Artificial intelligence algorithms based approach in evaluating COVID-19 patients and management

1. Montoro FS, Gordo MLP, Tascón ÁD, De Gracia MM, Velez SO, Fernández SF, et al. Quantification of pulmonary opacities using artificial intelligence in chest CT scans during SARS-CoV-2 pandemic: validation and prognostic assessment. Egypt J Radiol Nucl Med. 2023;54:156. MontoroFS GordoMLP TascónÁD De GraciaMM VelezSO FernándezSF Quantification of pulmonary opacities using artificial intelligence in chest CT scans during SARS-CoV-2 pandemic: validation and prognostic assessment Egypt J Radiol Nucl Med 2023 54 156 Search in Google Scholar

2. Xu X, Yu C, Qu J, Zhang L, Jiang S, Huang D, et al. Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. Eur J Nucl Med Mol Imaging. 2020;47:1275–80. XuX YuC QuJ ZhangL JiangS HuangD Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2 Eur J Nucl Med Mol Imaging 2020 47 1275 80 Search in Google Scholar

3. Use of laboratory methods for SARS diagnosis [Internet]. [cited 2025 Apr 9]. Available from: https://www.who.int/publications/m/item/use-of-laboratory-methods-for-sars-diagnosis Use of laboratory methods for SARS diagnosis [Internet] [cited 2025 Apr 9]. Available from: https://www.who.int/publications/m/item/use-of-laboratory-methods-for-sars-diagnosis Search in Google Scholar

4. Ippolito D, Ragusi M, Gandola D, Maino C, Pecorelli A, Terrani S, et al. Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia. Eur Radiol. 2021;31:2726–36. IppolitoD RagusiM GandolaD MainoC PecorelliA TerraniS Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia Eur Radiol 2021 31 2726 36 Search in Google Scholar

5. Chan JF-W, Yip CC-Y, To KK-W, Tang TH-C, Wong SC-Y, Leung K-H, et al. Improved Molecular Diagnosis of COVID-19 by the Novel, Highly Sensitive and Specific COVID-19-RdRp/Hel Real-Time Reverse Transcription-PCR Assay Validated In Vitro and with Clinical Specimens. J Clin Microbiol. 2020;58:e00310–20. ChanJF-W YipCC-Y ToKK-W TangTH-C WongSC-Y LeungK-H Improved Molecular Diagnosis of COVID-19 by the Novel, Highly Sensitive and Specific COVID-19-RdRp/Hel Real-Time Reverse Transcription-PCR Assay Validated In Vitro and with Clinical Specimens J Clin Microbiol 2020 58 e00310 20 Search in Google Scholar

6. Li B, Li X, Wang Y, Han Y, Wang Y, Wang C, et al. Diagnostic value and key features of computed tomography in Coronavirus Disease 2019. Emerg Microbes Infect. 9:787–93. LiB LiX WangY HanY WangY WangC Diagnostic value and key features of computed tomography in Coronavirus Disease 2019 Emerg Microbes Infect 9 787 93 Search in Google Scholar

7. Xie J, Tong Z, Guan X, Du B, Qiu H, Slutsky AS. Critical care crisis and some recommendations during the COVID-19 epidemic in China. Intensive Care Med. 2020;46:837–40. XieJ TongZ GuanX DuB QiuH SlutskyAS Critical care crisis and some recommendations during the COVID-19 epidemic in China Intensive Care Med 2020 46 837 40 Search in Google Scholar

8. Grassi R, Belfiore MP, Montanelli A, Patelli G, Urraro F, Giacobbe G, et al. COVID-19 pneumonia: computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT). Radiol med. 2021;126:553–60. GrassiR BelfioreMP MontanelliA PatelliG UrraroF GiacobbeG COVID-19 pneumonia: computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT) Radiol med 2021 126 553 60 Search in Google Scholar

9. Mossa-Basha M, Meltzer CC, Kim DC, Tuite MJ, Kolli KP, Tan BS. Radiology Department Preparedness for COVID-19: Radiology Scientific Expert Review Panel. Radiology [Internet]. 2020 [cited 2025 Apr 9]; Available from: https://pubs.rsna.org/doi/10.1148/radiol.2020200988 Mossa-BashaM MeltzerCC KimDC TuiteMJ KolliKP TanBS Radiology Department Preparedness for COVID-19: Radiology Scientific Expert Review Panel Radiology [Internet] 2020 [cited 2025 Apr 9]; Available from: https://pubs.rsna.org/doi/10.1148/radiol.2020200988 Search in Google Scholar

10. Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, et al. Coronavirus Disease 2019 (COVID-19): A Perspective from China. Radiology. 2020;296:E15–25. ZuZY JiangMD XuPP ChenW NiQQ LuGM Coronavirus Disease 2019 (COVID-19): A Perspective from China Radiology 2020 296 E15 25 Search in Google Scholar

11. Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology. 2020;295:202–7. ChungM BernheimA MeiX ZhangN HuangM ZengX CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) Radiology 2020 295 202 7 Search in Google Scholar

12. Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology. 2020;295:200463. BernheimA MeiX HuangM YangY FayadZA ZhangN Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection Radiology 2020 295 200463 Search in Google Scholar

13. Arbănași EM, Halmaciu I, Kaller R, Mureșan AV, Arbănași EM, Suciu BA, et al. Systemic Inflammatory Biomarkers and Chest CT Findings as Predictors of Acute Limb Ischemia Risk, Intensive Care Unit Admission, and Mortality in COVID-19 Patients. Diagnostics (Basel). 2022;12:2379. ArbănașiEM HalmaciuI KallerR MureșanAV ArbănașiEM SuciuBA Systemic Inflammatory Biomarkers and Chest CT Findings as Predictors of Acute Limb Ischemia Risk, Intensive Care Unit Admission, and Mortality in COVID-19 Patients Diagnostics (Basel) 2022 12 2379 Search in Google Scholar

14. Karaaslan T, Karaaslan E. Predictive Value of Systemic Immune-inflammation Index in Determining Mortality in COVID-19 Patients. J Crit Care Med (Targu Mures). 2022;8:156–64. KaraaslanT KaraaslanE Predictive Value of Systemic Immune-inflammation Index in Determining Mortality in COVID-19 Patients J Crit Care Med (Targu Mures) 2022 8 156 64 Search in Google Scholar

15. Li Y, Xia L. Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. AJR Am J Roentgenol. 2020;214:1280–6. LiY XiaL Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management AJR Am J Roentgenol 2020 214 1280 6 Search in Google Scholar

16. Lei J, Li J, Li X, Qi X. CT Imaging of the 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology. 2020;295:18. LeiJ LiJ LiX QiX CT Imaging of the 2019 Novel Coronavirus (2019-nCoV) Pneumonia Radiology 2020 295 18 Search in Google Scholar

17. Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine. 2020;382:1708–20. GuanW NiZ HuY LiangW OuC HeJ Clinical Characteristics of Coronavirus Disease 2019 in China New England Journal of Medicine 2020 382 1708 20 Search in Google Scholar

18. Caruso D, Zerunian M, Polici M, Pucciarelli F, Polidori T, Rucci C, et al. Chest CT Features of COVID-19 in Rome, Italy. Radiology. 2020;296:E79–85. CarusoD ZerunianM PoliciM PucciarelliF PolidoriT RucciC Chest CT Features of COVID-19 in Rome, Italy Radiology 2020 296 E79 85 Search in Google Scholar

19. Tian S, Xiong Y, Liu H, Niu L, Guo J, Liao M, et al. Pathological study of the 2019 novel coronavirus disease (COVID-19) through postmortem core biopsies. Mod Pathol. 2020;33:1007–14. TianS XiongY LiuH NiuL GuoJ LiaoM Pathological study of the 2019 novel coronavirus disease (COVID-19) through postmortem core biopsies Mod Pathol 2020 33 1007 14 Search in Google Scholar

20. Wang Y, Dong C, Hu Y, Li C, Ren Q, Zhang X, et al. Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study. Radiology. 2020;296:E55–64. WangY DongC HuY LiC RenQ ZhangX Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study Radiology 2020 296 E55 64 Search in Google Scholar

21. Chabi ML, Dana O, Kennel T, Gence-Breney A, Salvator H, Ballester MC, et al. Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia. Diagnostics. 2021;11:878. ChabiML DanaO KennelT Gence-BreneyA SalvatorH BallesterMC Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia Diagnostics 2021 11 878 Search in Google Scholar

22. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054–62. ZhouF YuT DuR FanG LiuY LiuZ Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study Lancet 2020 395 1054 62 Search in Google Scholar

23. Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, et al. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol. 2021;31:6096–104. WangS KangB MaJ ZengX XiaoM GuoJ A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19) Eur Radiol 2021 31 6096 104 Search in Google Scholar

24. Citu C, Gorun OM, Motoc A, Citu IM, Gorun F, Malita D. Correlation of Lung Damage on CT Scan with Laboratory Inflammatory Markers in COVID-19 Patients: A Single-Center Study from Romania. Journal of Clinical Medicine. 2022;11:4299. CituC GorunOM MotocA CituIM GorunF MalitaD Correlation of Lung Damage on CT Scan with Laboratory Inflammatory Markers in COVID-19 Patients: A Single-Center Study from Romania Journal of Clinical Medicine 2022 11 4299 Search in Google Scholar

25. Di Meglio L, Carriero S, Biondetti P, Wood BJ, Carrafiello G. Chest imaging in patients with acute respiratory failure because of coronavirus disease 2019. Curr Opin Crit Care. 2022;28:17–24. Di MeglioL CarrieroS BiondettiP WoodBJ CarrafielloG Chest imaging in patients with acute respiratory failure because of coronavirus disease 2019 Curr Opin Crit Care 2022 28 17 24 Search in Google Scholar

26. Kanne JP. Chest CT Findings in 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist. Radiology. 2020;295:16–7. KanneJP Chest CT Findings in 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist Radiology 2020 295 16 7 Search in Google Scholar

