A deep learning workflow for quantification of Micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation

Anand Panchbhai, Munuse Ceyda Ishanzadeh, Smarana Pankanti, Ahmed Sidali, Nadeeen Solaiman, Radhakrishnan Kanagaraj, John J. Murphy and Kalpana Surendranath 2023. A deep learning workflow for quantification of Micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation. Computer Methods and Programs in Biomedicine. 232 107447. https://doi.org/10.1016/j.cmpb.2023.107447

TitleA deep learning workflow for quantification of Micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation
TypeJournal article
AuthorsAnand Panchbhai, Munuse Ceyda Ishanzadeh, Smarana Pankanti, Ahmed Sidali, Nadeeen Solaiman, Radhakrishnan Kanagaraj, John J. Murphy and Kalpana Surendranath
Abstract

The cytokinesis block micronucleus assay is widely used for measuring/scoring/counting micronuclei, a marker of genome instability in cultured and primary cells. Though a gold standard method, this is a laborious and time-consuming process with person-to-person variation observed in quantification of micronuclei. We report in this study the utilisation of a new deep learning workflow for detection of micronuclei in DAPI stained nuclear images. The proposed deep learning framework achieved an average precision of >90% in detection of micronuclei. This proof of principle investigation in a DNA damage studies laboratory supports the idea of deploying AI powered tools in a cost-effective manner for repetitive and laborious tasks with relevant computational expertise. These systems will also help improving the quality of data and wellbeing of researchers.

KeywordsMN
genome instability
DAPI
cancer diagnostics
MN detection
Artificial Intelligence
Article number107447
JournalComputer Methods and Programs in Biomedicine
Journal citation232
ISSN0169-2607
1872-7565
Year2023
PublisherElsevier
Accepted author manuscript
License
CC BY-NC-ND 4.0
File Access Level
Open (open metadata and files)
Publisher's version
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cmpb.2023.107447
PubMed ID36889248
Web address (URL)https://doi.org/10.1016/j.cmpb.2023.107447
Publication dates
Published online26 Feb 2023
Published in printApr 2023

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