Inteligencia artificial en el diagnóstico histopatológico de microorganismos

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spelling	Inteligencia artificial en el diagnóstico histopatológico de microorganismos Parra Medina, Rafael Artículo de revista 3 33 Revista Repertorio de Medicina y Cirugía González Coba , Andrea Sociedad de Cirugía de Bogotá, Hospital de San José y Fundación Universitaria de Ciencias de la Salud Gomez López, Arley Quintero, Lina María Polo Nieto, Fernando Mosquera-Zamudio, Andrés Victoria Caro, María Romero Fandiño, Ivan Alberto Sprockel Díaz, John Jaime Journal article Ford AC, Yuan Y, Forman D, Hunt R, Moayyedi P. Helicobacter pylori eradication for the prevention of gastric neoplasia. Cochrane Database Syst Rev. 2020;7:CD005583. http://dx.doi.org/10.1002/14651858.CD005583.pub3 Shi W, Georgiou P, Akram A, Proute MC, Serhiyenia T, Kerolos ME, et al. Diagnostic Pitfalls of Digital Microscopy Versus Light Microscopy in Gastrointestinal Pathology: A Systematic Review. Cureus. 2021;13(8):e17116. http://dx.doi.org/10.7759/cureus.17116 Mohan BP, Khan SR, Kassab LL, Ponnada S, Mohy-Ud-Din N, Chandan S, et al. Convolutional neural networks in the computer-aided diagnosis of Helicobacter pylori infection and non-causal comparison to physician endoscopists: a systematic review with meta-analysis. Ann Gastroenterol. 2021;34(1):20-5. http://dx.doi.org/10.20524/aog.2020.0542 Huang CR, Chung PC, Sheu BS, Kuo HJ, Popper M. Helicobacter Pylori-Related Gastric Histology Classification Using Support-Vector-Machine-Based Feature Selection. IEEE Transactions on Information Technology in Biomedicine. 2008;12(4):523-31. http://dx.doi.org/10.1109/TITB.2007.913128 Delgado-Ortet M, Molina A, Alférez S, Rodellar J, Merino A. A deep learning approach for segmentation of red blood cell images and malaria detection. Entropy (Basel). 2020;22(6):657. http://dx.doi.org/10.3390/e22060657. Hu R-S, Hesham AE-L, Zou Q. Machine learning and its applications for protozoal pathogens and protozoal infectious diseases. Front Cell Infect Microbiol. 2022;12:882995. http://dx.doi.org/10.3389/fcimb.2022.882995. Sulyok M, Luibrand J, Strohäker J, Karacsonyi P, Frauenfeld L, Makky A, et al. Implementing deep learning models for the classification of Echinococcus multilocularis infection in human liver tissue. Parasit Vectors. 2023;16(1):29. http://dx.doi.org/10.1186/s13071-022-05640-w. Franklin MM, Schultz FA, Tafoya MA, Kerwin AA, Broehm CJ, Fischer EG, et al. A deep learning convolutional neural network can differentiate between Helicobacter pylori gastritis and autoimmune gastritis with results comparable to gastrointestinal pathologists. Arch Pathol Lab Med. 2022;146(1):117–22. http://dx.doi.org/10.5858/arpa.2020-0520-OA. Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet. 2019;393(10181):1577–9. http://dx.doi.org/10.1016/s0140-6736(19)30037-6 Mongan J, Moy L, Kahn CE Jr. Checklist for artificial intelligence in medical imaging (CLAIM): A guide for authors and reviewers. Radiol Artif Intell. 2020;2(2): e200029. http://dx.doi.org/10.1148/ryai.2020200029 Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. http://dx.doi.org/10.1186/s12916-019-1426-2 Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689. http://dx.doi.org/10.1136/bmj.m689 Kleppe A, Skrede O-J, De Raedt S, Liestøl K, Kerr DJ, Danielsen HE. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer. 2021;21(3):199–211. http://dx.doi.org/10.1038/s41568-020-00327-9 Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018;115(13):E2970–9. http://dx.doi.org/10.1073/pnas.1717139115 Hou L, Samaras D, Kurc TM, Gao Y, Davis JE, Saltz JH. Patch-based convolutional neural network for whole slide tissue image classification. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2016; 2016:2424–33. http://dx.doi.org/10.1109/CVPR.2016.266 Ocampo P, Moreira A, Coudray N, Sakellaropoulos T, Narula N, Snuderl M, et al. P1.09-32 classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. J Thorac Oncol. 