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Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to a...

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Detalles Bibliográficos
Autores principales: El Achi, Hanadi, Khoury, Joseph D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226574/
https://www.ncbi.nlm.nih.gov/pubmed/32224980
http://dx.doi.org/10.3390/cancers12040797
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author El Achi, Hanadi
Khoury, Joseph D.
author_facet El Achi, Hanadi
Khoury, Joseph D.
author_sort El Achi, Hanadi
collection PubMed
description Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.
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spelling pubmed-72265742020-05-18 Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology El Achi, Hanadi Khoury, Joseph D. Cancers (Basel) Review Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases. MDPI 2020-03-26 /pmc/articles/PMC7226574/ /pubmed/32224980 http://dx.doi.org/10.3390/cancers12040797 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
El Achi, Hanadi
Khoury, Joseph D.
Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology
title Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology
title_full Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology
title_fullStr Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology
title_full_unstemmed Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology
title_short Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology
title_sort artificial intelligence and digital microscopy applications in diagnostic hematopathology
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226574/
https://www.ncbi.nlm.nih.gov/pubmed/32224980
http://dx.doi.org/10.3390/cancers12040797
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