Revisión Sistemática de Literatura: Análisis de viabilidad para la detección y diagnóstico de Covid-19, aplicando modelos de Inteligencia Artificial (IA)

Autores/as

  • Jonathan Ricardo Tillaguango Jiménez Carrera de Ingeniería en Sistemas/Computación, Universidad Nacional de Loja, Loja, Ecuador

DOI:

https://doi.org/10.54753/cedamaz.v11i2.1183

Palabras clave:

Diagnóstico, Covid-19, CNN, VGG16, Radiografías pulmonares, Rayos X.

Resumen

Desde la declaración de la emergencia sanitaria provocada por el Covid-19 en marzo del 2020, hasta la fecha, existen aproximadamente 219 millones de contagiados, de los cuales 4,5 millones han muerto. En nuestro país, se estima que existen 508 mil casos confirmados y aproximadamente 32 mil muertes a causa de esta enfermedad. Pese a disponer de métodos verificados para diagnosticar Covid-19, las pruebas Polymerase Chain Reaction (PCR) o Real Time-PCR (RT-PCR), tienden a generar falsos positivos y negativos entre el 30\% y el 40\%. Por tal razón, ayudar a los métodos tradicionales a realizar un diagnóstico clínico preciso, usando como datos de entrada radiografías pulmonares, supone un cambio radical en la detección de Covid-19, puesto que, es una alternativa mucho más cómoda para el paciente y lo que es más importante, aumenta el nivel de precisión reduciendo a la vez, las tasas de falsos positivos y negativos. En la presente Revisión Sistemática de Literatura (RSL), la cual se ha basado en la metodología de Bárbara Kitchenham, busca sustentar la creación de un modelo basado en la arquitectura de Redes Neuronales Convolucionales (CNN), capaz de analizar radiografías pulmonares para el diagnóstico de Covid-19. Como resultado, se pudo dar contestación a las tres preguntas de investigación planteadas, mismas que sirvieron para delimitar el presente estudio, para ello se analizó 41 trabajos relacionados (TR), los cuales se enfocaban en diferentes métodos de diagnóstico basados en Inteligencia Artificial (IA), no obstante 16 de estos TR hacían referencia al uso de CNN para el diagnóstico de Covid-19 mediante el análisis de tomografías computarizadas (TC) y radiografías pulmonares (Rayos X), siendo esta última la opción más viable para aplicarlo en nuestro entorno, debido la disponibilidad de datos. Además, el uso de recursos por parte de estos métodos es asequible tanto a nivel local, usando la Unidad de Procesamiento Gráfico (GPU) Nvidia y memoria RAM superior a 8GB como base, o utilizar procesamiento en la nube usando Google Colab.

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Adly, A. S. A. S., Adly, A. S. A. S., y Adly, M. S. (2020). Approaches Based on artificial intelligence and the internet of intelligent things to prevent the spread of COVID-19: Scoping review. Journal of Medical Internet Research, 22(8). https://doi.org/10.2196/19104 DOI: https://doi.org/10.2196/19104

Al-Bawi, A., Al-Kaabi, K., Jeryo, M., y Al-Fatlawi, A. (2020). CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images. Research on Biomedical Engineering. https://doi.org/10.1007/s42600-020-00110-7 DOI: https://doi.org/10.1007/s42600-020-00110-7

Alsharif, W., y Qurashi, A. (2020). Effectiveness of COVID-19 diagnosis and management tools: A review. Radiography. https://doi.org/10.1016/j.radi.2020.09.010 DOI: https://doi.org/10.1016/j.radi.2020.09.010

Arias-Londoño, J. D., Gomez-Garcia, J. A., Moro-Velazquez, L., y Godino-Llorente, J. I. (2020). Artificial Intelligence applied to chest X-Ray images for the automatic detection of COVID-19. A thoughtful evaluation approach. DOI: https://doi.org/10.1109/ACCESS.2020.3044858

Born, J., Wiedemann, N., Cossio, M., Buhre, C., Brändle, G., Leidermann, K., Aujayeb, A., Moor, M., Rieck, B., y Borgwardt, K. (2021). Accelerating detection of lung pathologies with explainable ultrasound image analysis. Applied Sciences (Switzerland), 11(2), 1–23. https://doi.org/10.3390/app11020672 DOI: https://doi.org/10.3390/app11020672

Cai, W., Liu, T., Xue, X., Luo, G., Wang, X., Shen, Y., Fang, Q., Sheng, J., Chen, F., y Liang, T. (2020). CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients. Academic Radiology, 27(12), 1665–1678. https://doi.org/10.1016/j.acra.2020.09.004 DOI: https://doi.org/10.1016/j.acra.2020.09.004

