Decision Support System Algorithm for COVID-19 Diagnosis
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Study Design
Arms and Interventions
| Arm | 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:
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| 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:
|
| 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:
|
Outcome Measures
Primary Outcome Measures
- Diagnosing COVID-19 [Through study completion, an average of 1 year]
Determination of sensitivity and specificity in predicting COVID-19 diagnosis of hybrid decision support system
Eligibility Criteria
Criteria
Inclusion Criteria:
- Adult patients with a differential diagnosis of COVID-19
Exclusion Criteria:
-
Patients who are under 18 year-old
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Patients who have not either Thorax CT or SARS-CoV-2 RT-PCR
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Ankara University Faculty of Medicine | Ankara | Turkey | ||
| 2 | İhsan Doğramacı Bilkent Üniversitesi | Ankara | Turkey |
Sponsors and Collaborators
- Ankara University
- Presidency of Health Institute Turkey (TUSEB)
Investigators
- Study Chair: Özlem Özdemir Kumbasar, Prof Dr, Ankara University
Study Documents (Full-Text)
None provided.More Information
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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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. Review.
- 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.
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