A SURVEY OF AI IMAGING TECHNIQUES FOR COVID-19 DIAGNOSIS AND PROGNOSIS

KK Praneeth Tellakula

praneeth.tkk@gmail.com
Bannari Amman Institute of Technology (Anna University, Department of Electronics And Communication Engineering), Sathyamangalam (India)

Saravana Kumar R


Bannari Amman Institute of Technology (Anna University, Department of Electronics And Communication Engineering), Sathyamangalam (India)

Sanjoy Deb


Bannari Amman Institute of Technology (Anna University, Department of Electronics And Communication Engineering), Sathyamangalam (India)

Abstract

The Coronavirus Disease 2019 (COVID-19) has caused massive infections and death toll. Radiological imaging in chest such as computed tomography (CT) has been instrumental in the diagnosis and evaluation of the lung infection which is the common indication in COVID-19 infected patients. The technological advances in artificial intelligence (AI) furthermore increase the performance of imaging tools and support health professionals. CT, Positron Emission Tomography – CT (PET/CT), X-ray, Magnetic Resonance Imaging (MRI), and Lung Ultrasound (LUS) are used for diagnosis, treatment of COVID-19. Applying AI on image acquisition will help automate the process of scanning and providing protection to lab technicians. AI empowered models help radiologists and health experts in making better clinical decisions. We review AI-empowered medical imaging characteristics, image acquisition, computer-aided models that help in the COVID-19 diagnosis, management, and follow-up. Much emphasis is on CT and X-ray with integrated AI, as they are first choice in many hospitals.


Keywords:

Artificial Intelligence, COVID-19, diagnosis, follow-up, prognosis

Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020). Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology, 2019, 200642. https://doi.org/10.1148/radiol.2020200642
DOI: https://doi.org/10.1148/radiol.2020200642   Google Scholar

Arrieta, A.B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
DOI: https://doi.org/10.1016/j.inffus.2019.12.012   Google Scholar

Bai, H.X., Hsieh, B., Xiong, Z., Halsey, K., Choi, J.W., Tran, T.M.L., Pan, I., Shi, L.-B., Wang, D.-C., Mei, J., Jiang, X.-L., Zeng, Q.-H., Egglin, T.K., Hu, P.-F., Agarwal, S., Xie, F.-F., Li, S., Healey, T., Atalay, M.K., & Liao, W.-H. (2020). Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology, 1, 1–13. https://doi.org/10.1148/radiol.2020200823
DOI: https://doi.org/10.1148/radiol.2020200823   Google Scholar

Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z.A., Zhang, N., Diao, K., Lin, B., Zhu, X., Li, K., Li, S., Shan, H., Jacobi, A., & Chung, M. (2020). Chest CT findings in coronavirus disease 2019 (COVID-19): Relationship to duration of infection. Radiology, 295(3), 685–691. https://doi.org/10.1148/radiol.2020200463
DOI: https://doi.org/10.1148/radiol.2020200463   Google Scholar

Booij, R., Budde, R.P.J., Dijkshoorn, M.L., & van Straten, M. (2019). Accuracy of automated patient positioning in CT using a 3D camera for body contour detection. European Radiology, 29(4), 2079–2088. https://doi.org/10.1007/s00330-018-5745-z
DOI: https://doi.org/10.1007/s00330-018-5745-z   Google Scholar

Castellano, G., Bonilha, L., Li, L.M., & Cendes, F. (2004). Texture analysis of medical images. Clinical Radiology, 59(12), 1061–1069. https://doi.org/10.1016/j.crad.2004.07.008
DOI: https://doi.org/10.1016/j.crad.2004.07.008   Google Scholar

Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Hu, S., Wang, Y., Hu, X., Zheng, B., Zhang, K., Wu, H., Dong, Z., Xu, Y., Zhu, Y., Chen, X., Yu, L., & Yu, H. (2020). Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study. MedRxiv. https://doi.org/10.1101/2020.02.25.20021568
DOI: https://doi.org/10.1101/2020.02.25.20021568   Google Scholar

Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2009). Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology, 296(2), 1–30. https://doi.org/10.1148/radiol.2020200432
DOI: https://doi.org/10.1148/radiol.2020200432   Google Scholar

Ghoshal, B., & Tucker, A. (2020). Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection. http://arxiv.org/abs/2003.10769
  Google Scholar

Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, Ch., He, J., Liu, L., Shan, H., Lei, Ch., Hui, D.S.C., Du, B., Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., Li, S., Wang, J., Liang, Z., Peng, Y., Wei, L., Liu, Y., Hu, Y., Peng, P., Wang, J., Liu, J., Chen, Z., Li, G., Zheng, Z., Qiu, S., Luo, J., Ye, Ch., Zhu, S., & Zhong, N. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. The Journal of Emergency Medicine, 382, 1708-1720. https://doi.org/10.1056/NEJMoa2002032
DOI: https://doi.org/10.1056/NEJMoa2002032   Google Scholar

He, K., Zhang, X., Ren, S., & Sun, J. (2006). Deep Residual Learning for Image Recognition. https://arxiv.org/abs/1512.03385
  Google Scholar

Jin, C., Chen, W., Cao, Y., Xu, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., & Feng, J. (2020). Development and Evaluation of an AI System for COVID-19 Diagnosis. MedRxiv. https://doi.org/10.1101/2020.03.20.20039834
DOI: https://doi.org/10.1101/2020.03.20.20039834   Google Scholar

Jin, S., Wang, B., Xu, H., Luo, C., Wei, L., Zhao, W., Hou, X., Ma, W., Xu, Z., Zheng, Z., Sun, W., Lan, L., Zhang, W., Mu, X., Shi, C., Wang, Z., Lee, J., Jin, Z., Lin, M., Jin, H., Zhang, L., Guo, J., Zhao, B., Ren, Z., Wang, S., You, Z., Dong, J., Wang, X., Wang, J., & Xu, W. (2020). AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks. MedRxiv. https://doi.org/10.1101/2020.03.19.20039354
DOI: https://doi.org/10.1101/2020.03.19.20039354   Google Scholar

Liszewski, M.C., Görkem, S., Sodhi, K.S., & Lee, E.Y. (2017). Lung magnetic resonance imaging for pneumonia in children. Pediatric Radiology, 47(11), 1420–1430. https://doi.org/10.1007/s00247-017-3865-2
DOI: https://doi.org/10.1007/s00247-017-3865-2   Google Scholar

Liu, X., Guo, S., Yang, B., Ma, S., Zhang, H., Li, J., Sun, C., Jin, L., Li, X., Yang, Q., & Fu, Y. (2018). Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks. Journal of Digital Imaging, 31(5), 748–760. https://doi.org/10.1007/s10278-018-0052-4
DOI: https://doi.org/10.1007/s10278-018-0052-4   Google Scholar

Maddah, E., & Beigzadeh, B. (2020). Use of a smartphone thermometer to monitor thermal conductivity changes in diabetic foot ulcers: A pilot study. Journal of Wound Care, 29(1), 61–66.
  Google Scholar

https://doi.org/10.12968/jowc.2020.29.1.61
DOI: https://doi.org/10.12968/jowc.2020.29.1.61   Google Scholar

Marinari, L.A., Danny, M.A., & Miller, W.T. (2019). Sporadic coronavirus lower respiratory tract infection in adults: chest CT imaging features and comparison with other viruses. European Respiratory Journal, 54(suppl 63), PA4547. https://doi.org/10.1183/13993003.congress-2019.PA4547
DOI: https://doi.org/10.1183/13993003.congress-2019.PA4547   Google Scholar

Milletari, F., Navab, N., & Ahmadi, S.A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, 565–571. https://doi.org/10.1109/3DV.2016.79
DOI: https://doi.org/10.1109/3DV.2016.79   Google Scholar

