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High‐throughput whole‐slide scanning to enable large‐scale data repository building
Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost‐effective manner....
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9327504/ https://www.ncbi.nlm.nih.gov/pubmed/35511469 http://dx.doi.org/10.1002/path.5923 |
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author | Zarella, Mark D Rivera Alvarez, Keysabelis |
author_facet | Zarella, Mark D Rivera Alvarez, Keysabelis |
author_sort | Zarella, Mark D |
collection | PubMed |
description | Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost‐effective manner. Technical innovation in whole‐slide imaging has enabled high‐throughput slide scanning through the coordinated increase in scanner capacity, speed, and automation. Combining these hardware innovations with automated informatics approaches has enabled more efficient workflows and the opportunity to provide higher‐quality imaging data using fewer personnel. Here we review several practical considerations for deploying high‐throughput scanning and we present strategies to increase efficiency with a focus on quality. Finally, we review remaining challenges and issue a call to vendors to innovate in the areas of automation and quality control in order to make high‐throughput scanning realizable to laboratories with limited resources. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. |
format | Online Article Text |
id | pubmed-9327504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley & Sons, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-93275042022-07-30 High‐throughput whole‐slide scanning to enable large‐scale data repository building Zarella, Mark D Rivera Alvarez, Keysabelis J Pathol Invited Reviews Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost‐effective manner. Technical innovation in whole‐slide imaging has enabled high‐throughput slide scanning through the coordinated increase in scanner capacity, speed, and automation. Combining these hardware innovations with automated informatics approaches has enabled more efficient workflows and the opportunity to provide higher‐quality imaging data using fewer personnel. Here we review several practical considerations for deploying high‐throughput scanning and we present strategies to increase efficiency with a focus on quality. Finally, we review remaining challenges and issue a call to vendors to innovate in the areas of automation and quality control in order to make high‐throughput scanning realizable to laboratories with limited resources. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. John Wiley & Sons, Ltd 2022-06-08 2022-07 /pmc/articles/PMC9327504/ /pubmed/35511469 http://dx.doi.org/10.1002/path.5923 Text en © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Invited Reviews Zarella, Mark D Rivera Alvarez, Keysabelis High‐throughput whole‐slide scanning to enable large‐scale data repository building |
title | High‐throughput whole‐slide scanning to enable large‐scale data repository building |
title_full | High‐throughput whole‐slide scanning to enable large‐scale data repository building |
title_fullStr | High‐throughput whole‐slide scanning to enable large‐scale data repository building |
title_full_unstemmed | High‐throughput whole‐slide scanning to enable large‐scale data repository building |
title_short | High‐throughput whole‐slide scanning to enable large‐scale data repository building |
title_sort | high‐throughput whole‐slide scanning to enable large‐scale data repository building |
topic | Invited Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9327504/ https://www.ncbi.nlm.nih.gov/pubmed/35511469 http://dx.doi.org/10.1002/path.5923 |
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