CompletedNot Applicable

Pediatric Ventricle Function Assessment Study

United States3,993 participantsStarted 2014-01-01

Plain-language 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.

Who can participate

Age range18 Years

SexALL

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Inclusion criteria

✓. 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.

What they're measuring

RV dysfunction prediction

Timeframe: 10 minutes

Congenital Heart Disease Detection

Timeframe: 10 minutes

Left Ventricle Dysfunction

Timeframe: 10 minutes

Trial details

NCT IDNCT06739057

SponsorHBI Solutions Inc.

Sponsor typeINDUSTRY

Study typeOBSERVATIONAL

Primary completion2024-08-31

View on ClinicalTrials.gov More RV - Right Ventricular Abnormality trials