27. Li J, Yan R, Zhai Y, Qi X, Lei J. Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review. Diagn Interv Radiol. 2021;27:621–32. LiJ YanR ZhaiY QiX LeiJ Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review Diagn Interv Radiol 2021 27 621 32 Search in Google Scholar

28. Grassi R, Cappabianca S, Urraro F, Feragalli B, Montanelli A, Patelli G, et al. Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software. International Journal of Environmental Research and Public Health. 2020;17:6914. GrassiR CappabiancaS UrraroF FeragalliB MontanelliA PatelliG Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software International Journal of Environmental Research and Public Health 2020 17 6914 Search in Google Scholar

29. Contreras-Grande J, Pineda-Borja V, Díaz H, Calderon-Anyosa RJc, Rodríguez B, Morón M. CHEST COMPUTED TOMOGRAPHY FINDINGS ASSOCIATED WITH SEVERITY AND MORTALITY IN PATIENTS WITH COVID-19. Rev Peru Med Exp Salud Publica. 2021;38:206–13. Contreras-GrandeJ Pineda-BorjaV DíazH Calderon-AnyosaRJc RodríguezB MorónM CHEST COMPUTED TOMOGRAPHY FINDINGS ASSOCIATED WITH SEVERITY AND MORTALITY IN PATIENTS WITH COVID-19 Rev Peru Med Exp Salud Publica 2021 38 206 13 Search in Google Scholar

30. Chon Y, Kim JY, Suh YJ, Lee JY, Park JS, Moon SM, et al. Adverse Initial CT Findings Associated with Poor Prognosis of Coronavirus Disease. J Korean Med Sci. 2020;35:e316. ChonY KimJY SuhYJ LeeJY ParkJS MoonSM Adverse Initial CT Findings Associated with Poor Prognosis of Coronavirus Disease J Korean Med Sci 2020 35 e316 Search in Google Scholar

31. Stoian M, Roman A, Boeriu A, Onișor D, Bandila SR, Babă DF, et al. Long-Term Radiological Pulmonary Changes in Mechanically Ventilated Patients with Respiratory Failure due to SARS-CoV-2 Infection. Biomedicines. 2023;11:2637. StoianM RomanA BoeriuA OnișorD BandilaSR BabăDF Long-Term Radiological Pulmonary Changes in Mechanically Ventilated Patients with Respiratory Failure due to SARS-CoV-2 Infection Biomedicines 2023 11 2637 Search in Google Scholar

32. Lassau N, Ammari S, Chouzenoux E, Gortais H, Herent P, Devilder M, et al. Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nat Commun. 2021;12:634. LassauN AmmariS ChouzenouxE GortaisH HerentP DevilderM Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients Nat Commun 2021 12 634 Search in Google Scholar

33. Liu T, Zhang J, Yang Y, Ma H, Li Z, Zhang J, et al. The role of interleukin-6 in monitoring severe case of coronavirus disease 2019. EMBO Mol Med. 2020;12:e12421. LiuT ZhangJ YangY MaH LiZ ZhangJ The role of interleukin-6 in monitoring severe case of coronavirus disease 2019 EMBO Mol Med 2020 12 e12421 Search in Google Scholar

34. Ahmed IM, Hefny HM, Ali AH, Refaie BM, Abdelal KM, Taha EM. Serum interleukin-6 as a predictor of the severity of coronavirus disease 2019. Research and Opinion in Anesthesia & Intensive Care. 2023;10:68–74. AhmedIM HefnyHM AliAH RefaieBM AbdelalKM TahaEM Serum interleukin-6 as a predictor of the severity of coronavirus disease 2019 Research and Opinion in Anesthesia & Intensive Care 2023 10 68 74 Search in Google Scholar

35. Laguna-Goya R, Utrero-Rico A, Talayero P, Lasa-Lazaro M, Ramirez-Fernandez A, Naranjo L, et al. IL-6–based mortality risk model for hospitalized patients with COVID-19. Journal of Allergy and Clinical Immunology. 2020;146:799–807.e9. Laguna-GoyaR Utrero-RicoA TalayeroP Lasa-LazaroM Ramirez-FernandezA NaranjoL IL-6–based mortality risk model for hospitalized patients with COVID-19 Journal of Allergy and Clinical Immunology 2020 146 799 807.e9 Search in Google Scholar

36. Man MA, Rajnoveanu R-M, Motoc NS, Bondor CI, Chis AF, Lesan A, et al. Neutrophil-to-lymphocyte ratio, platelets-to-lymphocyte ratio, and eosinophils correlation with high-resolution computer tomography severity score in COVID-19 patients. PLoS One. 2021;16:e0252599. ManMA RajnoveanuR-M MotocNS BondorCI ChisAF LesanA Neutrophil-to-lymphocyte ratio, platelets-to-lymphocyte ratio, and eosinophils correlation with high-resolution computer tomography severity score in COVID-19 patients PLoS One 2021 16 e0252599 Search in Google Scholar

37. Najjar R. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics. 2023;13:2760. NajjarR Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging Diagnostics 2023 13 2760 Search in Google Scholar