Covid Radiographic Images Data-set for A.I

NCT04419545 | Unknown DMC

• 1 other identifier: CORDA
• Study Type: observational
• Target: 2,500
• Locations: 1 country
• Sites: 1

Brief Summary

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the Artificial intelligence community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non- COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

Conditions

Radiology; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• sensibility and specificity of neural network diagnosis

  sensibility and specificity of neural network diagnosis compared with human diagnosis

  at day 0

Study Arms (2)

interstitial pneumonia cases

Chest x-ray diagnosis

Diagnostic Test: Neural network diagnosis algorithm

Negative controls

Chest x-ray Negative for pneumonia

Diagnostic Test: Neural network diagnosis algorithm

Interventions

Neural network diagnosis algorithm DIAGNOSTIC_TEST

we feed neural network with chest x-ray radiography images for teaching the network for automatic diagnosis of interstitial pneumonia

Negative controls; interstitial pneumonia cases

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All the people treated in radiology dept

You may not qualify if:

• None

Sponsors & Collaborators

• A.O.U. Città della Salute e della Scienza: lead
• University of Turin, Italy: collaborator
• Azienda Ospedaliera Ordine Mauriziano di Torino: collaborator

Study Sites (1)

Azienda Ospedaliero Universitaria Città della Salute e della Scienza

Torino, Turin, 10126, Italy

RECRUITING

Study Officials

• Giorgio Limerutti, M.D.

  Radiology Unit A.O.U. Città della Salute e della Scienza

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Marco Grosso, M.Sc.

CONTACT

00390116331330; mgrosso2@gmail.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: M.D.

Study Record Dates

• First Submitted: June 4, 2020
• First Posted: June 5, 2020
• Study Start: March 24, 2020
• Primary Completion: December 31, 2020
• Study Completion: March 31, 2021
• Last Updated: June 5, 2020

Record last verified: 2020-06

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)