Chakraborty, C., y Abougreen, A. (2018). Intelligent Internet of Things and Advanced Machine Learning Techniques for COVID-19. EAI Endorsed Transactions on Pervasive Health and Technology, 168505. https://doi.org/10.4108/eai.28-1-2021.168505 DOI: https://doi.org/10.4108/eai.28-1-2021.168505

Chattopadhyay, S., Dey, A., Singh, P. K., Geem, Z. W., y Sarkar, R. (2021). Covid-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer. Diagnostics, 11(2), 315. https://doi.org/10.3390/diagnostics11020315 DOI: https://doi.org/10.3390/diagnostics11020315

Dass, S D S, Meskaran, F., y Saeedi, M. (2020). Expert system for early diagnosis of covid-19. International Journal of Current Research and Review, 12(22), 162–165. https://doi.org/10.31782/IJCRR.2020.122227

Dass, Sharana Dharshikgan Suresh, Meskaran, F., y Saeedi, M. (2020). Expert system for early diagnosis of covid-19. International Journal of Current Research and Review, 12(22), 162–165. https://doi.org/10.31782/IJCRR.2020.122227 DOI: https://doi.org/10.31782/IJCRR.2020.122227

de Freitas Barbosa, V. A., Gomes, J. C., de Santana, M. A., Albuquerque, J. E. A., de Souza, R. G., de Souza, R. E., & dos Santos, W. P. (2021). Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests. Research on Biomedical Engineering. https://doi.org/10.1007/s42600-020-00112-5 DOI: https://doi.org/10.1007/s42600-020-00112-5

El-bana, S., Al-Kabbany, A., y Sharkas, M. (2020). A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans. PeerJ Computer Science, 6, e303. https://doi.org/10.7717/peerj-cs.303 DOI: https://doi.org/10.7717/peerj-cs.303

Elzeki, O. M., Abd Elfattah, M., Salem, H., Hassanien, A. E., & Shams, M. (2021). A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset. PeerJ Computer Science, 7, e364. https://doi.org/10.7717/peerj-cs.364 DOI: https://doi.org/10.7717/peerj-cs.364

Gao, T. (2020). Chest X-ray image analysis and classification for COVID-19 pneumonia detection using deep CNN. In medRxiv. medRxiv. https://doi.org/10.1101/2020.08.20.20178913 DOI: https://doi.org/10.21203/rs.3.rs-64537/v2

Gazzah, S., Bencharef, O., & Marrakech, F. (2020). A Survey on how computer vision can response to urgent need to contribute in COVID-19 pandemics. DOI: https://doi.org/10.1109/ISCV49265.2020.9204043

Gisby, J., Clarke, C. L., Medjeral-Thomas, N., Malik, T. H., Papadaki, A., Mortimer, P. M., Buang, N. B., Lewis, S., Pereira, M., Toulza, F., Fagnano, E., Mawhin, M. A., Dutton, E. E., Tapeng, L., Kirk, P., Behmoaras, J., Sandhu, E., McAdoo, S. P., Prendecki, M. F., … Peters, J. E. (2020). Longitudinal proteomic profiling of high-risk patients with COVID-19 reveals markers of severity and predictors of fatal disease. In medRxiv (Vol. 16, Issue 2, p. e0247176). medRxiv. https://doi.org/10.1101/2020.11.05.20223289' DOI: https://doi.org/10.7554/eLife.64827

Google-Noticias. (2021). Coronavirus (COVID-19).

Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., Spada, L. L., Mirmozafari, M., Dehghani, M., Sabet, A., Roshani, S., Roshani, S., Bayat-Makou, N., Mohamadzade, B., Malek, Z., Jamshidi, A., Kiani, S., Hashemi-Dezaki, H., y Mohyuddin, W. (2020). Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment. IEEE Access, 8, 109581–109595. https://doi.org/10.1109/ACCESS.2020.3001973 DOI: https://doi.org/10.1109/ACCESS.2020.3001973

Javor, D., Kaplan, H., Kaplan, A., Puchner, S. B., Krestan, C., y Baltzer, P. (2020). Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography. European Journal of Radiology, 133. https://doi.org/10.1016/j.ejrad.2020.109402 DOI: https://doi.org/10.1016/j.ejrad.2020.109402

