A deep learning workflow for quantification of micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation.
Savash Ishanzadeh, Munuse C.
Solaiman, Nadeen Shaikh
Murphy, John J.
AffiliationIndian Institute of Technology Bhilai
University of Westminster
University of Bedfordshire
Subject Categories::G700 Artificial Intelligence
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AbstractThe 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.
CitationPanchbhai A, Savash Ishanzadeh MC, Sidali A, Solaiman N, Pankanti S, Kanagaraj R, Murphy JJ, Surendranath K (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)
SponsorsWork of the Genome Engineering laboratory is supported by the Quintin Hogg Trust through “Gene Editors of the Future programme” and the external donor fund of Raj Sitlani acquired through the development team (Simay Sali Sevik and Jordan Scamell) of the University of Westminster.
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