nach oben

Erschienen in:

05.02.2021 | Original Paper

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

verfasst von: José Daniel López-Cabrera, Rubén Orozco-Morales, Jorge Armando Portal-Diaz, Orlando Lovelle-Enríquez, Marlén Pérez-Díaz

Erschienen in: Health and Technology | Ausgabe 2/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

Vorheriger Artikel China health technology and stringency containment measures during COVID-19 pandemic: A discussion of first and second wave of COVID-19

Nächster Artikel Impact of the recreational use of virtual reality on physical and mental wellbeing during the Covid-19 lockdown

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

https://www.worldometers.info/coronavirus/

https://www.kaggle.com/c/rsna-pneumonia-detection-challenge

https://github.com/ieee8023/covid-chestxray-dataset

https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/data#

https://www.kaggle.com/ahmedali2019/pneumonia-sample-xrays

https://twitter.com/ChestImaging

Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. Int J Antimicrob Agents. 2020;55(3):105924. https://doi.org/10.1016/j.ijantimicag.2020.105924.CrossRef

Salman S, Salem ML. Routine childhood immunization may protect against COVID-19. Med Hypotheses. 2020;140:109689. https://doi.org/10.1016/j.mehy.2020.109689.CrossRef

Xie M, Chen Q. Insight into 2019 novel coronavirus — An updated interim review and lessons from SARS-CoV and MERS-CoV. Int J Infect Dis. 2020;94:119–24. https://doi.org/10.1016/j.ijid.2020.03.071.CrossRef

Liu R, et al. Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020. Clin Chim Acta. 2020;505:172–5. https://doi.org/10.1016/j.cca.2020.03.009.CrossRef

Narayan Das N, Kumar N, Kaur M, Kumar V, Singh D. Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays. IRBM, 2020. https://doi.org/10.1016/j.irbm.2020.07.001.

Ai T, et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020;296(2):E32–40.CrossRef

Dong D, et al. The role of imaging in the detection and management of COVID-19: a review. IEEE Rev Biomed Eng, pp. 1–1, 2020. https://doi.org/10.1109/RBME.2020.2990959.

Kanne JP, Little BP, Chung JH, Elicker BM, Ketai LH. Essentials for radiologists on COVID-19: an update—radiology scientific expert panel. Radiology. 2020;296(2):E113–4. https://doi.org/10.1148/radiol.2020200527.CrossRef

Castiglioni I, et al. Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy. MedRxiv, 2020.

10.

Apostolopoulos ID, Mpesiana TA. Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020. https://doi.org/10.1007/s13246-020-00865-4.CrossRef

11.

Asif S, Wenhui Y, Jin H, Tao Y, Jinhai S. Classification of COVID-19 from chest X-ray images using deep convolutional neural networks. MedRxiv. 2020. https://doi.org/10.1101/2020.05.01.20088211.

12.

Chowdhury MEH, et al. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access. 2020;8:132665–76. https://doi.org/10.1109/ACCESS.2020.3010287.CrossRef

13.

Islam MdZ, Islam MdM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inform Med Unlocked. 2020;20:100412. https://doi.org/10.1016/j.imu.2020.100412.CrossRef

14.

Nour M, Cömert Z, Polat K. A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization. Appl Soft Comput, p. 106580, 2020. https://doi.org/10.1016/j.asoc.2020.106580.

15.

Oh Y, Park S, Ye JC. Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans Med Imaging, 2020. https://doi.org/10.1109/TMI.2020.2993291.

16.

Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med, vol. 121, p. 103792, 2020. https://doi.org/10.1016/j.compbiomed.2020.103792.

17.

Pereira RM, Bertolini D, Teixeira LO, Silla CN, Costa YMG. COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Programs Biomed, p. 105532, 2020. https://doi.org/10.1016/j.cmpb.2020.105532.

18.

Wang L, Liu ZQ, Wong A. COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. 2020. http://arxiv.org/abs/2003.08971v4.

19.

Laghi A. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence. Lancet Digit Health. 2020;2(5):e225. https://doi.org/10.1016/S2589-7500(20)30079-0.CrossRef

20.

Ilyas M, Rehman H, Nait-ali A. Detection of Covid-19 from chest X-ray images using artificial intelligence: an early review. ArXiv Prepr. ArXiv200405436, 2020.

21.

Shi F, et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev Biomed Eng, pp. 1–1, 2020, https://doi.org/10.1109/RBME.2020.2987975.

22.

