Inteligencia artificial en el diagnóstico histopatológico de microorganismos
Artificial intelligence in the histopathological diagnosis of microorganisms
Barra lateral del artículo
PDF
FLIP
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Contenido principal del artículo
Resumen
Introducción: la mayoría de las aplicaciones en patología digital se encuentran relacionadas con la oncológica, aunque se han propuesto algunos modelos recientes que permiten evaluar la utilidad en el diagnóstico histológico de microorganismos. Material y métodos: se realizó la siguiente revisión en la que se incluyeron 10 artículos publicados en inglés, que tienen como eje central el diagnóstico histopatológico de microorganismos y diferentes modelos de inteligencia artificial. Discusión: los diseñados se han probado para el diagnóstico de Helicobacter pylori, Mycobacterium tuberculosis, Aspergillus, Mucorales y microorganismos relacionados con onicomicosis. Conclusiones: se recomienda el uso de la inteligencia artificial en el diagnóstico histopatológico de microorganismos como un campo emergente que refuerza la función del patólogo coordinador de los diferentes modelos, optimizando así su función y mejorando los tiempos de trabajo y los niveles de efectividad.
Palabras clave:
Descargas
Detalles del artículo
Referencias
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.
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
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
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.
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
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
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323(6088):533-536.
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.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44. http://dx.doi.org/10.1038/nature14539
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
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
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
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.
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.
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.
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
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.
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.
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.
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
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
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
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
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
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
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.
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
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.
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
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
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
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
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
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
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.
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.
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.
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.
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
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
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
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
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
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
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
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
Zhong Z, Wang X, Li J, Zhang B, Yan L, Xu S, et al. A study on the diagnosis of the Helicobacter pylori coccoid form with artificial intelligence technology. Front Microbiol. 2022;13:1008346. http://dx.doi.org/10.3389/fmicb.2022.1008346
Technical Guidance Group of the Fifth National TB Epidemiological Survey, The Office of the Fifth National TB Epidemiolological Survey. The fifth national tuberculosis epidemiological survey in 2010. Chinese Journal of Antituberculosis 2012;34:485-508.
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
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
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
Sua LF, Bolaños JE, Maya J, Sánchez A, Medina G, Zúñiga-Restrepo V, et al. Detection of mycobacteria in paraffin-embedded Ziehl-Neelsen-Stained tissues using digital pathology. Tuberculosis (Edinb). 2021;126(102025):102025. https://doi.org/10.1016/j.tube.2020.102025
Guarner J, Brandt ME. Histopathologic diagnosis of fungal infections in the 21st century. Clin Microbiol Rev. 2011;24(2):247–80. http://dx.doi.org/10.1128/CMR.00053-10
Okubo Y, Ishiwatari T, Izumi H, Sato F, Aki K, Sasai D, et al. Pathophysiological implication of reversed CT halo sign in invasive pulmonary mucormycosis: a rare case report. Diagn Pathol. 2013;8(1):82. http://dx.doi.org/10.1186/1746-1596-8-82
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. Disponible en: http://dx.doi.org/10.3314/mmj.22-00013
Velasquez-Agudelo V, Cardona-Arias JA. Meta-analysis of the utility of culture, biopsy, and direct KOH examination for the diagnosis of onychomycosis. BMC Infect Dis. 2017;17(1). http://dx.doi.org/10.1186/s12879-017-2258-3
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