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Artificial intelligence and its applications in digital hematopathology
The advent of whole-slide imaging, faster image data generation, and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples. In parallel, with continuous breakthroughs...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742095/ https://www.ncbi.nlm.nih.gov/pubmed/36518598 http://dx.doi.org/10.1097/BS9.0000000000000130 |
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author | Hu, Yongfei Luo, Yinglun Tang, Guangjue Huang, Yan Kang, Juanjuan Wang, Dong |
author_facet | Hu, Yongfei Luo, Yinglun Tang, Guangjue Huang, Yan Kang, Juanjuan Wang, Dong |
author_sort | Hu, Yongfei |
collection | PubMed |
description | The advent of whole-slide imaging, faster image data generation, and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples. In parallel, with continuous breakthroughs in object detection, image feature extraction, image classification and image segmentation, artificial intelligence (AI) is becoming the most beneficial technology for high-throughput analysis of image data in various biomedical imaging disciplines. Integrating digital images into biological workflows, advanced algorithms, and computer vision techniques expands the biologist’s horizons beyond the microscope slide. Here, we introduce recent developments in AI applied to microscopy in hematopathology. We give an overview of its concepts and present its applications in normal or abnormal hematopoietic cells identification. We discuss how AI shows great potential to push the limits of microscopy and enhance the resolution, signal and information content of acquired data. Its shortcomings are discussed, as well as future directions for the field. |
format | Online Article Text |
id | pubmed-9742095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-97420952022-12-13 Artificial intelligence and its applications in digital hematopathology Hu, Yongfei Luo, Yinglun Tang, Guangjue Huang, Yan Kang, Juanjuan Wang, Dong Blood Sci Special Issue: RNA and Hematopoiesis The advent of whole-slide imaging, faster image data generation, and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples. In parallel, with continuous breakthroughs in object detection, image feature extraction, image classification and image segmentation, artificial intelligence (AI) is becoming the most beneficial technology for high-throughput analysis of image data in various biomedical imaging disciplines. Integrating digital images into biological workflows, advanced algorithms, and computer vision techniques expands the biologist’s horizons beyond the microscope slide. Here, we introduce recent developments in AI applied to microscopy in hematopathology. We give an overview of its concepts and present its applications in normal or abnormal hematopoietic cells identification. We discuss how AI shows great potential to push the limits of microscopy and enhance the resolution, signal and information content of acquired data. Its shortcomings are discussed, as well as future directions for the field. Lippincott Williams & Wilkins 2022-07-14 /pmc/articles/PMC9742095/ /pubmed/36518598 http://dx.doi.org/10.1097/BS9.0000000000000130 Text en Copyright © 2022 The Authors. Published by Wolters Kluwer Health Inc., on behalf of the Chinese Medical Association (CMA) and Institute of Hematology, Chinese Academy of Medical Sciences & Peking Union Medical College (IHCAMS). https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Special Issue: RNA and Hematopoiesis Hu, Yongfei Luo, Yinglun Tang, Guangjue Huang, Yan Kang, Juanjuan Wang, Dong Artificial intelligence and its applications in digital hematopathology |
title | Artificial intelligence and its applications in digital hematopathology |
title_full | Artificial intelligence and its applications in digital hematopathology |
title_fullStr | Artificial intelligence and its applications in digital hematopathology |
title_full_unstemmed | Artificial intelligence and its applications in digital hematopathology |
title_short | Artificial intelligence and its applications in digital hematopathology |
title_sort | artificial intelligence and its applications in digital hematopathology |
topic | Special Issue: RNA and Hematopoiesis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742095/ https://www.ncbi.nlm.nih.gov/pubmed/36518598 http://dx.doi.org/10.1097/BS9.0000000000000130 |
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