Pediatric Ventricle Function Assessment Study

NCT06739057 | Completed

• 1 other identifier: ECHO_1
• Study Type: observational
• Target: 3,993
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to develop a deep learning-based framework for right ventricular (RV) segmentation, prediction of RV fractional area change (FAC), and identification of pediatric RV dysfunction. The AI model was designed to distinguish between normal pediatric hearts, pulmonary hypertension (PH), and Tetralogy of Fallot (TOF). To improve diagnostic accuracy, the investigators extended the analysis beyond the A4C view by integrating data from both A4C and PSAX views. Additionally, the framework was applied to predict left ventricular ejection fraction (LV EF), further showcasing its versatility and clinical utility.

Conditions

RV - Right Ventricular Abnormality; LV Dysfunction; Pulmonary Hypertension; TOF

Keywords

Echocardiogram; Right Ventricle (RV); Pulmonary Hypertension; TOF

Outcome Measures

Primary Outcomes (3)

• RV dysfunction prediction

  FAC \< 35% is considered RV abnormal. The automated extracted FAC values were then used to predict the probability of abnormal RV function.FAC estimation accuracy was evaluated using mean absolute error (MAE), root mean squared error (RMSE), and Pearson correlation coefficient (R). Diagnostic performance metrics included AUC, sensitivity, and specificity for RV function assessment

  10 minutes

• Congenital Heart Disease Detection

  For CHD detection, classification models were developed to distinguish between normal, PH, and TOF cases by analyzing RV echocardiogram videos. Accuracy, sensitivity, and specificity were used to evaluate the classifications of PH versus normal and PH versus TOF

  10 minutes

• Left Ventricle Dysfunction

  The performance is assessed by calculating the area under the receiver operating characteristic (ROC) curve , evaluating the model's accuracy in detecting abnormal EF values.

  10 minutes

Study Arms (4)

RV FAC Prediction Cohort

This cohort is designed to predict pediatric RV FAC using a deep learning model based on echocardiograms

RV Disease Classification Cohort

The cohort is designed to employ a deep learning model to differentiate between normal pediatric hearts and pulmonary hypertension (PH), as well as between Tetralogy of Fallot (TOF) and PH, using echocardiograms.

RV Function Assessment and Disease Classification Using A4C and PSAX View Cohorts

The cohort is designed to utilize A4C and PSAX echocardiographic views for pediatric RV function assessment and disease classification using deep learning models.

LV Ejection Fraction (EF) Prediction Cohort

The cohort is designed to validate our new deep learning model for LV EF assessment.

Eligibility Criteria

• Age: Up to 18 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Two groups of children are included. Group I: Children with normal right ventricular anatomy and function assessment based on screening echocardiograms and physcian follow up. Group II: Children with right ventricular abnormalities who have potential risk of adverse outcomes.

You may qualify if:

• Patients aged 0-18 years old.
• Patients with the following diagnoses (defined as abnormal RVs): premature infants with lung disease, congenital heart disease with systemic right ventricles, surgically repaired congenital heart disease resulting in pressure and/or volume load on the RV (tetralogy of Fallot, Double outlet right ventricle, etc.), and idiopathic pulmonary hypertension.

Sponsors & Collaborators

• HBI Solutions Inc.: lead

Study Sites (1)

Stanford University

Palo Alto, California, 94304, United States

Location

MeSH Terms

Conditions

Ventricular Dysfunction, Left; Hypertension, Pulmonary

Condition Hierarchy (Ancestors)

Ventricular Dysfunction; Heart Diseases; Cardiovascular Diseases; Lung Diseases; Respiratory Tract Diseases; Hypertension; Vascular Diseases

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Cardiothoracic Surgery (Pediatric Cardiac Surgery) and of Pediatrics (Cardiology), Stanford University School of Medicine

Study Record Dates

• First Submitted: December 12, 2024
• First Posted: December 18, 2024
• Study Start: January 1, 2014
• Primary Completion: August 31, 2024
• Study Completion: August 31, 2024
• Last Updated: December 20, 2024

Record last verified: 2024-12

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)