medigraphic.com
ISSN 2077-2874 (Electronic)
EDUMECENTRO. Revista Educación Médica del Centro

2021, Number 4

<< Back Next >>

EduMeCentro 2021; 13 (4)

Artificial intelligence: an imaging tool for COVID-19 positive patients

Lovelle EOA, Machín CWJ, Perez DM

Language: Spanish
References: 28
Page: 274-287
PDF size: 138.38 Kb.


ABSTRACT

Introduction: SARS-Cov-2 disease reinforces the importance of the use of new information and communication technologies based on the development and implementation of artificial intelligence systems that favor diagnosis.
Objective: to describe the possibility of using artificial intelligence as a tool in imaging for COVID-19 positive patients.
Methods: a review of bibliographic sources was carried out in Infomed, SciELO, PubMed and Google Scholar, from 2015 to 2020 with the use of keywords: coronavirus, COVID-19, pneumonia, radiography and artificial intelligence. 28 documents were selected for their relevance in the study.
Development: the creation of artificial intelligence systems that help medical diagnosis requires an interprofessional approach to science and constitutes one of the lines of work in Cuba during the pandemic. An essential condition for the introduction of artificial intelligence in radiological diagnosis is the training that doctors must receive to interact with it, through a training process that includes an evaluation and explanation of the quality of the data associated with both learning and to new predictions.
Conclusions: the use of artificial intelligence will improve the radiologist's performance to distinguish COVID-19; integrating these technologies into routine clinical workflow can help radiologists diagnose accurately.


REFERENCES

  1. Enciclopedia cubana. EcuRed.cu. [Internet]. La Habana; 2019 [acceso 14/09/2020]. Disponible en: https://www.ecured.cu/

  2. Kaplan A, Haenlein M. Siri, Siri in my Hand, who's the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence. J Business Horizons [Internet]. 2019 [citado 10/09/2020];62(1):[aprox. 11 p.]. Disponible en: https://www.sciencedirect.com/science/article/abs/pii/S0007681318301393

  3. Lage Dávila A. Una publicación doble necesaria: Desafíos del desarrollo. El problema de las nuevas funciones de la investigación en la sociedad, visto desde la perspectiva de un hombre de laboratorio y en un país en desarrollo. Rev Medisur [Internet]. 2015 [citado 19/10/2021];13(2):[aprox. 10 p.]. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1727-897X2015000200003&lng=es

  4. Wu X, Hui H, Niu M, Li L, Wang L, He B, Yang X, Li L, Li H, Tian J, Zha Y. Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study. Eur J Radiol [Internet]. 2020 [citado 04/09/2020];128:[aprox. 26 p.]. Disponible en: https://pubmed.ncbi.nlm.nih.gov/32408222/

  5. Hwang EJ, Nam JG, Lim WH, Park SJ, Jeong YS, Kang JH, et al. Deep Learning for Chest Radiograph Diagnosis in the Emergency Department. Radiology [Internet]. 2019 [citado 04/09/2020];293(3):[aprox. 8 p.]. Disponible en: https://pubs.rsna.org/doi/pdf/10.1148/radiol.2019191225

  6. Servín D. Diseño formativo interprofesional: una estrategia para desarrollar el pensamiento complejo en estudiantes de ciencias de la salud. FEM [Internet]. 2020 [citado 28/09/2020];23(1):[aprox. 6 p.]. Disponible en: https://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S2014-98322020000100007&lng=es

  7. Hosseiny M, Kooraki S, Gholamrezanezhad A, Reddy S, Myers L. Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome. AJR Am J Roentgenol [Internet]. 2020 [citado 28/02/2020];214(5):[aprox. 5 p.]. Disponible en: https://pubmed.ncbi.nlm.nih.gov/32108495/

  8. Cisneros Hidalgo YA, González Carbonell RA, Ortiz Prado A, Jacobo Almendáriz VH. Algoritmo para predecir tensiones con técnicas de inteligencia artificial en una tibia humana. Rev Cubana Invest Biomed [Internet]. 2015 [citado 20/09/2020];34(3):[aprox. 12 p.]. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-03002015000300004&lng=es

  9. De la Cruz Figueroa LF, Fernández Rodríguez R, González Rangel MA. Hacia herramientas de inteligencia artificial en la enseñanza médica. Enfoque preliminar. RCIM [Internet]. 2018 [citado 28/09/2020];10(1):[aprox. 8 p.]. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1684-18592018000100008&lng=es

  10. Vidal Ledo MJ, Madruga González A, Valdés Santiago D. Inteligencia artificial en la docencia médica. Educ Med Super [Internet]. 2019 [citado 28/09/2020];33(3):[aprox. 18 p.]. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-21412019000300014

  11. Gutiérrez Martínez JA, Febles Estrada A. Las tecnologías disruptivas y su aplicación en la medicina, una visión al 2030. Rev Cubana Salud Publica [Internet]. 2019 [citado 28/09/2020]; 45(4):[aprox. 22 p.]. Disponible en: http://www.revsaludpublica.sld.cu/index.php/spu/article/view/1563/1366

  12. Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. AJR Am J Roentgenol [Internet]. 2020 [citado 28/09/2020];215(1):[aprox. 7 p.]. Disponible en: https://pubmed.ncbi.nlm.nih.gov/32174129/

  13. Kim JY, Choe PG, Oh Y, Oh KJ, Kim J, Park SJ, Park JH, Na HK, Oh MD. The First Case of 2019 Novel Coronavirus Pneumonia Imported into Korea from Wuhan, China: Implication for Infection Prevention and Control Measures. J Korean Med Sci [Internet]. 2020 [citado 28/09/2020];35(5):[aprox. 6 p.]. Disponible en: https://pubmed.ncbi.nlm.nih.gov/32030925/

  14. Pan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T, et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): A study of 63 patients in Wuhan, China.