2018;13(10):S562. http://dx.doi.org/10.1016/j.jtho.2018.08.808. Koohbanani NA, Jahanifar M, Tajadin NZ, Rajpoot N. NuClick: A deep learning framework for interactive segmentation of microscopy images [Internet]. arXiv [cs.CV]. 2020 [citado el 25 de abril de 2023]. Disponible en: http://arxiv.org/abs/2005.14511 Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N. AggNet: Deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging. 2016;35(5):1313–21. http://dx.doi.org/10.1109/TMI.2016.2528120 Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301–9. http://dx.doi.org/10.1038/s41591-019-0508-1 Song Z, Zou S, Zhou W, Huang Y, Shao L, Yuan J, et al. Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning. Nat Commun. 2020;11(1):4294. http://dx.doi.org/10.1038/s41467-020-18147-8 Nagpal K, Foote D, Liu Y, Chen P-HC, Wulczyn E, Tan F, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med. 2019;2(1):48. http://dx.doi.org/10.1038/s41746-019-0112-2 Veta M, Heng YJ, Stathonikos N, Bejnordi BE, Beca F, Wollmann T, et al. Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Med Image Anal. 2019;54:111–21. http://dx.doi.org/10.1016/j.media.2019.02.012 Pinckaers H, Litjens G. Neural Ordinary Differential Equations for semantic segmentation of individual colon glands [Internet]. arXiv [eess. IV]. 2019. Disponible en: http://arxiv.org/abs/1910.10470 Qaiser T, Pugh M, Margielewska S, Hollows R, Murray P, Rajpoot N. Digital tumor-collagen proximity signature predicts survival in diffuse large B-cell lymphoma. In: Digital Pathology. Cham: Springer International Publishing; 2019. p. 163–71. Yang M, Nurzynska K, Walts AE, Gertych A. A CNN-based active learning framework to identify mycobacteria in digitized Ziehl-Neelsen stained human tissues. Comput Med Imaging Graph. 2020;84(101752):101752. http://dx.doi.org/10.1016/j.compmedimag.2020.101752 Klein S, Gildenblat J, Ihle MA, Merkelbach-Bruse S, Noh K-W, Peifer M, et al. Deep learning for sensitive detection of Helicobacter Pylori in gastric biopsies. BMC Gastroenterol. 2020;20(1):417. http://dx.doi.org/10.1186/s12876-020-01494-7 https://revistas.fucsalud.edu.co/index.php/repertorio/article/download/1585/2742 Text http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/publishedVersion http://purl.org/redcol/resource_type/ARTREV http://purl.org/coar/resource_type/c_dcae04bc http://purl.org/coar/resource_type/c_6501 info:eu-repo/semantics/article https://revistas.fucsalud.edu.co/index.php/repertorio/article/download/1585/2777 https://revistas.fucsalud.edu.co/index.php/repertorio/article/download/1585/2764 Gonçalves WGE, Santos MHPD, Brito LM, Palheta HGA, Lobato FMF, Demachki S, et al. DeepHP: A new gastric mucosa histopathology dataset for Helicobacter pylori infection diagnosis. Int J Mol Sci. 2022;23(23):14581. http://dx.doi.org/10.3390/ijms232314581 https://revistas.fucsalud.edu.co/index.php/repertorio/article/download/1585/2725 https://revistas.fucsalud.edu.co/index.php/repertorio/article/download/1585/2546 Jansen P, Creosteanu A, Matyas V, Dilling A, Pina A, Saggini A, et al. Deep learning assisted diagnosis of onychomycosis on whole-slide images. J Fungi (Basel). 2022;8(9):912. http://dx.doi.org/10.3390/jof8090912 Tochigi N, Sadamoto S, Oura S, Kurose Y, Miyazaki Y, Shibuya K. Artificial intelligence in the diagnosis of invasive mold infection: Development of an automated histologic identification system to distinguish between Aspergillus and Mucorales. Med Mycol J. 2022;63(4):91–7. http://dx.doi.org/10.3314/mmj.22-00013 Sua LF, Bolaños JE, Maya J, Sánchez A, Medina G, Zúñiga-Restrepo V, et al. Detection of mycobacteria in paraffinembedded Ziehl-Neelsen-Stained tissues using digital pathology. Tuberculosis (Edinb). 