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Artificial intelligence-based triage of large bowel biopsies can improve workflow

BACKGROUND: Large bowel biopsies are one of the commonest types of biopsy specimen. We describe a service evaluation study to test the feasibility of using artificial intelligence (AI) to triage large bowel biopsies from a reporting backlog and prioritize those that require more urgent reporting. ME...

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Autores principales: Mayall, Frederick George, Goodhead, Mark David, de Mendonça, Louis, Brownlie, Sarah Eleanor, Anees, Azka, Perring, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852686/
https://www.ncbi.nlm.nih.gov/pubmed/36687528
http://dx.doi.org/10.1016/j.jpi.2022.100181
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author Mayall, Frederick George
Goodhead, Mark David
de Mendonça, Louis
Brownlie, Sarah Eleanor
Anees, Azka
Perring, Stephen
author_facet Mayall, Frederick George
Goodhead, Mark David
de Mendonça, Louis
Brownlie, Sarah Eleanor
Anees, Azka
Perring, Stephen
author_sort Mayall, Frederick George
collection PubMed
description BACKGROUND: Large bowel biopsies are one of the commonest types of biopsy specimen. We describe a service evaluation study to test the feasibility of using artificial intelligence (AI) to triage large bowel biopsies from a reporting backlog and prioritize those that require more urgent reporting. METHODS: The pathway was developed in the UK by National Health Service (NHS) laboratory staff working in a medium-sized general hospital.   The AI platform was interfaced with the slide scanner software and the reporting platform’s software, so that pathologists could correct the AI label and reinforce the training set as they reported the cases. RESULTS: he AI classifier achieved a sensitivity of 97.56% and specificity of 93.02% for the case-level-diagnosis of neoplasia (adenoma and adenocarcinoma) and for an AI diagnosis of any significant pathology (i.e., adenomas, adenocarcinomas, inflammation, hyperplastic polyps, and sessile serrated lesions) sensitivity was 95.65% and specificity 92.96%. The automated AI diagnostic classification pathway took approximately 175 s per slide to download and process the scanned whole slide image (WSI) and return an AI diagnostic classification. Biopsies with an AI diagnosis of neoplasia or inflammation were prioritized for reporting while the remainder followed the routine reporting pathway. The AI triaged pathway resulted in a significantly shorter reporting turnaround time for pathologist verified neoplastic cases (P < 0.001) and inflammation (P < 0.05). The project’s costs amounted to  £14800, excluding laboratory staff salaries. More time and resources were spent on developing the interface between the AI platform and laboratory IT systems than on the development of the AI platform itself. CONCLUSIONS: NHS laboratory staff were able to implement an AI solution to accurately triage large bowel biopsies into several diagnostic classes and this improved reporting turnaround times for cases with neoplasia or with inflammation.
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spelling pubmed-98526862023-01-21 Artificial intelligence-based triage of large bowel biopsies can improve workflow Mayall, Frederick George Goodhead, Mark David de Mendonça, Louis Brownlie, Sarah Eleanor Anees, Azka Perring, Stephen J Pathol Inform Original Research Article BACKGROUND: Large bowel biopsies are one of the commonest types of biopsy specimen. We describe a service evaluation study to test the feasibility of using artificial intelligence (AI) to triage large bowel biopsies from a reporting backlog and prioritize those that require more urgent reporting. METHODS: The pathway was developed in the UK by National Health Service (NHS) laboratory staff working in a medium-sized general hospital.   The AI platform was interfaced with the slide scanner software and the reporting platform’s software, so that pathologists could correct the AI label and reinforce the training set as they reported the cases. RESULTS: he AI classifier achieved a sensitivity of 97.56% and specificity of 93.02% for the case-level-diagnosis of neoplasia (adenoma and adenocarcinoma) and for an AI diagnosis of any significant pathology (i.e., adenomas, adenocarcinomas, inflammation, hyperplastic polyps, and sessile serrated lesions) sensitivity was 95.65% and specificity 92.96%. The automated AI diagnostic classification pathway took approximately 175 s per slide to download and process the scanned whole slide image (WSI) and return an AI diagnostic classification. Biopsies with an AI diagnosis of neoplasia or inflammation were prioritized for reporting while the remainder followed the routine reporting pathway. The AI triaged pathway resulted in a significantly shorter reporting turnaround time for pathologist verified neoplastic cases (P < 0.001) and inflammation (P < 0.05). The project’s costs amounted to  £14800, excluding laboratory staff salaries. More time and resources were spent on developing the interface between the AI platform and laboratory IT systems than on the development of the AI platform itself. CONCLUSIONS: NHS laboratory staff were able to implement an AI solution to accurately triage large bowel biopsies into several diagnostic classes and this improved reporting turnaround times for cases with neoplasia or with inflammation. Elsevier 2023-01-02 /pmc/articles/PMC9852686/ /pubmed/36687528 http://dx.doi.org/10.1016/j.jpi.2022.100181 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Original Research Article
Mayall, Frederick George
Goodhead, Mark David
de Mendonça, Louis
Brownlie, Sarah Eleanor
Anees, Azka
Perring, Stephen
Artificial intelligence-based triage of large bowel biopsies can improve workflow
title Artificial intelligence-based triage of large bowel biopsies can improve workflow
title_full Artificial intelligence-based triage of large bowel biopsies can improve workflow
title_fullStr Artificial intelligence-based triage of large bowel biopsies can improve workflow
title_full_unstemmed Artificial intelligence-based triage of large bowel biopsies can improve workflow
title_short Artificial intelligence-based triage of large bowel biopsies can improve workflow
title_sort artificial intelligence-based triage of large bowel biopsies can improve workflow
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852686/
https://www.ncbi.nlm.nih.gov/pubmed/36687528
http://dx.doi.org/10.1016/j.jpi.2022.100181
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