Bullock J, Luccioni A, Pham KH, Lam CSN, Luengo-Oroz M. Mapping the landscape of artificial intelligence applications against COVID-19. ArXiv200311336 Cs, 2020, Accessed 25 Aug 2020. [Online]. Available: http://arxiv.org/abs/2003.11336.

23.

Ulhaq A, Khan A, Gomes D, Paul M. Computer vision for COVID-19 control: a survey. http://arxiv.org/abs/2004.09420. Accessed 11 Jun 2020

24.

Nguyen TT. Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions. Prepr. DOI, vol. 10, 2020.

25.

Albahri OS, et al. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. J Infect Public Health. 2020. https://doi.org/10.1016/j.jiph.2020.06.028.CrossRef

26.

Shah FM, et al. A comprehensive survey of COVID-19 detection using medical images. 2020. https://engrxiv.org/9fdyp/download/?format=pdf.

27.

Shoeibi A, et al. Automated detection and forecasting of COVID-19 using deep learning techniques: a review. 2020. http://arxiv.org/abs/2007.10785. Accessed 14 Aug 2020.

28.

Farhat H, Sakr GE, Kilany R. Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. Mach Vis Appl, vol. 31, no. 6, 2020. https://doi.org/10.1007/s00138-020-01101-5.

29.

Islam MM, Karray F, Alhajj R, Zeng J. A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19). 2020. http://arxiv.org/abs/2008.04815. Accessed 28 Aug 2020.

30.

Chen Y, et al. A Survey on Artificial Intelligence in Chest Imaging of COVID-19. BIO Integr. 2020;1(3):137–46. https://doi.org/10.15212/bioi-2020-0015.CrossRef

31.

Maguolo G, Nanni L. A critic evaluation of methods for COVID-19 automatic detection from X-ray images. 2020. http://arxiv.org/abs/2004.12823. Accessed 21 May 2020.

32.

Teixeira LO, Pereira RM, Bertolini D, Oliveira LS, Nanni L, Costa YMG. Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images. 2020. http://arxiv.org/abs/2009.09780. Accessed 29 Sep 2020.

33.

Tabik S, et al. COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images. IEEE J Biomed Health Inform. 2020;24(12):3595–605. https://doi.org/10.1109/JBHI.2020.3037127.CrossRef

34.

Tartaglione E, Barbano CA, Berzovini C, Calandri M, Grangetto M. Unveiling COVID-19 from chest X-ray with deep learning: a hurdles race with small data 2020. http://arxiv.org/abs/2004.05405. Accessed 16 Aug 2020.

35.

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In Adv Neural Inform Process Syst 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012. pp. 1097–1105.

36.

Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning, vol. 1. MIT press Cambridge, 2016.

37.

Hatcher WG, Yu W. A survey of deep learning: Platforms, applications and emerging research trends. IEEE Access. 2018;6:24411–32. https://doi.org/10.1109/ACCESS.2018.2830661.CrossRef

38.

Baltruschat IM, Nickisch H, Grass M, Knopp T, Saalbach A. Comparison of deep learning approaches for multi-label chest X-ray classification. Sci Rep, vol. 9, no. 1, Art. no. 1, 2019. https://doi.org/10.1038/s41598-019-42294-8.

39.

Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLOS Med. 2018;15(11):e1002683. https://doi.org/10.1371/journal.pmed.1002683.CrossRef

40.

Yao L, Prosky J, Covington B, Lyman K. A strong baseline for domain adaptation and generalization in medical imaging. 2019. http://arxiv.org/abs/1904.01638. Accessed 26 Aug 2020.

41.

Prevedello LM, et al. Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions. Radiol Artif Intell. 2019;1(1):e180031. https://doi.org/10.1148/ryai.2019180031.CrossRef

42.

Cohen JP, Hashir M, Brooks R, Bertrand H. On the limits of cross-domain generalization in automated X-ray prediction. 2020. http://arxiv.org/abs/2002.02497. Accessed 05 Aug 2020.

43.

Aggarwal CC. Neural networks and deep learning. Springer, 2018.

44.

Aljondi R, Alghamdi S. Diagnostic value of imaging modalities for COVID-19: Scoping review. J Med Internet Res. 2020;22(8):e19673. https://doi.org/10.2196/19673.CrossRef

45.

Poggiali E, et al. Can lung US help critical care clinicians in the early diagnosis of novel coronavirus (COVID-19) pneumonia? Radiology. 2020;295(3):E6–E6. https://doi.org/10.1148/radiol.2020200847.CrossRef

46.