2021;126(102025):102025. https://doi.org/10.1016/j.tube.2020.102025 Pantanowitz L, Wu U, Seigh L, LoPresti E, Yeh F-C, Salgia P, et al. Artificial intelligence-based screening for mycobacteria in whole-slide images of tissue samples. Am J Clin Pathol. 2021;156(1):117–28. http://dx.doi.org/10.1093/ajcp/aqaa215 Litjens G, Bandi P, Ehteshami Bejnordi B, Geessink O, Balkenhol M, Bult P, et al. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. Gigascience. 2018;7(6):giy065. http://dx.doi.org/10.1093/gigascience/giy065. Xiong Y, Ba X, Hou A, Zhang K, Chen L, Li T. Automatic detection of mycobacterium tuberculosis using artificial intelligence. J Thorac Dis. 2018;10(3):1936-1940. http://dx.doi.org/10.21037/jtd.2018.01.91 Franklin MM, Schultz FA, Tafoya MA, Kerwin AA, Broehm CJ, Fischer EG, et al. A deep learning convolutional neural network can differentiate between Helicobacter pylori gastritis and autoimmune gastritis with results comparable to gastrointestinal pathologists. Arch Pathol Lab Med. 2022;146(1):117–22. http://dx.doi.org/10.5858/arpa.2020-0520-OA Gonçalves WGE, Dos Santos MH de P, Lobato FMF, Ribeiro-Dos-Santos Â, de Araújo GS. Deep learning in gastric tissue diseases: a systematic review. BMJ Open Gastroenterol. 2020;7(1): e000371. http://dx.doi.org/10.1136/bmjgast-2019-000371 Liu Y, Gadepalli K, Norouzi M, Dahl GE, Kohlberger T, Boyko A, et al. Detecting cancer metastases on gigapixel pathology images. arXiv [cs.CV]. 2017 [citado el 25 de abril de 2023]. Disponible en: http://arxiv.org/abs/1703.02442. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44. http://dx.doi.org/10.1038/nature14539 Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):411–8.http://dx.doi.org/10.1007/978-3-642-40763-5_51. diagnosis application/epub+zip text/html application/pdf 2024-09-20 2024-09-20 21:27:03 2024-09-20 21:27:03 artificial intelligence fungi deep learning tissue algorithms text/xml mycobacterium tuberculosis helicobacter pylori inteligencia artificial hongos aprendizaje profundo tejido diagnóstico algoritmos mycobacterium tuberculosis helicobacter pylori audio/mpeg 0121-7372 Ciresan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J. Flexible, High Performance Convolutional Neural Networks for Image Classification [Internet]. Idsia.ch. [citado el 25 de abril de 2023]. Disponible en: https://people.idsia.ch/~juergen/ijcai2011.pdf Luchini C, Pantanowitz L, Adsay V, Asa SL, Antonini P, Girolami I, et al. Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring. Mod Pathol. 2022;35(6):712–20. http://dx.doi.org/10.1038/s41379-022-01055-1. Kather JN, Weis C-A, Bianconi F, Melchers SM, Schad LR, Gaiser T, et al. Multi-class texture analysis in colorectal cancer histology. Sci Rep. 2016;6:27988. http://dx.doi.org/10.1038/srep27988. Prewitt JM, Mendelsohn ML. The analysis of cell images. Ann N Y Acad Sci. 1966;128(3):1035–53. http://dx.doi.org/10.1111/j.1749-6632.1965.tb11715.x. van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med. 2021;27(5):775–84. http://dx.doi.org/10.1038/s41591-021-01343-4. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. 2 de diciembre de 2015 [citado 11 de diciembre de 2018]; Disponible en: https://arxiv.org/abs/1512.00567 Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. http://dx.doi.org/10.1038/nature21056 Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402-10. http://dx.doi.org/10.1001/jama.2016.17216 Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. En: Pereira F, Burges CJ, Bottou L, Weinberger KQ, editores. Advances in Neural Information Processing Systems [Internet]. Curran Associates, Inc.; 2012. p.1-9. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323(6088):533-536. Kuok C-P, Horng M-H, Liao Y-M, Chow N-H, Sun Y-N. An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks. Microsc Res Tech. 2019;82(6):709–19. http://dx.doi.org/10.1002/jemt.23217 Lin E, Fuda F, Luu HS, Cox AM, Fang F, Feng J, et al. Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology. Semin Diagn Pathol. 2023;40(2):88–94. http://dx.doi.org/10.1053/j.semdp.2023.02.001 Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20(5):e253–61. http://dx.doi.org/10.1016/s1470-2045(19)30154-8 2462-991X Smith KP, Kirby JE. Image analysis and artificial intelligence in infectious disease diagnostics. Clin Microbiol Infect. 2020;26(10):1318–23. http://dx.doi.org/10.1016/j.cmi.2020.03.012 College of American Pathologists. What is pathology? [Internet]. 2019 [citado el 25 de abril de 2023]. Disponible en: https://www.cap.org/member-resources/articles/what-is-pathology. 237 230 Revista Repertorio de Medicina y Cirugía - 2020 https://creativecommons.org/licenses/by-nc-sa/4.0/ https://doi.org/10.31260/RepertMedCir.01217372.1585 10.31260/RepertMedCir.01217372.1585 https://revistas.fucsalud.edu.co/index.php/repertorio/article/view/1585 Publication
institution	FUNDACIÓN UNIVERSITARIA DE CIENCIA DE LA SALUD
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country_str	Colombia
collection	Revista Repertorio de Medicina y Cirugía
title	Inteligencia artificial en el diagnóstico histopatológico de microorganismos
spellingShingle	Inteligencia artificial en el diagnóstico histopatológico de microorganismos Parra Medina, Rafael González Coba , Andrea Gomez López, Arley Quintero, Lina María Polo Nieto, Fernando Mosquera-Zamudio, Andrés Victoria Caro, María Romero Fandiño, Ivan Alberto Sprockel Díaz, John Jaime diagnosis artificial intelligence fungi deep learning tissue algorithms mycobacterium tuberculosis helicobacter pylori inteligencia artificial hongos aprendizaje profundo tejido diagnóstico algoritmos mycobacterium tuberculosis helicobacter pylori
title_short	Inteligencia artificial en el diagnóstico histopatológico de microorganismos
title_full	Inteligencia artificial en el diagnóstico histopatológico de microorganismos
title_fullStr	Inteligencia artificial en el diagnóstico histopatológico de microorganismos
title_full_unstemmed	Inteligencia artificial en el diagnóstico histopatológico de microorganismos
title_sort	inteligencia artificial en el diagnóstico histopatológico de microorganismos
author	Parra Medina, Rafael González Coba , Andrea Gomez López, Arley Quintero, Lina María Polo Nieto, Fernando Mosquera-Zamudio, Andrés Victoria Caro, María Romero Fandiño, Ivan Alberto Sprockel Díaz, John Jaime
author_facet	Parra Medina, Rafael González Coba , Andrea Gomez López, Arley Quintero, Lina María Polo Nieto, Fernando Mosquera-Zamudio, Andrés Victoria Caro, María Romero Fandiño, Ivan Alberto Sprockel Díaz, John Jaime
topic	diagnosis artificial intelligence fungi deep learning tissue algorithms mycobacterium tuberculosis helicobacter pylori inteligencia artificial hongos aprendizaje profundo tejido diagnóstico algoritmos mycobacterium tuberculosis helicobacter pylori
topic_facet	diagnosis artificial intelligence fungi deep learning tissue algorithms mycobacterium tuberculosis helicobacter pylori inteligencia artificial hongos aprendizaje profundo tejido diagnóstico algoritmos mycobacterium tuberculosis helicobacter pylori
citationvolume	33
citationissue	3
publisher	Sociedad de Cirugía de Bogotá, Hospital de San José y Fundación Universitaria de Ciencias de la Salud
ispartofjournal	Revista Repertorio de Medicina y Cirugía
source	https://revistas.fucsalud.edu.co/index.php/repertorio/article/view/1585
language
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publishDate	2024-09-20
date_accessioned	2024-09-20 21:27:03
date_available	2024-09-20 21:27:03
url	https://revistas.fucsalud.edu.co/index.php/repertorio/article/view/1585
url_doi	https://doi.org/10.31260/RepertMedCir.01217372.1585
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eissn	2462-991X
doi	10.31260/RepertMedCir.01217372.1585
citationstartpage	230
citationendpage	237
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