DEEP LEARNING TECHNIQUES FOR COVID-19 DISEASE DETECTION: A META-ANALYSIS

Main Article Content

Faisal Maqbool Zahid
Shahla Faisal
Mohsin Ali
Khawar Shahzad
Ayesha Khaliq

Keywords

Artificial Intelligence, COVID-19, Diagnostic Accuracy, Sensitivity, Specificity

Abstract

Deep learning has been identified as a new technology with the potential to support medical decision-making for a variety of disorders, including localized and diffuse COVID-19 disease. In the medical field, deep learning has gained importance for processing the complexity and amount of imaging data like CT scans and X-ray images. The current study evaluates the detection accuracy of deep learning methods for the detection of COVID-19. A search technique was devised to search three databases Web of Science, PubMed, and Google Scholar, and looked for studies that were published between January 1 and December 15, 2020. The meta-analysis was performed using the selected 22 studies, comprising 4595 chest X-ray images obtained from the patients of COVID-19. The value for pooled sensitivity obtained was 0.91 with a 95% CI of 0.89-0.94 and for pooled specificity was 0.94 with a 95% CI of 0.90-0.96. The value of heterogeneity was obtained I2 = 78%, (p < 0.01). Our findings demonstrate that deep learning models have a great potential for appropriately classifying COVID-19 cases and separating them from patients suffering from other types of pneumonia as well as healthy people. Implementing deep learning-based technologies can help radiologists detect COVID-19 correctly and promptly, and thereby address the COVID-19 pandemic. Health practitioners can use artificial intelligence and deep learning systems to make faster and more efficient decisions.

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References

1. M. Park, A. R. Cook, J. T. Lim, Y. Sun, B. L. Dickens, A systematic review of covid-19 epidemiology based on current evidence, Journal of Clinical Medicine 9 (2020) 967.
2. J. Sun, W.-T. He, L. Wang, A. Lai, X. Ji, X. Zhai, G. Li, M. A. Suchard, J. Tian, J. Zhou, et al., Covid-19: epidemiology, evolution, and cross-disciplinary perspectives, Trends in molecular medicine 26 (2020) 483–495.
3. A. Remuzzi, G. Remuzzi, Covid-19 and italy: what next?, The lancet 395 (2020) 1225–1228.

