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2023, Number 1

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Revista Cubana de Informática Médica 2023; 15 (1)

Impact of Artificial Intelligence in Radiology

Iglesias LD

Language: Spanish
References: 23
Page:
PDF size: 413.52 Kb.


ABSTRACT

Introduction: The growing computational development that has occurred in recent years, as well as the access to a large number of data (Big Data), has made the exploitation of computing resources possible to develop algorithms that increase the quality and scope of artificial intelligence (AI), which is taking a central role in radiology.
Objective: To analyze the impact of artificial intelligence in radiology and the need for its implementation in imaging services.
Method: A total of 23 bibliographical references in English and Spanish, most of them obtained from PubMed, SciELO and ScienceDirect databases, were analyzed using descriptors such as “inteligencia artificial”, “radiología” and “aprendizaje automático” for the Spanish language and "artificial intelligence", “radiology” and “machine learning” for the English language.
Results: There is no area of ​​Radiology in which artificial intelligence has not been implemented in order to improve and develop programs that make it easier for the radiologist and the technician to obtain and diagnose images. Cuba is also immersed in this process; the first steps are being taken towards the development of these technologies.
Conclusions: Research, workflow optimization, radiomics, prediction and classification of images are benefits that AI brings us; achieving an increase in the quality of these processes is only possible through the alliance between medical and computer sciences.


REFERENCES

  1. IBM Cloud Education. What is artificial intelligence (AI)? [Internet]. New York: IBM, c2022 [citado 10 Jul 2022]; [aprox. 14 p.]. Disponible en: https://www.ibm.com/cloud/learn/what-is-artificial-intelligence1.

  2. Pérez del Barrio A, Menéndez Fernández-Miranda P, Sanz Bellón P, Lloret Iglesias L, Rodríguez González D. Inteligencia artificial en Radiología: introducción a los conceptos más importantes. Radiol [Internet]. 2022 [citado 2 Sep 2022]; 64(3):228-236. Disponible en: https://dx.doi.org/10.1016/j.rx.2022.03.0032.

  3. SAS. Machine Learning: Qué es y por qué es importante [Internet]. North Carolina: SAS Institute Inc; c2022 [citado 4 Ago 2022]; [aprox. 7 p.]. Disponible en: Disponible en: https://www.sas.com/es_co/insights/analytics/machine-learning.html 3.

  4. Kelleher J. Deep learning. Massachusetts: The MIT Press; 2019.

  5. Hardy M, Harvey H. Artificial intelligence in diagnostic imaging: impact on the radiography profession. Br J Radiol [Internet]. Mar 2020 [citado 15 Ago 2022];93(1108). Disponible en : https://doi.org/10.1259/bjr.201908405.

  6. Takahashi R, Kajikawa Y. Computer-aided diagnosis: A survey with bibliometric analysis. Int J Med Inf [Internet]. May 2017 [citado 23 Jun 2022]; 101:58-67. Disponible en: https://doi.org/10.1016/j.ijmedinf.2017.02.0046.

  7. Shi F, Wang F, Shi J, Wu Z, Wang Q, Tang Z, et al. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Rev Biomed Eng [Internet]. Abr 2021 [citado 24 Ago 2022]; 14:4-15. Disponible en: https://doi.org/10.1109/RBME.2020.29879757.

  8. Hong Y, Fleming L, Hengyuan M, Ziqi Z, Stefan J, Jinxin L, et al. Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections. J X-Ray Sci Technol [ Internet]. Feb 2021 [citado 7 Sep 2022]; 29(1):1-17. Disponible en: https://doi.org/10.3233/xst-200735

  9. Jin Choi K, Keon Jang J, Soo Lee S, Sub Sung Y, Hyun Shim W, Sung Kim H, et al. Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver. Radiol [Internet]. Sep 2018 [citado 12 Sep 2022]; 289(3). Disponible en: https://doi.org/10.1148/radiol.20181807639.

  10. Beunza Nuin J, Puertas Sanz E, Condés Moreno E. Manual práctico de inteligencia artificial en entornos sanitarios. Barcelona: Elsevier; 2020.

  11. Elton D, Yang A, Kleiner D, Lubner M, Pickhardt P, Veranos R, et al. Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis. Radiol Artif Intell [Internet]. Ago 2022 [citado 22 Sep 2022]; 4(5). Disponible en: https://doi.org/10.1148/ryai.21026811.

