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NCT Number: NCT07738419

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

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.

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Key information

Conditions

Echocardiography Artificial Intelligence Diagnostic Imaging

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Centro Cardiologico Monzino, IRCCS

Milan, Lombardy, 20132, Italy

Location status: Recruiting

Location contact

Chiara Centenaro

CONTACT

[email protected]

0258002031 ext. +39

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. Agreement between AI-derived and expert-derived echocardiographic measurements across predefined patient subgroups

    Time frame: 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.

  2. Agreement between AI-derived and expert-derived echocardiographic measurements

    Time frame: 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.

  3. Time required for echocardiographic analysis

    Time frame: 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 outcomes

  1. Agreement between AI-assisted and expert final echocardiographic diagnoses

    Time frame: January 2027

    Agreement between the final echocardiographic diagnosis suggested by the US2.AI software and the final diagnosis reported by the expert echocardiographer.

Study contacts

Contact information is provided by the study sponsor or research team.

Laura Fusini, MD

CONTACT

[email protected]

0258002909 ext. +39

Sponsors and collaborators

Lead sponsor

Centro Cardiologico Monzino

Other

Collaborators

  • ASST Grande Ospedale Metropolitano Niguarda
  • Azienda Ospedaliero Universitaria Policlinico Modena
  • Monaldi Hospital, Napoli, Italy
  • Ospedale San Giovanni Evangelista Tivoli
  • Policlinico G . Martino, Messina Italy
  • Universita di Verona

Registry information

Official study title

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)

Acronym: PANECHO

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Jul 31, 2026
Registry last updated
Jul 31, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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