Moro, F., Buonsenso, D., Moruzzi, M.C., Inchingolo, R., Smargiassi, A., Demi, L., Larici, A.R., Scambia, G., Lanzone, A., & Testa, A.C. (2020). How to perform lung ultrasound in pregnant women with suspected COVID-19. Ultrasound in Obstetrics and Gynecology, 55(5), 593–598. https://doi.org/10.1002/uog.22028
DOI: https://doi.org/10.1002/uog.22028   Google Scholar

Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks Ali. https://arxiv.org/abs/2003.10849
DOI: https://doi.org/10.1007/s10044-021-00984-y   Google Scholar

Nemati, E., Rahman, M.M., Nathan, V., Vatanparvar, K., & Kuang, J. (2019). Poster Abstract: A Comprehensive Approach for Cough Type Detection. Proceedings - 4th IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2019 (pp. 15–16). IEEE. https://doi.org/10.1109/CHASE48038.2019.00013
DOI: https://doi.org/10.1109/CHASE48038.2019.00013   Google Scholar

Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., Zheng, D., Wang, J., Hesketh, R.L., Yang, L., & Zheng, Ch. (2020). Time Course of Lung Changes On Chest CT During Recovery From 2019 Novel Coronavirus (COVID-19) Pneumonia. Radiology, 295(3), 1–15. https://doi.org/https://doi.org/10.1148/radiol.2020200370
DOI: https://doi.org/10.1148/radiol.2020200370   Google Scholar

Qi, X., Jiang, Z., Yu, Q., Shao, Ch., Zhang, H., Yue, H., Ma, B., Wang, Y., Liu, Ch., Meng, X., Huang, S., Wang, J., Xu, D., Lei, J., Xie, G., Huang, H., Yang, J., Ji, J., Pan, H., Zou, S., & Ju, S. (2001). Machine learningbased CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARSCoV-2 infection: A multicenter study. MedRxiv. https://doi.org/10.1101/2020.02.29.20029603
DOI: https://doi.org/10.1101/2020.02.29.20029603   Google Scholar

Qin, C., Liu, F., Yen, T.C., & Lan, X. (2020). 18F-FDG PET/CT findings of COVID-19: a series of four highly suspected cases. European Journal of Nuclear Medicine and Molecular Imaging, 47(5), 1281–1286. https://doi.org/10.1007/s00259-020-04734-w
DOI: https://doi.org/10.1007/s00259-020-04734-w   Google Scholar

Rahimzadeh, M., & Attar, A. (2020). A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in Medicine Unlocked, 19, 100360. https://doi.org/10.1016/j.imu.2020.100360
DOI: https://doi.org/10.1016/j.imu.2020.100360   Google Scholar

Richardson, P., Griffin, I., Tucker, C., Smith, D., Oechsle, O., Phelan, A., & Stebbing, J. (2020). Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. The Lancet, 395(10223), e30–e31. https://doi.org/10.1016/S0140-6736(20)30304-4
DOI: https://doi.org/10.1016/S0140-6736(20)30304-4   Google Scholar

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. https://arxiv.org/abs/1505.04597
DOI: https://doi.org/10.1007/978-3-319-24574-4_28   Google Scholar

Shan, F., Gao, Y., Wang, Y., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D., & Shi, Y. (2020). Lung Infection Quantification of COVID-19 in CT Images with Deep Learning. arXiv:2003.04655. https://doi.org/10.1002/mp.14609
DOI: https://doi.org/10.1002/mp.14609   Google Scholar

Shi, F., Xia, L., Shan, F., Wu, D., Wei, Y., Yuan, H., Jiang, H., Gao, Y., Sui, H., & Shen, D. (2020). Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification. http://arxiv.org/abs/2003.09860
DOI: https://doi.org/10.1088/1361-6560/abe838   Google Scholar

