventricular hypertrophy, 8-10 ventricular dilation, 9,11 atrial fibrillation and other arrhythmias, 17,26,40,41 and age, 42,43 sex, 42 and time to death.…”

Section: Clinical Significance and Implicationsmentioning

confidence: 99%

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

Mayourian,

La Cava,

Vaid

et al. 2024

Circulation

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BACKGROUND: Artificial intelligence–enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored. METHODS: A convolutional neural network was trained on paired ECG–echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert–classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG–echocardiogram pairs per patient at Boston Children’s Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). RESULTS: The training cohort comprised 92 377 ECG–echocardiogram pairs (46 261 patients; median age, 8.2 years). Test groups included internal testing (12 631 patients; median age, 8.8 years; 4.6% composite outcomes), emergency department (2830 patients; median age, 7.7 years; 10.0% composite outcomes), and external validation (5088 patients; median age, 4.3 years; 6.1% composite outcomes) cohorts. Model performance was similar on internal test and emergency department cohorts, with model predictions of LV hypertrophy outperforming the pediatric cardiologist expert benchmark. Adding age and sex to the model added no benefit to model performance. When using quantitative outcome cutoffs, model performance was similar between internal testing (composite outcome: AUROC, 0.88, AUPRC, 0.43; LV dysfunction: AUROC, 0.92, AUPRC, 0.23; LV hypertrophy: AUROC, 0.88, AUPRC, 0.28; LV dilation: AUROC, 0.91, AUPRC, 0.47) and external validation (composite outcome: AUROC, 0.86, AUPRC, 0.39; LV dysfunction: AUROC, 0.94, AUPRC, 0.32; LV hypertrophy: AUROC, 0.84, AUPRC, 0.25; LV dilation: AUROC, 0.87, AUPRC, 0.33), with composite outcome negative predictive values of 99.0% and 99.2%, respectively. Saliency mapping highlighted ECG components that influenced model predictions (precordial QRS complexes for all outcomes; T waves for LV dysfunction). High-risk ECG features include lateral T-wave inversion (LV dysfunction), deep S waves in V1 and V2 and tall R waves in V5 and V6 (LV hypertrophy), and tall R waves in V4 through V6 (LV dilation). CONCLUSIONS: This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children, which may facilitate improved access to care by democratizing the expertise of pediatric cardiologists.

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“…View classification represents another vital step towards building a fully automated echocardiogram interpretation system. Currently, there are additional challenges in creating a view classification model for pediatric echocardiograms compared to adult echocardiograms: multiple variations in anatomy, size, structure, and views ( 23 ).…”

Section: Current Challenges In Implementing Aimentioning

confidence: 99%

Role of artificial intelligence in early detection of congenital heart diseases in neonates

Ejaz,

Thyyib,

Ibrahim

et al. 2024

Front. Digit. Health

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In the domain of healthcare, most importantly pediatric healthcare, the role of artificial intelligence (AI) has significantly impacted the medical field. Congenital heart diseases represent a group of heart diseases that are known to be some of the most critical cardiac conditions present at birth. These heart diseases need a swift diagnosis as well as an intervention to ensure the wellbeing of newborns. Fortunately, with the help of AI, including the highly advanced algorithms, analytics and imaging involved, it provides us with a promising era for neonatal care. This article reviewed published data in PubMed, Science Direct, UpToDate, and Google Scholar between the years 2015–2023. To conclude The use of artificial intelligence in detecting congenital heart diseases has shown great promise in improving the accuracy and efficiency of diagnosis. Several studies have demonstrated the efficacy of AI-based approaches for diagnosing congenital heart diseases, with results indicating that the systems can achieve high levels of sensitivity and specificity. In addition, AI can help reduce the workload of healthcare professionals allowing them to focus on other critical aspects of patient care. Despite the potential benefits of using AI, in addition to detecting congenital heart disease, there are still some challenges to overcome, such as the need for large amounts of high-quality data and the requirement for careful validation of the algorithms. Nevertheless, with ongoing research and development, AI is likely to become an increasingly valuable tool for improving the diagnosis and treatment of congenital heart diseases.

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An Automated View Classification Model for Pediatric Echocardiography Using Artificial Intelligence

Cited by 9 publications

References 23 publications

Deep Learning for Transesophageal Echocardiography View Classification

Deep Learning for Transesophageal Echocardiography View Classification

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

Role of artificial intelligence in early detection of congenital heart diseases in neonates

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