Diagnostic Accuracy of a Deep Learning-Based Software for Automated Multiparametric Echocardiographic Measurements

PANECHO: Diagnostic Accuracy of a Deep Learning-Based Artificial Intelligence Software Developed for Automated Multiparametric Echocardiographic Measurements From Echocardiographic Video Images: A Multicenter Study of the Italian Society of Echocardiography and Cardiovascular Imaging (SIECVI)

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07738419

Acronym

PANECHO

Enrollment

1157

Registered

2026-07-31

Start date

2026-04-27

Completion date

2027-01-31

Last updated

2026-07-31

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence, Diagnostic Imaging, Echocardiography

Brief summary

Transthoracic echocardiography is an essential imaging modality for the diagnosis and follow-up of cardiovascular diseases. Comprehensive echocardiographic assessment requires multiple quantitative measurements of cardiac structure and function, which are time-consuming and highly dependent on operator expertise. US2.AI (Us2.v1) is an artificial intelligence (deep learning)-based software designed to automatically analyze standard two-dimensional and Doppler echocardiographic DICOM video clips acquired from different ultrasound vendors. The software provides automated measurements of cardiac morphology and function, including chamber dimensions and volumes, left and right ventricular systolic and diastolic function, myocardial strain, and Doppler-derived parameters, generating a comprehensive echocardiographic report based on current international guideline recommendations. In addition, the software may assist in identifying echocardiographic features suggestive of several cardiovascular conditions, including heart failure, pulmonary hypertension, hypertrophic cardiomyopathy, cardiac amyloidosis, valvular heart disease, and ischemic cardiomyopathy.

Detailed description

This is a non profit, prospective, multicenter observational study aimed at evaluating the diagnostic accuracy of the US2.AI software by comparing its automated echocardiographic measurements with measurements performed by experienced echocardiographers, considered the reference standard. The study will assess the agreement between automated and expert-derived measurements and determine the reliability of the software in routine clinical practice. Demonstrating high diagnostic accuracy may support the use of artificial intelligence to standardize echocardiographic measurements and facilitate comprehensive image analysis, particularly in settings where advanced analysis tools or highly experienced operators are not readily available.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

* Adults aged 18 years or older. * Undergoing clinically indicated standard transthoracic echocardiography. * Adequate echocardiographic image quality for automated and expert analysis. * Written informed consent provided prior to study participation.

Exclusion criteria

* Age \<18 years. * Frequent and/or complex cardiac arrhythmias during echocardiographic examination. * Suboptimal echocardiographic images.

Design outcomes

Primary

Measure	Time frame	Description
Agreement between AI-derived and expert-derived echocardiographic measurements across predefined patient subgroups	January 2027	Comparison of the agreement between automated and expert-derived measurements in predefined subgroups, including participants with normal echocardiographic findings and those with specific cardiovascular diseases.
Agreement between AI-derived and expert-derived echocardiographic measurements	Jan 2027	Agreement between automated echocardiographic measurements generated by the US2.AI software and manual measurements performed by experienced echocardiographers (reference standard) across standard two-dimensional, Doppler, and strain parameters.
Time required for echocardiographic analysis	January 2027	Comparison of the time required to obtain a complete set of echocardiographic measurements using manual analysis by experienced echocardiographers versus automated analysis by the US2.AI software.

Secondary

Measure	Time frame	Description
Agreement between AI-assisted and expert final echocardiographic diagnoses	January 2027	Agreement between the final echocardiographic diagnosis suggested by the US2.AI software and the final diagnosis reported by the expert echocardiographer.

Countries

Italy

Contacts

CONTACTLaura Fusini, MD

laura.fusini@cardiologicomonzino.it0258002909

Outcome results