4. E. Fan, J. R. Beitler, L. Brochard, C. S. Calfee, N. D. Ferguson, A. S. Slutsky, D. Brodie, Covid-19-associated acute respiratory distress syndrome: is a different approach to management warranted?, The Lancet Respiratory Medicine (2020).
5. A. Bernheim, X. Mei, M. Huang, Y. Yang, Z. A. Fayad, N. Zhang, K. Diao, B. Lin, X. Zhu, K. Li, et al., Chest ct findings in coronavirus disease-19 (covid-19): relationship to duration of infection, Radiology (2020) 200463.
6. B. P. Jones, E. T. Tay, I. Elikashvili, J. E. Sanders, A. Z. Paul, B. P. Nelson, L. A. Spina, J. W. Tsung, Feasibility and safety of substituting lung ultrasonography for chest radiography when diagnosing pneumonia in children: a randomized controlled trial, Chest 150 (2016) 131–138.
7. X. Ye, H. Xiao, B. Chen, S. Zhang, Accuracy of lung ultrasonography versus chest radiography for the diagnosis of adult community-acquired pneumonia: review of the literature and meta-analysis, PloS one 10 (2015) e0130066.
8. H. X. Bai, B. Hsieh, Z. Xiong, K. Halsey, J. W. Choi, T. M. L. Tran, I. Pan, L.-B. Shi, D.-C. Wang, J. Mei, et al., Performance of radiologists in differentiating covid-19 from non-covid-19 viral pneumonia at chest ct, Radiology 296 (2020) E46–E54.
9. T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, Z. Sun, L. Xia, Correlation of chest ct and rt-pcr testing for coronavirus disease 2019 (covid-19) in china: a report of 1014 cases, Radiology 296 (2020) E32–E40.
10. H. Shi, X. Han, N. Jiang, Y. Cao, O. Alwalid, J. Gu, Y. Fan, C. Zheng, Radiological findings from 81 patients with covid-19 pneumonia in wuhan, china: a descriptive study, The Lancet infectious diseases 20 (2020) 425–434.
11. I. D. Apostolopoulos, T. A. Mpesiana, Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks, Physical and Engineering Sciences in Medicine 43 (2020) 635–640.
12. M. Moezzi, K. Shirbandi, H. K. Shahvandi, B. Arjmand, F. Rahim, The diagnostic accuracy of artificial intelligence-assisted ct imaging in covid-19 disease: A systematic review and meta-analysis, Informatics in medicine unlocked (2021) 100591.
13. H. Y. F. Wong, H. Y. S. Lam, A. H.-T. Fong, S. T. Leung, T. W.-Y. Chin, C. S. Y. Lo, M. M.-S. Lui,
14. J. C. Y. Lee, K. W.-H. Chiu, T. W.-H. Chung, et al., Frequency and distribution of chest radiographic findings in patients positive for covid-19, Radiology 296 (2020) E72–E78.
15. A. A. Borkowski, N. A. Viswanadhan, L. B. Thomas, R. D. Guzman, L. A. Deland, S. M. Mastorides, Using artificial intelligence for covid-19 chest x-ray diagnosis, Federal Practitioner 37 (2020) 398.
16. A. Khan, M. Z. Asghar, H. Ahmad, F. M. Kundi, S. Ismail, A rule-based sentiment classification framework for health reviews on mobile social media, Journal of Medical Imaging and Health Informatics 7 (2017) 1445–1453.
17. H. Ahmad, A. Arif, A. M. Khattak, A. Habib, M. Z. Asghar, B. Shah, Applying deep neural networks for predicting dark triad personality trait of online users, in: 2020 International Conference on Information Networking (ICOIN), 2020, pp. 102–105. doi:10.1109/ICOIN48656.2020.9016525.
18. H. Ahmad, M. Z. Asghar, F. M. Alotaibi, I. A. Hameed, Applying deep learning technique for depression classification in social media text, Journal of Medical Imaging and Health Informatics 10 (2020) 2446–2451.
19. L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Z. Fang, Q. Song, et al., Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct, Radiology (2020).
20. S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, M. Cai, J. Yang, Y. Li, X. Meng, et al., A deep learning algorithm using ct images to screen for corona virus disease (covid-19), European radiology (2021) 1–9.
21. J. Chen, L. Wu, J. Zhang, L. Zhang, D. Gong, Y. Zhao, Q. Chen, S. Huang, M. Yang, X. Yang, et al., Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography, Scientific reports 10 (2020) 1–11.
22. X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, L. Yu, Q. Ni, Y. Chen, J. Su, et al., A deep learning system to screen novel coronavirus disease 2019 pneumonia, Engineering 6 (2020) 1122–1129.
23. M. Alsharif, Y. Alsharif, S. Chaudhry, M. Albreem, A. Jahid, E. Hwang, Artificial intelligence technology for diagnosing covid-19 cases: a review of substantial issues, Eur Rev Med Pharmacol Sci 24 (2020) 9226–9233.
24. Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., ... & Schönlieb, C. B. (2021). Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence, 3(3), 199-217.
25. Y. Pathak, P. K. Shukla, A. Tiwari, S. Stalin, S. Singh, Deep transfer learning based classification model for covid-19 disease, Irbm (2020).
26. A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, A. Mohammadi, Application of deep learning technique to manage covid-19 in routine clinical practice using ct images: Results of 10 convolutional neural networks, Computers in biology and medicine 121 (2020) 103795.
27. A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, P. R. Pinheiro, Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection, Ieee Access 8 (2020) 91916–91923.
28. O. Attallah, D. A. Ragab, M. Sharkas, Multi-deep: a novel cad system for coronavirus (covid-19) diagnosis from ct images using multiple convolution neural networks, PeerJ 8 (2020) e10086.
