Decision Support System Algorithm for COVID-19 Diagnosis

May 15, 2022 updated by: Dr.Ozlem Ozdemir Kumbasar, Ankara University

Developing Hybrid Decision Support System Algorithm for COVID-19 Diagnosis Between RT-PCR Graphics and Thorax CT Images Using Deep Learning

COVID-19 is an infectious disease caused by a newly discovered Coronavirus which was first identified in Wuhan, China in December 2019. Then the novel coronavirus outbreak was described and announced as a pandemic by World Health Organization (WHO) on March 11, 2020.

Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard test for diagnosis of COVID-19. Nevertheless, due to its high false-negative rates (%10-50), diagnosis and treatment decisions do not depend on RT-PCR alone. Clinical presentation of patient and radiological findings are also important. However, neither clinical presentation nor computed tomography (CT) findings are specific for COVID-19. As a consequence of these challenges, the diagnosis of the disease and the protection of the community health become more difficult. The investigators of this study hypothesized that deep learning-based decision support system may help for definitive diagnosis of COVID-19. The aim is to develop a deep learning-based decision support system algorithm based on clinical presentation of patient, laboratory and CT findings and RT-PCR data. Previously, deep learning algorithms with the use of widely known deep neural network architectures such as Inception, UNet, ResNet were developed. However all of these studies were based on CT findings. There are not any deep learning study in literature combining the clinical, radiological, and laboratory findings of patients.

The project is based on the available data of COVID-19 patients that will be obtained from the Ministry of Health. Then the data will be evaluated for relevance and reliability and labeled for the training of machine. Following the anonymization of data, data will be processed according to the predetermined inclusion-exclusion criteria. Thorax CT data will be labeled as typical / indeterminate / atypical / negative for COVID-19 pneumonia. Also, CT images of patients with known non-COVID-19 diseases will be labeled for the training of machine. Then, fever, lymphocyte count, neutrophil to lymphocyte ratio, contact information, RT-PCR findings will be labeled. Subsequently, the patients will be labeled and the machine will be trained with deep learning method with the help of this grouped and labeled data. Following the training phase, the algorithm will be tested and if the machine reaches the target specificity and sensitivity, the prototype will be tested. And then, the prototype will be embedded into the hospital software system. This software and algorithm will serve as an early warning system for clinicians and provide a better diagnostic rate especially with decreasing false-negative results. The effects of a pandemic cannot be measured by only the number of people diagnosed and isolated, or treatment provided. A pandemic affects not only community health but also individuals' psychological status, education, teaching methods, working models, daily lifestyles, producer/consumer behaviors, supply/demand balance; in other words every single area of life. On top of that, a pandemic causes long-term damages hard to reverse. The software will increase the diagnostic success rates, help to control the pandemic and minimize the collateral damages mentioned above. The investigators believe that, the product that will be produced at the end of this project will be of great benefit in controlling the secondary wave of COVID-19 expected to occur.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

3215

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Turkey
      • Ankara, Turkey
        • Ankara University Faculty of Medicine
      • Ankara, Turkey
        • İhsan Doğramacı Bilkent Üniversitesi

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

Adult patients with a differential diagnosis of COVID-19 and tested for it with Thorax CT and RT-PCR in Turkey.

Description

Inclusion Criteria:

  • Adult patients with a differential diagnosis of COVID-19

Exclusion Criteria:

  • Patients who are under 18 year-old
  • Patients who have not either Thorax CT or SARS-CoV-2 RT-PCR

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
COVID-19 Pneumonia

COVID-19 patients who have pneumonia on thorax CT

either Thorax CT + SARS-CoV-2 RT-PCR + Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +/-

or Thorax CT + SARS-CoV-2 RT-PCR - Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +
Diagnostic test: Thorax CT
Subjects in all arms have a Thorax CT and RT-PCR for SARS-CoV-2.
Other Names:
  • RT-PCR
COVID-19, without Pneumonia

COVID-19 patients who have not pneumonia on thorax CT

Thorax CT - SARS-CoV-2 RT-PCR + Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +/-
Diagnostic test: Thorax CT
Subjects in all arms have a Thorax CT and RT-PCR for SARS-CoV-2.
Other Names:
  • RT-PCR
Non COVID-19

Patients with viral infection symptoms who is not diagnosed with COVID-19

either Thorax CT - SARS-CoV-2 RT-PCR - Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 +/-

or Thorax CT + SARS-CoV-2 RT-PCR - Clinical signs of COVID-19 +/- Any contact with someone with COVID-19 -
Diagnostic test: Thorax CT
Subjects in all arms have a Thorax CT and RT-PCR for SARS-CoV-2.
Other Names:
  • RT-PCR

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnosing COVID-19
Time Frame: Through study completion, an average of 1 year
Determination of sensitivity and specificity in predicting COVID-19 diagnosis of hybrid decision support system
Through study completion, an average of 1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Ankara University

Collaborators

Presidency of Health Institute Turkey (TUSEB)

Investigators

  • Study Chair: Özlem Özdemir Kumbasar, Prof Dr, Ankara University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 31, 2020

Primary Completion (Actual)

November 1, 2021

Study Completion (Actual)

April 1, 2022

Study Registration Dates

First Submitted

July 16, 2020

First Submitted That Met QC Criteria

July 19, 2020

First Posted (Actual)

July 21, 2020

Study Record Updates

Last Update Posted (Actual)

May 17, 2022

Last Update Submitted That Met QC Criteria

May 15, 2022

Last Verified

May 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 09081

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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