Data_Sheet_1_Artificial Intelligence for COVID-19: A Systematic Review.DOCX
Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations.
Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555).
Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines.
Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.
History
References
- https://doi.org//10.1038/s41586-020-2012-7
- https://doi.org//10.1111/jebm.12376
- https://doi.org//10.1111/jebm.12388
- https://doi.org//10.1001/jama.2020.12839
- https://doi.org//10.1016/S0140-6736(20)30183-5
- https://doi.org//10.1001/jama.2020.1585
- https://doi.org//10.1080/14787210.2020.1744275
- https://doi.org//10.7861/futurehosp.6-2-94
- https://doi.org//10.2196/10010
- https://doi.org//10.1001/jama.2019.16842
- https://doi.org//10.1001/jamadermatol.2018.2348
- https://doi.org//10.2196/20259
- https://doi.org//10.1371/journal.pone.0250952
- https://doi.org//10.1148/radiol.2020201491
- https://doi.org//10.1016/j.media.2021.102096
- https://doi.org//10.1016/j.psep.2020.11.007
- https://doi.org//10.3389/fimmu.2020.01581
- https://doi.org//10.1007/s00146-020-00978-0
- https://doi.org//10.2196/21476
- https://doi.org//10.1371/journal.pmed.1000097
- https://doi.org//10.7326/M18-1377
- https://doi.org//10.7326/M18-1376
- https://doi.org//10.1007/s00330-020-07087-y
- https://doi.org//10.1016/j.compbiomed.2020.103795
- https://doi.org//10.1109/TMI.2020.2996256
- https://doi.org//10.2196/19569
- https://doi.org//10.1148/radiol.2020200905
- https://doi.org//10.1186/s12938-020-00809-9
- https://doi.org//10.1038/s41591-020-0931-3
- https://doi.org//10.1155/2020/8843664
- https://doi.org//10.1109/TMI.2020.2995508
- https://doi.org//10.3233/SHTI200481
- https://doi.org//10.1007/s11356-020-10133-3
- https://doi.org//10.1109/TMI.2020.2994908
- https://doi.org//10.1183/13993003.00775-2020
- https://doi.org//10.1016/j.ejrad.2020.109041
- https://doi.org//10.1016/j.chaos.2020.110153
- https://doi.org//10.1016/j.cell.2020.04.045
- https://doi.org//10.1038/s41467-020-17971-2
- https://doi.org//10.1080/07391102.2020.1788642
- https://doi.org//10.1007/s00330-020-07044-9
- https://doi.org//10.1007/s00259-020-04929-1
- https://doi.org//10.1016/j.eng.2020.04.010
- https://doi.org//10.21037/atm.2020.03.132
- https://doi.org//10.1007/s13246-020-00865-4
- https://doi.org//10.1038/s41598-020-70479-z
- https://doi.org//10.1007/s13246-020-00888-x
- https://doi.org//10.1371/journal.pone.0235187
- https://doi.org//10.1016/j.cmpb.2020.105581
- https://doi.org//10.1016/j.compbiomed.2020.103869
- https://doi.org//10.1148/radiol.2020201874
- https://doi.org//10.1016/j.chaos.2020.110245
- https://doi.org//10.1016/j.compbiomed.2020.103792
- https://doi.org//10.1016/j.compbiomed.2020.103805
- https://doi.org//10.1007/s00264-020-04609-7
- https://doi.org//10.1016/j.chaos.2020.110071
- https://doi.org//10.1016/j.cmpb.2020.105608
- https://doi.org//10.1155/2020/8828855
- https://doi.org//10.1016/j.imu.2020.100412
- https://doi.org//10.1016/j.bbe.2020.08.008
- https://doi.org//10.1016/j.asoc.2020.106580
- https://doi.org//10.3233/XST-200715
- https://doi.org//10.1016/j.imu.2020.100360
- https://doi.org//10.1109/ACCESS.2020.3003810
- https://doi.org//10.1016/j.chaos.2020.110122
- https://doi.org//10.1016/j.mehy.2020.109761
- https://doi.org//10.1109/ACCESS.2020.2994762
- https://doi.org//10.1007/s11739-020-02475-0
- https://doi.org//10.3389/fpubh.2020.00357
- https://doi.org//10.1101/2020.04.17.20070219
- https://doi.org//10.1038/s41467-020-17280-8
- https://doi.org//10.1017/S0950268820001727
- https://doi.org//10.1007/s00330-020-07269-8
- https://doi.org//10.1183/13993003.01104-2020
- https://doi.org//10.3389/fcell.2020.00683
- https://doi.org//10.1186/s12938-020-00807-x
- https://doi.org//10.1038/s41598-020-71114-7
- https://doi.org//10.3390/jcm9061668
- https://doi.org//10.1097/RTI.0000000000000544
- https://doi.org//10.3389/fbioe.2020.00898
- https://doi.org//10.3390/ijerph17114076
- https://doi.org//10.2196/18828
- https://doi.org//10.3390/ijerph17124204
- https://doi.org//10.1016/j.chaos.2020.110212
- https://doi.org//10.1109/TCYB.2020.2990162
- https://doi.org//10.1016/j.chaos.2020.110017
- https://doi.org//10.1016/j.chaos.2020.109864
- https://doi.org//10.1016/j.chaos.2020.109853
- https://doi.org//10.1016/j.chaos.2020.110227
- https://doi.org//10.1021/acs.jpclett.0c01579
- https://doi.org//10.1016/j.bj.2020.05.001
- https://doi.org//10.1021/acs.jproteome.0c00316
- https://doi.org//10.15252/emmm.202012697
- https://doi.org//10.1007/s12539-020-00376-6
- https://doi.org//10.1021/acs.jpclett.0c02278
- https://doi.org//10.1080/07391102.2020.1802341
- https://doi.org//10.1002/minf.202000028
- https://doi.org//10.1007/s11604-018-0726-3
- https://doi.org//10.1038/cddis.2014.9
- https://doi.org//10.1002/jmv.25786
- https://doi.org//10.1148/radiol.2020200432
- https://doi.org//10.1038/nature14539
- https://doi.org//10.1016/j.amjmed.2017.10.035
- https://doi.org//10.1148/radiol.2020200241
- https://doi.org//10.1148/radiol.2020201343
- https://doi.org//10.2214/AJR.20.23391
- https://doi.org//10.1007/s11547-020-01197-9
- https://doi.org//10.1007/s10916-020-01562-1
- https://doi.org//10.3348/kjr.2019.0025
- https://doi.org//10.2196/preprints.21476
- https://doi.org//10.1002/adtp.202000034
- https://doi.org//10.1093/bioinformatics/btaa224
- https://doi.org//10.1080/07391102.2020.1855250
- https://doi.org//10.1038/s41598-020-78758-5
- https://doi.org//10.1017/dmp.2021.9
Usage metrics
Read the peer-reviewed publication
Categories
- Radiology and Organ Imaging
- Foetal Development and Medicine
- Obstetrics and Gynaecology
- Primary Health Care
- Medical and Health Sciences not elsewhere classified
- Dermatology
- Emergency Medicine
- Gastroenterology and Hepatology
- Geriatrics and Gerontology
- Intensive Care
- Medical Genetics (excl. Cancer Genetics)
- Nephrology and Urology
- Nuclear Medicine
- Orthopaedics
- Otorhinolaryngology
- Pathology (excl. Oral Pathology)
- Family Care
CC BY 4.0