The reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for diagnose COVID‑19 among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model’s performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis.
The severe acute respiratory syndrome coronavirus 2 (SARS-coV-2) outbreak started in December 2019 in the Hubei province, China. The associated disease, coronavirus disease 2019 (COVID-19)1, has now spread worldwide. The World Health Organization currently reports more than 10 million confirmed cases and 500,000 deaths. Increased mortality rates and the collapse of healthcare systems have been reported in several regions2,3,4. Indeed, due to SARS-coV-2 contagiousness, promiscuity within health systems can promote patient-to-patient transmission5,6 and the contamination of healthcare workers7, rapidly leading to the saturation of health systems8. To limit this effect, patients with COVID-19 infection are hospitalized in specific units after being emergency department (ED) triage9. Therefore, it is essential to have a reliable and easy-to-use tool for diagnose COVID‑19. SARS-coV-2 real-time RT-PCR reverse transcription–polymerase chain reaction (RT-PCR) is the accepted standard for diagnose COVID-1910. However, RT-PCR performances are sub-optimal and, like for any other test, there are false negatives results11,12.
Therefore, additional investigations should be performed in patients with negative RT-PCR results but high clinical probability of COVID-19. In this context, chest-CT is an interesting tool because it allows detecting virus-induced lung tissue damages and alternative diagnoses13. Thus, when a patient presents a high clinical probability of COVID-19, a negative RT-PCR and a chest-CT showing typical COVID-19 lesions with no sign of alternative diagnosis, it is possible to consider that the patient has COVID-19 with a false negative RT-PCR result. The use of chest-CT alone cannot be recommended, but its combined use with clinic and RT-PCR allows to resolve diagnostic ambiguities14. However, RT-PCR and chest-CT cannot be performed in all patients suspected to have COVID-19 for many reasons, including reagent shortage15, device unavailability, lack of human resources, and high costs. Moreover, the time required to perform both tests increase the risk of ED overcrowding by patients waiting for their results. Therefore, health professionals must adapt their diagnostic strategies in function of their resources16. To our knowledge, the potential contribution of machine learning using imaging, clinical and laboratory data has been poorly evaluated in this context. Machine learning is an inherited artificial intelligence approach that enables computers to extract or classify patterns. It allows predicting whether a patient belongs to a predefined group using explanatory variables. The recent increase in machine learning models in the healthcare field suggests that these methods could improve the diagnose COVID‑19 strategy17. The objective of this study was to develop and evaluate machine learning models using clinical-biological data from health records to improve the RT-PCR and chest-CT performances to diagnose COVID-19 among post-emergency hospitalized patients.
Our study demonstrates that machine learning models can be developed for improving COVID-19 diagnosis in patients hospitalized through the ED. Models based on chest-CT or RT-PCR will increase the performance of these tests by using clinico-biological variables. After generalization, machine learning should play a key role in the management of the outbreak by improving the performances of chest-CT and RT-PCR for diagnose COVID‑19.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
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FULL Paper PDF file:Machine learning is the key to diagnose COVID‑19: a proof‑of‑concept study
Nature Portfolio, Scientific Reports
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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.