No Cover Image

Journal article 72 views

Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring

Eloise Smith, Jade Wagman, Claire Barnes Orcid Logo, Paul Rees Orcid Logo, George Johnson Orcid Logo

Mutagenesis

Swansea University Authors: Eloise Smith, Jade Wagman, Claire Barnes Orcid Logo, Paul Rees Orcid Logo, George Johnson Orcid Logo

Full text not available from this repository: check for access using links below.

Check full text

DOI (Published version): 10.1093/mutage/geaf026

Abstract

Micronuclei (MN) are critical biomarkers for pathological conditions, yet their manual scoring is inherently laborious and prone to significant interobserver variability, limiting the reliability and scalability of genotoxicity assessments. Recent advancements in deep learning and computer vision ha...

Full description

Published in: Mutagenesis
ISSN: 0267-8357 1464-3804
Published: Oxford University Press (OUP) 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa70891
first_indexed 2025-11-13T15:49:47Z
last_indexed 2025-11-14T12:47:50Z
id cronfa70891
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>70891</id><entry>2025-11-13</entry><title>Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring</title><swanseaauthors><author><sid>c64053c032feb7f8611df76b956a3a1e</sid><firstname>Eloise</firstname><surname>Smith</surname><name>Eloise Smith</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>623a454cd68c9ae546db107255445a2d</sid><firstname>Jade</firstname><surname>Wagman</surname><name>Jade Wagman</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>024232879fc13d5ceac584360af8742c</sid><ORCID>0000-0003-1031-7127</ORCID><firstname>Claire</firstname><surname>Barnes</surname><name>Claire Barnes</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>537a2fe031a796a3bde99679ee8c24f5</sid><ORCID>0000-0002-7715-6914</ORCID><firstname>Paul</firstname><surname>Rees</surname><name>Paul Rees</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>37d0f121db69fd09f364df89e4405e31</sid><ORCID>0000-0001-5643-9942</ORCID><firstname>George</firstname><surname>Johnson</surname><name>George Johnson</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-11-13</date><abstract>Micronuclei (MN) are critical biomarkers for pathological conditions, yet their manual scoring is inherently laborious and prone to significant interobserver variability, limiting the reliability and scalability of genotoxicity assessments. Recent advancements in deep learning and computer vision have revolutionized automated MN detection in various assay samples, enhancing accuracy and efficiency and reducing human bias. While these artificial intelligence (AI)-powered techniques have been demonstrated in in vitro genotoxicity testing, their application to the minimally invasive buccal micronucleus cytome (BMCyt) assay for human biomonitoring remains largely unexplored. The BMCyt assay, invaluable for assessing genotoxic damage in environmentally exposed populations, presents unique challenges, including sample variability, confounding factors, and the complexity of scoring multiple cytogenetic endpoints. This review covers the evolution of AI-based MN detection, analysing key methodologies and advancements. It highlights the untapped potential of integrating AI into the BMCyt assay to overcome current analytical limitations, improve reproducibility, increase throughput, and eliminate observer bias. By facilitating more robust and scalable genomic damage monitoring, AI integration will significantly enhance the utility of the BMCyt assay in large-scale epidemiological studies and human biomonitoring.</abstract><type>Journal Article</type><journal>Mutagenesis</journal><volume>0</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>Oxford University Press (OUP)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0267-8357</issnPrint><issnElectronic>1464-3804</issnElectronic><keywords>deep learning, object detection, micronuclei, buccal cell, human biomonitoring, cytome assay</keywords><publishedDay>14</publishedDay><publishedMonth>11</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-11-14</publishedDate><doi>10.1093/mutage/geaf026</doi><url/><notes>Review</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>Swansea University</funders><projectreference/><lastEdited>2025-12-15T15:57:28.1990465</lastEdited><Created>2025-11-13T15:47:50.5986450</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Biomedical Science</level></path><authors><author><firstname>Eloise</firstname><surname>Smith</surname><order>1</order></author><author><firstname>Jade</firstname><surname>Wagman</surname><order>2</order></author><author><firstname>Claire</firstname><surname>Barnes</surname><orcid>0000-0003-1031-7127</orcid><order>3</order></author><author><firstname>Paul</firstname><surname>Rees</surname><orcid>0000-0002-7715-6914</orcid><order>4</order></author><author><firstname>George</firstname><surname>Johnson</surname><orcid>0000-0001-5643-9942</orcid><order>5</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling v2 70891 2025-11-13 Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring c64053c032feb7f8611df76b956a3a1e Eloise Smith Eloise Smith true false 623a454cd68c9ae546db107255445a2d Jade Wagman Jade Wagman true false 