Shi, H., Han, X., Jiang, N., Cao, Y., Alwalid, O., Gu, J., Fan, Y., & Zheng, C. (2020). Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. The Lancet Infectious Diseases, 20(4), 425–434. https://doi.org/10.1016/S1473-3099(20)30086-4
DOI: https://doi.org/10.1016/S1473-3099(20)30086-4   Google Scholar

Shi, W., Peng, X., Liu, T., Cheng, Z., Lu, H., Yang, S., Zhang, J., Li, F., Wang, M., Zhang, X., Gao, Y., Shi, Y., Zhang, Z., & Shan, F. (2020). Deep Learning-Based Quantitative Computed Tomography Model in Predicting the Severity of COVID-19: A Retrospective Study in 196 Patients. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3546089
DOI: https://doi.org/10.2139/ssrn.3546089   Google Scholar

Singh, V., Ma, K., Tamersoy, B., Chang, Y.-J., Wimmer, A., O’Donnell, T., & Chen, T. (2017). DARWIN: Deformable Patient Avatar Representation With Deep Image Network. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Collins & S. Duchesne (Eds.), Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science (vol 10434). Springer, Cham. https://doi.org/10.1007/978-3-319-66185-8_56
DOI: https://doi.org/10.1007/978-3-319-66185-8_56   Google Scholar

Song, F., Shi, N., Shan, F., Zhang, Z., Shen, J., Lu, H., Ling, Y., Jiang, Y., & Shi, Y. (2020). Emerging 2019 novel coronavirus (2019-NCoV) pneumonia. Radiology, 295(1), 210–217. https://doi.org/10.1148/radiol.2020200274
DOI: https://doi.org/10.1148/radiol.2020200274   Google Scholar

Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., Zhao, Y., Li, Y., Wang, X., & Peng, Z. (2020). Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA - Journal of the American Medical Association, 323(11), 1061–1069. https://doi.org/10.1001/jama.2020.1585
DOI: https://doi.org/10.1001/jama.2020.1585   Google Scholar

Wang, L., & Wong, A. (2020). COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. http://arxiv.org/abs/2003.09871
DOI: https://doi.org/10.1038/s41598-020-76550-z   Google Scholar

Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B. (2020). A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). MedRxiv. https://doi.org/10.1101/2020.02.14.20023028
DOI: https://doi.org/10.1101/2020.02.14.20023028   Google Scholar

Wang, Y., Hu, M., Li, Q., Zhang, X.-P., Zhai, G., & Yao, N. (2020). Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. http://arxiv.org/abs/2002.05534
  Google Scholar

WHO. (2020). WHO Corona symptoms. https://www.who.int/health-topics/coronavirus#tab=tab_3
  Google Scholar

Wong, H.Y.F., Lam, H.Y.S., Fong, A.H.-T., Leung, S.T., Chin, T.W.-Y., Lo, C.S.Y., Lui, M.M.-S., Lee, J.C.Y., Chiu, K.W.-H., Chung, T., Lee, E.Y.P., Wan, E.Y.F., Hung, F.N.I., Lam, T.P.W., Kuo, M., & Ng, M.-Y. (2016). Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients. Imaging, 279(3), 849–858. https://doi.org/10.1148/radiol.2020201160
DOI: https://doi.org/10.1148/radiol.2020201160   Google Scholar

Yan, L., Zhang, H.-T., Goncalves, J., Xiao, Y., Wang, M., Guo, Y., Sun, C., Tang, X., Jin, L., Zhang, M., Huang, X., Xiao, Y., Cao, H., Chen, Y., Ren, T., Wang, F., Xiao, Y., Huang, S., Tan, X., Huang, N., Jiao, B., Zhang, Y., Luo, A., Mombaerts, L., Jin, J., Cao, Z., Li, S., Xu, H., & Yuan, Y. (2020). A machine learning-based model for survival prediction in patients with severe COVID-19 infection. MedRxiv. https://doi.org/10.1101/2020.02.27.20028027
DOI: https://doi.org/10.1101/2020.02.27.20028027   Google Scholar

Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., Shi, Q., Jin, S., Zhang, L., & You, Z. (2020). COVID19 Chest CT Image Segmentation – A Deep Convolutional Neural Network Solution. http://arxiv.org/abs/2004.10987
  Google Scholar

Yao, X.H., Li, T.Y., He, Z.C., Ping, Y.F., Liu, H.W., Yu, S.C., Mou, H.M., Wang, L.H., Zhang, H.R., Fu, W.J., Luo, T., Liu, F., Guo, Q.N., Chen, C., Xiao, H.L., Guo, H.T., Lin, S., Xiang, D.F., Shi, Y., Pan, G.Q., Li, Q.R., Huang, X., Cui, Y., Liu, X.Z., Tang, W., Pan, P.F., Huang, X.Q., Ding, Y.Q., & Bian, X.W. (2020). A pathological report of three COVID-19 cases by minimal invasive autopsies. Zhonghua bing li xue za zhi = Chinese journal of pathology, 49(5), 411–417. https://doi.org/10.3760/cma.j.cn112151-20200312-00193
  Google Scholar

Ye, Z., Zhang, Y., Wang, Y., Huang, Z., & Song, B. (2020). Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. European Radiology, 30(8), 4381-4389. https://doi.org/10.1007/s00330-020-06801-0
DOI: https://doi.org/10.1007/s00330-020-06801-0   Google Scholar

Zhang, J., Xie, Y., Liao, Z., Pang, G., Verjans, J., Li, W., Sun, Z., He, J., Li, Y., Shen, C., & Xia, Y. (2020). COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection. http://arxiv.org/abs/2003.12338
  Google Scholar

Zhao, D., Yao, F., Wang, L., Zheng, L., Gao, Y., Ye, J., Guo, F., Zhao, H., & Gao, R. (2020). A Comparative Study on the Clinical Features of Coronavirus 2019 (COVID-19) Pneumonia With Other Pneumonias. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 71(15), 756–761. https://doi.org/10.1093/cid/ciaa247
DOI: https://doi.org/10.1093/cid/ciaa247   Google Scholar

Zhavoronkov, A., Aladinskiy, V., Zhebrak, A., Zagribelnyy, B., Terentiev, V., Bezrukov, D., Polykovskiy, D., Shayakhmetov, R., Filimonov, A., Orekhov, P., Yan, Y., Popova, O., Vanhaelen, Q., Aliper, A., & Ivanenkov, Y. (2020). Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches. ChemRxiv. https://doi.org/10.26434/chemrxiv.11829102.v2
DOI: https://doi.org/10.26434/chemrxiv.11829102   Google Scholar

Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020). Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label. MedRxiv. https://doi.org/10.1101/2020.03.12.20027185
DOI: https://doi.org/10.1101/2020.03.12.20027185   Google Scholar

Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (vol. 11045 LNCS, pp. 3–11). Springer, Cham. https://doi.org/10.1007/978-3-030-00889-5_1
DOI: https://doi.org/10.1007/978-3-030-00889-5_1   Google Scholar

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Published
2021-06-30

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Tellakula, K. P., Kumar R, S., & Deb, S. (2021). A SURVEY OF AI IMAGING TECHNIQUES FOR COVID-19 DIAGNOSIS AND PROGNOSIS. Applied Computer Science, 17(2), 40–55. https://doi.org/10.35784/acs-2021-12

Authors

KK Praneeth Tellakula 
praneeth.tkk@gmail.com
Bannari Amman Institute of Technology (Anna University, Department of Electronics And Communication Engineering), Sathyamangalam India

Authors

Saravana Kumar R 

Bannari Amman Institute of Technology (Anna University, Department of Electronics And Communication Engineering), Sathyamangalam India

Authors

Sanjoy Deb 

Bannari Amman Institute of Technology (Anna University, Department of Electronics And Communication Engineering), Sathyamangalam India

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