29. X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, W. Liu, C. Zheng, A weakly-supervised framework for covid-19 classification and lesion localization from chest ct, IEEE transactions on medical imaging 39 (2020) 2615–2625.
30. H. X. Bai, R. Wang, Z. Xiong, B. Hsieh, K. Chang, K. Halsey, T. M. L. Tran, J. W. Choi, D.-C. Wang, L.-B. Shi, et al., Artificial intelligence augmentation of radiologist performance in distinguishing covid-19 from pneumonia of other origin at chest ct, Radiology 296 (2020) E156–E165.
31. P. Gifani, A. Shalbaf, M. Vafaeezadeh, Automated detection of covid-19 using ensemble of transfer learning with deep convolutional neural network based on ct scans, International Journal of Computer Assisted Radiology and Surgery 16 (2021) 115–123. URL: https://doi.org/10.1007/ s11548-020-02286-w. doi:10.1007/s11548-020-02286-w.
32. Z. Han, B. Wei, Y. Hong, T. Li, J. Cong, X. Zhu, H. Wei, W. Zhang, Accurate screening of covid-19 using attention-based deep 3d multiple instance learning, IEEE transactions on medical imaging 39 (2020) 2584–2594.
33. S. A. Harmon, T. H. Sanford, S. Xu, E. B. Turkbey, H. Roth, Z. Xu, D. Yang, A. Myronenko,
34. V. Anderson, A. Amalou, et al., Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets, Nature communications 11 (2020) 1–7.
35. D. Javor, H. Kaplan, A. Kaplan, S. Puchner, C. Krestan, P. Baltzer, Deep learning analysis provides accurate covid-19 diagnosis on chest computed tomography, European journal of radiology 133 (2020) 109402.
36. C. Jin, W. Chen, Y. Cao, Z. Xu, Z. Tan, X. Zhang, L. Deng, C. Zheng, J. Zhou, H. Shi, et al., Development and evaluation of an artificial intelligence system for covid-19 diagnosis, Nature communications 11 (2020) 1–14.
37. H. Ko, H. Chung, W. S. Kang, K. W. Kim, Y. Shin, S. J. Kang, J. H. Lee, Y. J. Kim, N. Y. Kim, H. Jung, et al., Covid-19 pneumonia diagnosis using a simple 2d deep learning framework with a single chest ct image: model development and validation, Journal of medical Internet research 22 (2020) e19569.
38. X. Mei, H.-C. Lee, K.-y. Diao, M. Huang, B. Lin, C. Liu, Z. Xie, Y. Ma, P. M. Robson, M. Chung, et al., Artificial intelligence–enabled rapid diagnosis of patients with covid-19, Nature medicine 26 (2020) 1224–1228.
39. X. Ouyang, J. Huo, L. Xia, F. Shan, J. Liu, Z. Mo, F. Yan, Z. Ding, Q. Yang, B. Song, et al., Dual-sampling attention network for diagnosis of covid-19 from community acquired pneumonia, IEEE Transactions on Medical Imaging 39 (2020) 2595–2605.
40. M. Agarwal, L. Saba, S. K. Gupta, A. Carriero, Z. Falaschi, A. Pasche`, P. Danna, A. El-Baz, S. Naidu, J. S. Suri, A novel block imaging technique using nine artificial intelligence models for covid-19 disease classification, characterization and severity measurement in lung computed tomography scans on an italian cohort, Journal of Medical Systems 45 (2021) 1–30.
41. J. Song, H. Wang, Y. Liu, W. Wu, G. Dai, Z. Wu, P. Zhu, W. Zhang, K. W. Yeom, K. Deng, End-to-end automatic differentiation of the coronavirus disease 2019 (covid-19) from viral pneumonia based on chest ct, European journal of nuclear medicine and molecular imaging 47 (2020) 2516–2524.
42. J. Wang, Y. Bao, Y. Wen, H. Lu, H. Luo, Y. Xiang, X. Li, C. Liu, D. Qian, Prior-attention residual learning for more discriminative covid-19 screening in ct images, IEEE Transactions on Medical Imaging 39 (2020) 2572–2583.
43. S. Wang, Y. Zha, W. Li, Q. Wu, X. Li, M. Niu, M. Wang, X. Qiu, H. Li, H. Yu, et al., A fully automatic deep learning system for covid-19 diagnostic and prognostic analysis, European Respiratory Journal 56 (2020).
44. X. Wu, H. Hui, M. Niu, L. Li, L. Wang, B. He, X. Yang, L. Li, H. Li, J. Tian, et al., Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study, European Journal of Radiology 128 (2020) 109041.
45. H. Mahmoud, M. S. Taha, A. Askoura, M. Aleem, A. Omran, S. Aboelela, Can chest ct improve sensitivity of covid-19 diagnosis in comparison to pcr? a meta-analysis study, The Egyptian Journal of Otolaryngology 36 (2020) 1–7.
46. T. E. Komolafe, J. Agbo, E. O. Olaniyi, K. Komolafe, X. Yang, Prevalence of covid-19 diagnostic output with chest computed tomography: A systematic review and meta-analysis, Diagnostics 10 (2020) 1023.
47. M. T. Vafea, E. Atalla, M. Kalligeros, E. Mylona, F. Shehadeh, E. Mylonakis, Chest ct findings in asymptomatic cases with covid-19: a systematic review and meta-analysis, Clinical radiology 75 (2020) 876–e33.
48. C. Bao, X. Liu, H. Zhang, Y. Li, J. Liu, Coronavirus disease 2019 (covid-19) ct findings: a systematic review and meta-analysis, Journal of the American college of radiology 17 (2020) 701–709.
49. B. Bo¨ger, M. M. Fachi, R. O. Vilhena, A. F. Cobre, F. S. Tonin, R. Pontarolo, Systematic review with meta-analysis of the accuracy of diagnostic tests for covid-19, American journal of infection control 49 (2021) 21–29.
50. H. Kim, H. Hong, S. H. Yoon, Diagnostic performance of ct and reverse transcriptase polymerase chain reaction for coronavirus disease 2019: a meta-analysis, Radiology 296 (2020) E145–E155.
51. M. L. Duarte, L. R. d. Santos, A. C. d. S. Contenc¸as, W. Iared, M. S. PeccinA´ . N. Atallah, Reverse-transcriptase polymerase chain reaction versus chest computed tomography for detecting early symptoms of covid-19. a diagnostic accuracy systematic review and meta-analysis, Sao Paulo Medical Journal 138 (2020) 422–432.

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