Covid19NCT04479319
Decision Support System Algorithm for COVID-19 Diagnosis
An observational study of Covid19, run by Ankara University at 2 sites in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Covid19
- What is being tested?
- Thorax CT
- How many people
- 3,215 participants
- Where
- 2 sitesTurkey (Türkiye)
- Study dates
- 31 Dec 2020to 1 Apr 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients with a differential diagnosis of COVID-19 and tested for it with Thorax CT and RT-PCR in Turkey.
Who may be able to join
- Adult patients with a differential diagnosis of COVID-19
Who cannot join
- Patients who are under 18 year-old
- Patients who have not either Thorax CT or SARS-CoV-2 RT-PCR
Trial locations and contacts
2 sites in 1 country.
Turkey (Türkiye)2 sites
Ankara University Faculty of Medicine
Ankara
İhsan Doğramacı Bilkent Üniversitesi
Ankara
About this study
Official title: 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.
- Conditions
- Covid19
- Being tested
- Thorax CT
- Keywords
- Deep learning, RT-PCR, Thorax CT
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Thorax CTDiagnostic test
Also called RT-PCR
Subjects in all arms have a Thorax CT and RT-PCR for SARS-CoV-2.
| Study group | Role | What they receive |
|---|---|---|
| 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 + Receives: 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 +/- Receives: 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 - Receives: Diagnostic Test: Thorax CT |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnosing COVID-19
Determination of sensitivity and specificity in predicting COVID-19 diagnosis of hybrid decision support system
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Jul 2020
- Study start31 Dec 2020
- Primary completion1 Nov 2021
- Study completion1 Apr 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Covid19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 3,215 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ankara University
- Sponsor type
- University, hospital or other
- Collaborators
- Health Institutes of Turkey
- Responsible party
- Principal investigator, Dr.Ozlem Ozdemir Kumbasar, Ankara University
- Investigators
- Özlem Özdemir Kumbasar, Prof Dr, Study chair, Ankara University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04479319
- Sponsor's protocol ID
- 09081
- Status last verified
- May 2022
- First submitted
- 16 Jul 2020
- Last updated
- 17 May 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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
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.
PubMed 32324653Santosh 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.
PubMed 32189081Vaishya 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.
PubMed 32305024Li 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.
PubMed 32191588Zhou 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.
PubMed 32613207Li 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.
PubMed 32174053Li 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.
PubMed 32118615
Study at a glance
From the registry record, last updated by the sponsor on 17 May 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Developing Hybrid Decision Support System Algorithm for COVID-19 Diagnosis Between RT-PCR Graphics and Thorax CT Images Using Deep Learning
- ClinicalTrials.gov ID
- NCT04479319
- Acronym / sponsor ID
- 09081
- Conditions
- Covid19
- Keywords
- Deep learning, RT-PCR, Thorax CT
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 3,215 participants (actual)
- Lead sponsor
- Ankara UniversityUniversity, hospital or other
- Collaborators
- Health Institutes of Turkey
- Study start
- 31 Dec 2020 (actual)
- Primary completion
- 1 Nov 2021 (actual)
- Study completion
- 1 Apr 2022 (actual)
- First posted
- 21 Jul 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| COVID-19 Pneumonia | – | Diagnostic Test: Thorax CTCOVID-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 + |
| COVID-19, without Pneumonia | – | Diagnostic Test: Thorax CTCOVID-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 +/- |
| Non COVID-19 | – | Diagnostic Test: Thorax CTPatients 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 - |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients with a differential diagnosis of COVID-19 and tested for it with Thorax CT and RT-PCR in Turkey.
Key inclusion criteria (1 listed)
- Adult patients with a differential diagnosis of COVID-19
Key exclusion criteria (2 listed)
- Patients who are under 18 year-old
- Patients who have not either Thorax CT or SARS-CoV-2 RT-PCR
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Thorax CTAlso: RT-PCRSubjects in all arms have a Thorax CT and RT-PCR for SARS-CoV-2. | Diagnostic test | COVID-19 Pneumonia, COVID-19, without Pneumonia, Non COVID-19 |
Primary outcome
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 May 2022.
- BackgroundJan 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: PubMed 32324653Simpson 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.
- Background: PubMed 32189081Santosh 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.
- Background: PubMed 32305024Vaishya 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.
- Background: PubMed 32191588Li 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.
- Background: PubMed 32613207Zhou 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.
- Background: PubMed 32174053Li 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.
- Background: PubMed 32118615Li 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Özlem Özdemir Kumbasar, Prof Dr study chair
Ankara University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Covid19, run by Ankara University at 2 sites in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 May 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnosing COVID-19 (time frame: Through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04479319, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 May 2022.