  15. Eur Radiol [Internet]. 2020 [citado 28/09/20];30(6):[aprox. 3 p.]. Disponible en: https://pubmed.ncbi.nlm.nih.gov/32055945/

  16. Durrani M, Inam-ul-Haq, Kalsoom U, Yousaf A. Chest X-rays fidings in COVID 19 patients at a University Teaching Hospital - Adescriptive study. Pak J Med Sci [Internet]. 2020 [citado 20/09/2020];36:[aprox. 16 p.]. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306947/

  17. Ippolito D, Pecorelli A, Maino C, Capodaglio C, Mariani I ,Giandola T , et al. Diagnostic impact of bedside chest X-ray features of 2019 novel coronavirus in the routine admission at the emergency department: case series from Lombardy region. Eur J of Radiol [Internet]. 2020 [citado 21/09/2020];129:[aprox. 36 p.]. Disponible en: https://www.ejradiology.com/article/S0720-048X%2820%2930281-3/fulltext

  18. Ministerio de Salud Pública de Cuba. Protocolo de Actuación Nacional para la COVID-19. Versión 1.6. [Internet]. La Habana: Minsap; 2021. Disponible en: https://files.sld.cu/editorhome/files/2021/03/VERSION_FINAL_6_EXTENDIDA_PROTOCOLO_REVISADA_28_MARZO__2021.pdf

  19. Espinosa Brito A. Reflexiones a propósito de la pandemia de COVID-19 [I]: del 18 de marzo al 2 de abril de 2020. Rev Anales Academia de Ciencias de Cuba [Internet]. 2020 [citado 12/09/2020];10(2):[aprox. 35 p.]. Disponible en: http://www.revistaccuba.sld.cu/index.php/revacc/article/view/765/797

  20. Tran BX, Vu GT, Ha GH, Vuong QH, Ho MT, Vuong TT, et al. Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study. J Clin Med [Internet]. 2019 [citado 02/09/020];8(3):[aprox. 2 p.]. Disponible en: https://pubmed.ncbi.nlm.nih.gov/30875745/

  21. Farhat H, Sakr GE, Kilany R. Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. Mach Vis Appl [Internet]. 2020 [citado 14/09/2020];31(6):[aprox. 53 p.]. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386599/

  22. Wang S, Wang T, Yang L, Yang DM, Fujimoto J, Yi F et al. Conv Path: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. E Bio Medicine [Internet]. 2019 [citado 14/09/2020];50:[aprox. 8 p.]. Disponible en: https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(19)30703-0/fulltext

  23. Cai L, Gao J, Zhao D. A review of the application of deep learning in medical image classification and segmentation. Ann Transl Med [Internet]. 2020 [citado 14/09/2020];8(11):[aprox. 26 p.]. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7327346/

  24. Mazurowski MA, Buda M, Saha A, Bashir MR. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging [Internet]. 2019 [citado 24/09/2020];49(4):[aprox. 15 p.]. Disponible en: https://pubmed.ncbi.nlm.nih.gov/30575178/

  25. Li L, Qin L, Zeguo X, Yin Y, Wang X, Kong B, et al. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology [Internet]. 2020 [citado 24/09/2020];296(2):[aprox. 9 p.]. Disponible en: https://pubs.rsna.org/doi/10.1148/radiol.2020200905

  26. Shuai Wang, Bo Kang, JinluMa, Xianjun Zeng, Mingming Xiao, Jia Guo, et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). Med Rxiv [Internet]. 2020 [citado 24/09/2020];31(8):[aprox. 9 p.]. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904034/

  27. Murphy K, Smits H, Knoops AJG, Korst MBJM, Samson T, Scholten ET, Schalekamp S, Schaefer-Prokop CM, Philipsen RHHM, Meijers A, Melendez J, van Ginneken B, Rutten M. COVID-19 on Chest Radiographs. A Multireader Evaluation of an Artificial Intelligence System. Radiology [Internet]. 2020 [citado 04/09/2020];296(3):[aprox. 16 p.]. Disponible en: https://www.researchgate.net/publication/341257615_COVID-19_on_the_Chest_Radiograph_A_Multi-Reader_Evaluation_of_an_AI_System

  28. Chen J, Wu L, Zhang J, Zhang L, Gong D, Zhao Y, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia onhigh-resolution computed tomography: a prospective study. Med Rxiv [Internet]. 2020 [citado 24/09/2020];2:[aprox. 27 p.]. Disponible en: https://www.medrxiv.org/content/10.1101/2020.02.25.20021568v2.full.pdf




2020     |     www.medigraphic.com