Ng M-Y, et al. Imaging profile of the COVID-19 infection: Radiologic findings and literature review. Radiol Cardiothorac Imaging. 2020;2(1):e200034. https://doi.org/10.1148/ryct.2020200034.CrossRef

47.

Ghoshal B, Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. 2020. http://arxiv.org/abs/2003.10769. Accessed 29 Jul 2020.

48.

Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. 2020.

49.

Bukhari SUK, Bukhari SSK, Syed A, Shah SSH. The diagnostic evaluation of Convolutional Neural Network (CNN) for the assessment of chest X-ray of patients infected with COVID-19. 2020. https://doi.org/10.1101/2020.03.26.20044610.

50.

Hammoudi K, et al. Deep Learning on chest X-ray images to detect and evaluate pneumonia cases at the era of COVID-19. 2020. http://arxiv.org/abs/2004.03399. Accessed 09 Sep 2020.

51.

Karim MR, Döhmen T, Rebholz-Schuhmann D, Decker S, Cochez M, Beyan O. DeepCOVIDExplainer: Explainable COVID-19 diagnosis based on chest X-ray images. 2020. https://arxiv.org/abs/2004.04582v3. Accessed 10 Jul 2020.

52.

Li X, Li C, Zhu D. COVID-MobileXpert: On-device COVID-19 screening using snapshots of chest X-ray. 2020.

53.

Hemdan EED, Shouman MA, Karar ME. COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. 2020. http://arxiv.org/abs/2003.11055. Accessed 07 Aug 2020.

54.

Narin A, Kaya C, Pamuk, Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. 2020.

55.

Sethy PK, Behera SK, Ratha RK, Biswas P. Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. 2020. https://www.preprints.org/manuscript/202003.0300/v2. Accessed 16 Sep 2020.

56.

Zhang J, Xie Y, Li Y, Shen C, Xia Y. Covid-19 screening on chest X-ray images using deep learning based anomaly detection. 2020.

57.

Cohen JP, Morrison P, Dao L. COVID-19 image data collection. 2020.

58.

Ucar F, Korkmaz D. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses. 2020;140:109761. https://doi.org/10.1016/j.mehy.2020.109761.CrossRef

59.

Toğaçar M, Ergen B, Cömert Z. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput Biol Med. 2020;121:103805. https://doi.org/10.1016/j.compbiomed.2020.103805.CrossRef

60.

Abdel-Basset M, Mohamed R, Elhoseny M, Chakrabortty RK, Ryan M. A Hybrid COVID-19 detection model using an improved marine predators algorithm and a ranking-based diversity reduction strategy. IEEE Access. 2020;8:79521–40. https://doi.org/10.1109/ACCESS.2020.2990893.CrossRef

61.

Naudé W. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. Ai Soc, p. 1, 2020. https://doi.org/10.1007/s00146-020-00978-0.

62.

Kundu S, Elhalawani H, Gichoya JW, Kahn CE. How might AI and chest imaging help unravel COVID-19’s mysteries? Radiol Artif Intell. 2020;2(3):e200053. https://doi.org/10.1148/ryai.2020200053.CrossRef

63.

Garcia Santa Cruz B, Sölter J, Nicolas Bossa M, Dominik Husch A. On the composition and limitations of publicly available COVID-19 X-ray imaging datasets. 2020. http://arxiv.org/abs/2008.11572. Accessed 21 Sep 2020.

64.

Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT. New machine learning method for image-based diagnosis of COVID-19. PLoS One. 2020;15(6):e0235187. https://doi.org/10.1371/journal.pone.0235187.CrossRef

65.

Jain G, Mittal D, Thakur D, Mittal MK. A deep learning approach to detect Covid-19 coronavirus with X-ray images. Biocybern Biomed Eng. 2020;40(4):1391–405. https://doi.org/10.1016/j.bbe.2020.08.008.CrossRef

66.

Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Singh V. Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet. Chaos Solitons Fract, p. 109944, 2020. https://doi.org/10.1016/j.chaos.2020.109944.

67.

Tsiknakis N, et al. Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays. Exp Ther Med. 2020;20(2):727–35. https://doi.org/10.3892/etm.2020.8797.CrossRef

68.

Wang L, Lin ZQ, Wong A. COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep, vol. 10, no. 1, Art. no. 1, 2020. https://doi.org/10.1038/s41598-020-76550-z.

69.

Wong SC, Gatt A, Stamatescu V, McDonnell MD. Understanding data augmentation for classification: When to warp? In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia, 2016, pp. 1–6. https://doi.org/10.1109/DICTA.2016.7797091.