  12. Nguyen T, Pérez A, Graffy P, Jang S, Veranos R, Garrett J, et al. Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool. Radiol Artif Intell [Internet]. Ago 2022 [citado 3 Sep 2022]; 4(5). Disponible en: https://doi.org/10.1148/ryai.22004212.

  13. Khosravi B, Rouzrokh P, Maradit Kremers H, Larson D, Johnson Q, Faghani S, et al. Patient-specific Hip Arthroplasty Dislocation Risk Calculator: An Explainable Multimodal Machine Learning-based Approach. Radiol Artif Intell [Internet]. Oct 2022 [citado 10 Oct 2022]; 4(6). Disponible en: https://doi.org/10.1148/ryai.22006713.

  14. Raschio E, Contreras C, Allende F, Maturana P. Inteligencia artificial: Desarrollo de algoritmos de clasificación y segmentación en radiografía de tórax. Rev Chil Radiol [Internet]. Abr 2021 [citado 20 Jul 2022]; 27(1). Disponible en: http://dx.doi.org/10.4067/S0717-9308202100010000814.

  15. Francois Paul J, Rohnean A. Eight clinical cases demonstrating the diagnostic value of ViosWorks 4D powered by Arterys [Internet]. Paris: General Electric Company; 2018 [citado 6 Sep 2022]. Disponible en: Disponible en: https://www.gehealthcare.com/products/magnetic-resonance-imaging/signa-works/-/media/Files/M/MR_GBL_SW_ViosWorks_Clinical_Cases_Brochure_1218_lores.pdf 15.

  16. Seetharam K, Lerakis S. Cardiac magnetic resonance imaging: the future is bright [version 1; peer review: 2 approved]. F1000Res [Internet]. Sep 2019 [citado 15 Sep 2022]; 8:1636. Disponible en: https://doi.org/10.12688%2Ff1000research.19721.116.

  17. Cirio J, Ciardi C, Buezasa M, Dilucab P, Caballeroa M, Lopeza M, et al. Implementación de la inteligencia artificial en el tratamiento hiperagudo de reperfusión arterial en un centro integral de ataque cerebrovascular. Neurol Arg [Internet]. 2021 [citado 3 Nov 2022]; 13(4):212-220. Disponible en: https://doi.org/10.1016/j.neuarg.2021.07.00317.

  18. Zhuo Z, Zhang J, Duan Y, Qu L, Ye C, Liu Y, et al. Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning. Radiol Artif Intell [Internet]. Sep 2022 [citado 12 Oct 2022]; 4(6). Disponible en: https://doi.org/10.1148/ryai.21029218.

  19. Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz C. Deep Learning in Neuroradiology. Am J Neuroradiol [Internet]. 2018 [citado 17 Jul 2022]; 39(10):1776-1784. Disponible en: https://doi.org/10.3174/ajnr.A554319.

  20. Shur J, Doran S, Kumar S, Downey K, O'Connor J, Papanikolaou N, et al. Radiomics in Oncology: A Practical Guide. Radiophys [Internet]. Oct 2021 [citado 18 Oct 2022]; 41(6). Disponible en: https://doi.org/10.1148/rg.202121003720.

  21. Malik N, Geraghty B, Dasgupta A, Jabehdar Maralani P, Sandhu M, Lin Tseng C, et al. MRI radiomics to differentiate between low grade glioma and glioblastoma peritumoral region. J Neuro Oncol [Internet]. Oct 2021 [citado 13 Sep 2022]; 155:181-191. Disponible en: https://doi.org/10.1007/s11060-021-03866-921.

  22. Mulet De los Reyes A, C Suárez, Noriega Alemán M. Herramienta para la detección automática de nódulos pulmonares solitarios en series de imágenes de tomografía computarizada multicorte. Rev Cub Investig Biomed [Internet]. Jun 2020 [citado 15 Jun 2022]; 39(2). Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-0300202000020001922.

  23. Larrondo Pons E. Imagis 2.0, un sistema de información PACS basado en DICOM[Internet]. Santiago de Cuba: Centro de Biofísica Médica; c2021 [citado 20 Dic 2022]; [aprox. 4 p.]. Disponible en:: https://www.cent.uo.edu.cu/cbm/productos/23.




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