024232879fc13d5ceac584360af8742c 0000-0003-1031-7127 Claire Barnes Claire Barnes true false 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 37d0f121db69fd09f364df89e4405e31 0000-0001-5643-9942 George Johnson George Johnson true false 2025-11-13 Micronuclei (MN) are critical biomarkers for pathological conditions, yet their manual scoring is inherently laborious and prone to significant interobserver variability, limiting the reliability and scalability of genotoxicity assessments. Recent advancements in deep learning and computer vision have revolutionized automated MN detection in various assay samples, enhancing accuracy and efficiency and reducing human bias. While these artificial intelligence (AI)-powered techniques have been demonstrated in in vitro genotoxicity testing, their application to the minimally invasive buccal micronucleus cytome (BMCyt) assay for human biomonitoring remains largely unexplored. The BMCyt assay, invaluable for assessing genotoxic damage in environmentally exposed populations, presents unique challenges, including sample variability, confounding factors, and the complexity of scoring multiple cytogenetic endpoints. This review covers the evolution of AI-based MN detection, analysing key methodologies and advancements. It highlights the untapped potential of integrating AI into the BMCyt assay to overcome current analytical limitations, improve reproducibility, increase throughput, and eliminate observer bias. By facilitating more robust and scalable genomic damage monitoring, AI integration will significantly enhance the utility of the BMCyt assay in large-scale epidemiological studies and human biomonitoring. Journal Article Mutagenesis 0 Oxford University Press (OUP) 0267-8357 1464-3804 deep learning, object detection, micronuclei, buccal cell, human biomonitoring, cytome assay 14 11 2025 2025-11-14 10.1093/mutage/geaf026 Review COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2025-12-15T15:57:28.1990465 2025-11-13T15:47:50.5986450 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Biomedical Science Eloise Smith 1 Jade Wagman 2 Claire Barnes 0000-0003-1031-7127 3 Paul Rees 0000-0002-7715-6914 4 George Johnson 0000-0001-5643-9942 5
title Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring
spellingShingle Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring
Eloise Smith
Jade Wagman
Claire Barnes
Paul Rees
George Johnson
title_short Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring
title_full Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring
title_fullStr Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring
title_full_unstemmed Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring
title_sort Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring
author_id_str_mv c64053c032feb7f8611df76b956a3a1e
623a454cd68c9ae546db107255445a2d
024232879fc13d5ceac584360af8742c
537a2fe031a796a3bde99679ee8c24f5
37d0f121db69fd09f364df89e4405e31
author_id_fullname_str_mv c64053c032feb7f8611df76b956a3a1e_***_Eloise Smith
623a454cd68c9ae546db107255445a2d_***_Jade Wagman
024232879fc13d5ceac584360af8742c_***_Claire Barnes
537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
37d0f121db69fd09f364df89e4405e31_***_George Johnson
author Eloise Smith
Jade Wagman
Claire Barnes
Paul Rees
George Johnson
author2 Eloise Smith
Jade Wagman
Claire Barnes
Paul Rees
George Johnson
format Journal article
container_title Mutagenesis
container_volume 0
publishDate 2025
institution Swansea University
issn 0267-8357
1464-3804
doi_str_mv 10.1093/mutage/geaf026
publisher Oxford University Press (OUP)
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Biomedical Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Biomedical Science
document_store_str 0
active_str 0
description Micronuclei (MN) are critical biomarkers for pathological conditions, yet their manual scoring is inherently laborious and prone to significant interobserver variability, limiting the reliability and scalability of genotoxicity assessments. Recent advancements in deep learning and computer vision have revolutionized automated MN detection in various assay samples, enhancing accuracy and efficiency and reducing human bias. While these artificial intelligence (AI)-powered techniques have been demonstrated in in vitro genotoxicity testing, their application to the minimally invasive buccal micronucleus cytome (BMCyt) assay for human biomonitoring remains largely unexplored. The BMCyt assay, invaluable for assessing genotoxic damage in environmentally exposed populations, presents unique challenges, including sample variability, confounding factors, and the complexity of scoring multiple cytogenetic endpoints. This review covers the evolution of AI-based MN detection, analysing key methodologies and advancements. It highlights the untapped potential of integrating AI into the BMCyt assay to overcome current analytical limitations, improve reproducibility, increase throughput, and eliminate observer bias. By facilitating more robust and scalable genomic damage monitoring, AI integration will significantly enhance the utility of the BMCyt assay in large-scale epidemiological studies and human biomonitoring.
published_date 2025-11-14T15:57:29Z
_version_ 1851590443078254592
score 10.684627

Similar Items