Decision Support System Algorithm for COVID-19 Diagnosis

NCT04479319 | Completed

• 1 other identifier: 09081
• Study Type: observational
• Target: 3,215
• Locations: 1 country
• Sites: 2

Brief Summary

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.

Conditions

Covid19

Keywords

Deep learning; RT-PCR; Thorax CT

Outcome Measures

Primary Outcomes (1)

• Diagnosing COVID-19

  Determination of sensitivity and specificity in predicting COVID-19 diagnosis of hybrid decision support system

  Through study completion, an average of 1 year

Study Arms (3)

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

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

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

Interventions

Thorax CT DIAGNOSTIC_TEST

Subjects in all arms have a Thorax CT and RT-PCR for SARS-CoV-2.

Also known as: RT-PCR

COVID-19 Pneumonia; COVID-19, without Pneumonia; Non COVID-19

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• Adult patients with a differential diagnosis of COVID-19

You may not qualify if:

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

Sponsors & Collaborators

• Ankara University: lead
• Health Institutes of Turkey: collaborator

Study Sites (2)

Ankara University Faculty of Medicine

Ankara, Turkey (Türkiye)

Location

İhsan Doğramacı Bilkent Üniversitesi

Ankara, Turkey (Türkiye)

Location

Related Publications (8)

• Jan B, Farman H, Khan M, Imran M, Islam IU, Ahmad A, et al. Deep learning in big data analytics: a comparative study. Computers & Electrical Engineering. 2019;75:275-87

  BACKGROUND

• Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M, Henry TS, Kanne JP, Kligerman S, Ko JP, Litt H. Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA - Secondary Publication. J Thorac Imaging. 2020 Jul;35(4):219-227. doi: 10.1097/RTI.0000000000000524.

  PMID: 32324653 BACKGROUND

• Santosh KC. AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. J Med Syst. 2020 Mar 18;44(5):93. doi: 10.1007/s10916-020-01562-1.

  PMID: 32189081 BACKGROUND

• Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020 Jul-Aug;14(4):337-339. doi: 10.1016/j.dsx.2020.04.012. Epub 2020 Apr 14.

  PMID: 32305024 BACKGROUND

• Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K, Liu D, Wang G, Xu Q, Fang X, Zhang S, Xia J, Xia J. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology. 2020 Aug;296(2):E65-E71. doi: 10.1148/radiol.2020200905. Epub 2020 Mar 19.

  PMID: 32191588 BACKGROUND

• Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018 Sep;11045:3-11. doi: 10.1007/978-3-030-00889-5_1. Epub 2018 Sep 20.

  PMID: 32613207 BACKGROUND

• Li D, Wang D, Dong J, Wang N, Huang H, Xu H, Xia C. False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases. Korean J Radiol. 2020 Apr;21(4):505-508. doi: 10.3348/kjr.2020.0146. Epub 2020 Mar 5.

  PMID: 32174053 BACKGROUND

• Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, Li C. The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia. Invest Radiol. 2020 Jun;55(6):327-331. doi: 10.1097/RLI.0000000000000672.

  PMID: 32118615 BACKGROUND

MeSH Terms

Conditions

COVID-19

Interventions

COVID-19 Nucleic Acid Testing

Condition Hierarchy (Ancestors)

Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases

Intervention Hierarchy (Ancestors)

COVID-19 Testing; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Investigative Techniques

Study Officials

• Özlem Özdemir Kumbasar, Prof Dr

  Ankara University

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof Dr

Study Record Dates

• First Submitted: July 16, 2020
• First Posted: July 21, 2020
• Study Start: December 31, 2020
• Primary Completion: November 1, 2021
• Study Completion: April 1, 2022
• Last Updated: May 17, 2022

Record last verified: 2022-05

Locations

TU (2)