70.

Luz EJ, Silva PL, Silva R, Silva LP, Moreira GJ, Menotti D. Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images. 2020. https://arxiv.org/pdf/2004.05717.pdf.

71.

Farooq M, Hafeez A. COVID-resnet: A deep learning framework for screening of COVID19 from radiographs. 2020. http://arxiv.org/abs/2003.14395. Accessed 07 Aug 20.

72.

Hassanien AE, Mahdy LN, Ezzat KA, Elmousalami HH, Ella HA. Automatic X-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine. 2020. https://doi.org/10.1101/2020.03.30.20047787.

73.

Tahir A, et al. Coronavirus: Comparing COVID-19, SARS and MERS in the eyes of AI. 2020. http://arxiv.org/abs/2005.11524. Accessed 14 Aug 2020.

74.

Pizer SM, et al. Adaptive histogram equalization and its variations. Comput Vis Graph Image Process. 1987;39(3):355–68. https://doi.org/10.1016/S0734-189X(87)80186-X.CrossRef

75.

Goodfellow I, et al. Generative adversarial nets. 2014. pp. 2672–2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets. Accessed 06 Sep 2020.

76.

Russakovsky O, et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis. 2015;115(3):211–52. https://doi.org/10.1007/s11263-015-0816-y.MathSciNetCrossRef

77.

Goodwin BD, Jaskolski C, Zhong C, Asmani H. Intra-model variability in COVID-19 classification using chest X-ray images. 2020. http://arxiv.org/abs/2005.02167. Accessed 11 Jun 2020.

78.

Apostolopoulos I, Aznaouridis S, Tzani M. Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. 2020.

79.

Alom MZ, Rahman MMS, Nasrin MS, Taha TM, Asari VK. COVID_mtnet: COVID-19 detection with multi-task deep learning approaches. 2020. http://arxiv.org/abs/2004.03747. Accessed 16 Aug 2020.

80.

Lv D, Qi W, Li Y, Sun L, Wang Y. A cascade network for detecting COVID-19 using chest X-rays. 2020. http://arxiv.org/abs/2005.01468. Accessed 14 Aug 2020.

81.

Rajaraman S, Siegelman J, Alderson PO, Folio LS, Folio LR, Antani SK. Iteratively pruned deep learning ensembles for COVID-19 detection in chest X-rays. 2020. http://arxiv.org/abs/2004.08379. Accessed 14 Aug 2020.

82.

Yeh CF, et al. A cascaded learning strategy for robust COVID-19 pneumonia chest X-ray screening. 2020. http://arxiv.org/abs/2004.12786. Accessed 14 Aug 2020.

83.

Kermany DS, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122-1131.e9. https://doi.org/10.1016/j.cell.2018.02.010.CrossRef

84.

Shih G, et al. Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia. Radiol Artif Intell. 2019;1(1):e180041. https://doi.org/10.1148/ryai.2019180041.CrossRef

85.

Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chestx-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017. pp. 3462–3471, https://doi.org/10.1109/CVPR.2017.369.

86.

Bustos A, Pertusa A, Salinas J-M, de la Iglesia-Vayá M. PadChest: a large chest X-ray image dataset with multi-label annotated reports. Med Image Anal. 2020;66:101797. https://doi.org/10.1016/j.media.2020.101797.CrossRef

87.

Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A. COVID-CAPS: a capsule network-based framework for identification of COVID-19 cases from X-ray images. 2020.

Titel: Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging
verfasst von: José Daniel López-Cabrera
Rubén Orozco-Morales
Jorge Armando Portal-Diaz
Orlando Lovelle-Enríquez
Marlén Pérez-Díaz
Publikationsdatum: 05.02.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Health and Technology / Ausgabe 2/2021
Print ISSN: 2190-7188
Elektronische ISSN: 2190-7196
DOI: https://doi.org/10.1007/s12553-021-00520-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2021

Evaluation of the quality of information on the internet about 2019 coronavirus outbreak in relation to orthodontics

Mobile apps for self-management in pregnancy: a systematic review

Applying software-defined networking to support telemedicine health consultation during and post Covid-19 era

Application of telemedicine and eHealth technology for clinical services in response to COVID‑19 pandemic

Impact of the recreational use of virtual reality on physical and mental wellbeing during the Covid-19 lockdown

Design and implementation framework of social assistive robotics for people with dementia - a scoping review

Premium Partner