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Brugada Syndrome and Artificial Intelligence Applications to Diagnosis

NCT04641585 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 144

Last updated 2020-11-24

No results posted yet for this study

Summary

Aim of the project is the development of an integrated platform, based on machine learning and omic techniques, able to support physicians in as much as possible accurate diagnosis of Type 1 Brugada Syndrome (BrS).

Conditions

  • Brugada Syndrome 1

Interventions

DIAGNOSTIC_TEST

Patients affected by Brugada Syndrome 1

ECG analysis by Machine Learning algorithms and blood collection for the transcriptomic study of markers possibly associated with the disease

Sponsors & Collaborators

  • Fondazione Toscana Gabriele Monasterio

    collaborator OTHER

  • Azienda USL Toscana Sud Est

    collaborator OTHER_GOV

  • Azienda USL Toscana Nord Ovest

    collaborator OTHER

  • Azienda Ospedaliero-Universitaria Careggi

    collaborator OTHER

  • Azienda Ospedaliero, Universitaria Pisana

    collaborator OTHER

  • Istituto di Fisiologia Clinica CNR

    lead OTHER

Principal Investigators

  • Federico Vozzi, Ph.D. · Istituto di Fisiologia Clinica

Study Design

Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
14 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2021-01-15
Primary Completion
2023-03-15
Completion
2023-09-15

Countries

  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04641585 on ClinicalTrials.gov