Kamil, M. Y. (2021). A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images. International Journal of Electrical and Computer Engineering, 11(1), 844–850. https://doi.org/10.11591/ijece.v11i1.pp844-850 DOI: https://doi.org/10.11591/ijece.v11i1.pp844-850

Kang, H., Xia, L., Yan, F., Wan, Z., Shi, F., Yuan, H., Jiang, H., Wu, D., Sui, H., Zhang, C., y Shen, D. (2020). Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning. IEEE Transactions on Medical Imaging, 39(8), 2606–2614. https://doi.org/10.1109/TMI.2020.2992546 DOI: https://doi.org/10.1109/TMI.2020.2992546

Kavitha, K. V, Deshpande, S. R., Pandit, A. P., y Unnikrishnan, A. G. (2020). Application of tele-podiatry in diabetic foot management: A series of illustrative cases. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(6), 1991–1995. https://doi.org/10.1016/j.dsx.2020.10.009 DOI: https://doi.org/10.1016/j.dsx.2020.10.009

Kitchenham, B., y Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering.

Kutlu, Y., y Cangozlu, Y. (2021). Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks. Natural and Engineering Sciences, 6(1), 60–74. https://doi.org/10.28978/nesciences.868087 DOI: https://doi.org/10.28978/nesciences.868087

Li, M. D., Little, B. P., Alkasab, T. K., Mendoza, D. P., Succi, M. D., Shepard, J.-A. O., Lev, M. H., y Kalpathy-Cramer, J. (2021). Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs. Academic Radiology. https://doi.org/10.1016/j.acra.2021.01.016 DOI: https://doi.org/10.1016/j.acra.2021.01.016

Li, W. T., Ma, J., Shende, N., Castaneda, G., Chakladar, J., Tsai, J. C., Apostol, L., Honda, C. O., Xu, J., Wong, L. M., Zhang, T., Lee, A., Gnanasekar, A., Honda, T. K., Kuo, S. Z., Yu, M. A., Chang, E. Y., Rajasekaran, M. R., y Ongkeko, W. M. (2020). Using machine learning of clinical data to diagnose COVID-19: A systematic review and meta-analysis. BMC Medical Informatics and Decision Making, 20(1). https://doi.org/10.1186/s12911-020-01266-z DOI: https://doi.org/10.1186/s12911-020-01266-z

Maghded, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., Rawat, D. B., y Rabie, K. (2020). A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study. Proceedings - 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science, IRI 2020, 180–187. https://doi.org/10.1109/IRI49571.2020.00033 DOI: https://doi.org/10.1109/IRI49571.2020.00033

Meng, L., Dong, D., Li, L., Niu, M., Bai, Y., Wang, M., Qiu, X., Zha, Y., y Tian, J. (2020). A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study. IEEE Journal of Biomedical and Health Informatics, 24(12), 3576–3584. https://doi.org/10.1109/JBHI.2020.3034296 DOI: https://doi.org/10.1109/JBHI.2020.3034296

Mohammed, M. A., Abdulkareem, K. H., Al-Waisy, A. S., Mostafa, S. A., Al-Fahdawi, S., Dinar, A. M., Alhakami, W., Baz, A., Al-Mhiqani, M. N., Alhakami, H., Arbaiy, N., Maashi, M. S., Mutlag, A. A., Garcia-Zapirain, B., & De La Torre Diez, I. (2020). Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods. IEEE Access, 8, 99115–99131. https://doi.org/10.1109/ACCESS.2020.2995597' DOI: https://doi.org/10.1109/ACCESS.2020.2995597

Nguyen, D. M. H., Nguyen, D. M., Vu, H., Nguyen, B. T., Nunnari, F., y Sonntag, D. (2020). An Attention Mechanism with Multiple Knowledge Sources for COVID-19 Detection from CT Images.

Nour, M., Cömert, Z., y Polat, K. (2020). A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization. Applied Soft Computing, 97. https://doi.org/10.1016/j.asoc.2020.106580 DOI: https://doi.org/10.1016/j.asoc.2020.106580

OMS. (2021). Enfermedad por el coronavirus (COVID-19): Vacunas. Onu.

OMS, O. M. de la S. (2020). Zoonosis.

Petticrew, M., y Roberts, H. (2008). Systematic Reviews in the Social Sciences: A Practical Guide. In Systematic Reviews in the Social Sciences: A Practical Guide. Blackwell Publishing Ltd. https://doi.org/10.1002/9780470754887 DOI: https://doi.org/10.1002/9780470754887

Purohit, K., Kesarwani, A., Kisku, D. R., y Dalui, M. (2020). COVID-19 detection on chest X-Ray and CT Scan images using multi-image augmented deep learning model. In bioRxiv. bioRxiv. https://doi.org/10.1101/2020.07.15.205567 DOI: https://doi.org/10.1101/2020.07.15.205567

Qiu, J., Peng, S., Yin, J., Wang, J., Jiang, J., Li, Z., Song, H., & Zhang, W. (2021). A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions. Interdisciplinary Sciences: Computational Life Sciences. https://doi.org/10.1007/s12539-020-00410-7 DOI: https://doi.org/10.1007/s12539-020-00410-7

Ramajo, J., y Márquez, M. Á. (2008). Componentes espaciales en el modelo Shift-Share. Una aplicación al caso de las regiones peninsulares españolas. Estadística Española, 50(168), 247–272.

Review, S. (2020). Deep Learning in Detection and Diagnosis of Covid-19 using Radiology Modalities : A. 1–12. DOI: https://doi.org/10.1155/2021/9868517

Sahan, A. M., Al-Itbi, A. S., y Hameed, J. S. (2021). COVID-19 detection based on deep learning and artificial bee colony. 9(1), 29–36. DOI: https://doi.org/10.21533/pen.v9i1.1774

Sethy, P. K., Behera, S. K., Anitha, K., Pandey, C., y Khan, M. R. (2021). Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison. Journal of X-Ray Science and Technology, 1–14. https://doi.org/10.3233/xst-200784 DOI: https://doi.org/10.3233/XST-200784

Silahudin, D., Henderi, y Holidin, A. (2020). Model expert system for diagnosis of COVID-19 using naïve bayes classifier. IOP Conference Series: Materials Science and Engineering, 1007(1). https://doi.org/10.1088/1757-899X/1007/1/012067 DOI: https://doi.org/10.1088/1757-899X/1007/1/012067

Taresh, M., Zhu, N., y Ali Ali, T. A. (2020). Transfer learning to detect COVID-19 automatically from X-ray images, using convolutional neural networks. In medRxiv. medRxiv. https://doi.org/10.1101/2020.08.25.20182170 DOI: https://doi.org/10.1101/2020.08.25.20182170

Thepade, S. D., Bang, S. V., Chaudhari, P. R., y Dindorkar, M. R. (2020). Covid19 Identification from Chest X-ray Images Using Machine Learning Classifiers with GLCM Features. Electronic Letters on Computer Vision and Image Analysis, 19(3), 85–97. https://doi.org/10.5565/REV/ELCVIA.1277 DOI: https://doi.org/10.5565/rev/elcvia.1277

Yao, H, Zhang, N., Zhang, R., Duan, M., Xie, T., Pan, J., Peng, E., Huang, J., Zhang, Y., Xu, X., Xu, H., Zhou, F., y Wang, G. (2020). Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests. Frontiers in Cell and Developmental Biology, 8. https://doi.org/10.3389/fcell.2020.00683

Yao, Haochen, Zhang, N., Zhang, R., Duan, M., Xie, T., Pan, J., Peng, E., Huang, J., Zhang, Y., Xu, X., Xu, H., Zhou, F., y Wang, G. (2020). Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests. Frontiers in Cell and Developmental Biology, 8(10), 2776–2786. https://doi.org/10.3389/fcell.2020.00683 DOI: https://doi.org/10.3389/fcell.2020.00683

Yazdani, S., Minaee, S., Kafieh, R., Saeedizadeh, N., & Sonka, M. (2020). COVID CT-Net: Predicting Covid-19 from chest CT images using attentional convolutional network. ArXiv.

Zhang, D., Liu, X., Shao, M., Sun, Y., Lian, Q., y Zhang, H. (2021). The value of artificial intelligence and imaging diagnosis in the fight against COVID-19. Personal and Ubiquitous Computing. https://doi.org/10.1007/s00779-021-01522-7 DOI: https://doi.org/10.1007/s00779-021-01522-7

Zoabi, Y., Deri-Rozov, S., y Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. Npj Digital Medicine, 4(1). https://doi.org/10.1038/s41746-020-00372-6 DOI: https://doi.org/10.1038/s41746-020-00372-6

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2021-12-24

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Tillaguango Jiménez, J. R. . (2021). Revisión Sistemática de Literatura: Análisis de viabilidad para la detección y diagnóstico de Covid-19, aplicando modelos de Inteligencia Artificial (IA). CEDAMAZ, 11(2), 142–151. https://doi.org/10.54753/cedamaz.v11i2.1183

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