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<p>The alkaline comet assay is widely used to assess genotoxicity in biomonitoring studies but lacks standardized scoring system and shows high inter-laboratory variability. Visual scoring is time-consuming, operator-dependent, and limited in sensitivity to low DNA damage levels. We developed a fully automated, open-access scoring model for comet assay analysis to address these limitations. The AIComet model performances were assessed and the model was compared to manual scoring performed in-house on the lymphocytes of 327 healthy volunteers, and compared to visual scoring by 11 trained investigators on open-access images from&#xa0;(Møller et al. <CitationRef CitationID="CR22">2023</CitationRef>). The influence of a larger number of nuclei scored on the sensitivity of the results was evaluated. AIComet showed excellent results for comet classification and detection. Correlation with manual scoring by our laboratory investigator was excellent with a y = 1.02x − 0.155 linear regression equation and 0.92 R<sup>2</sup>. AIComet scores (51.0, 40.2, and 55.0 a.u) on the three datasets consistently fell close to the median values from the 13 investigators (50.4, 41.0, and 61.7 a.u). Regarding its sensitivity, N = 310 nuclei scored are needed to obtain a median relative difference of 10% between the score for N nuclei and the score based on all nuclei available, with a [0.4%–42.1%] 95% confidence interval.</p><p>AIComet is a standardized, reproducible, time-efficient technique for comet assay scoring, reducing operator-dependent variability while improving sensitivity for detecting subtle differences in DNA damage. These findings could be important, primarily in biomonitoring studies with low-exposure settings, but also for reproducing comet assay results overall.</p>

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AIComet: a reliable automated scoring program for DNA damage assessment in the comet assay

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月21日

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  • Adrien Germot,
  • Nicolas Elie,
  • Léonie Ibazizene,
  • Sterenn Guillemot,
  • Poppy Evenden,
  • Peter Møller,
  • Raphaël Delépée,
  • Matthieu Meryet-Figuiere,
  • Pierre Lebailly

摘要

The alkaline comet assay is widely used to assess genotoxicity in biomonitoring studies but lacks standardized scoring system and shows high inter-laboratory variability. Visual scoring is time-consuming, operator-dependent, and limited in sensitivity to low DNA damage levels. We developed a fully automated, open-access scoring model for comet assay analysis to address these limitations. The AIComet model performances were assessed and the model was compared to manual scoring performed in-house on the lymphocytes of 327 healthy volunteers, and compared to visual scoring by 11 trained investigators on open-access images from (Møller et al. 2023). The influence of a larger number of nuclei scored on the sensitivity of the results was evaluated. AIComet showed excellent results for comet classification and detection. Correlation with manual scoring by our laboratory investigator was excellent with a y = 1.02x − 0.155 linear regression equation and 0.92 R2. AIComet scores (51.0, 40.2, and 55.0 a.u) on the three datasets consistently fell close to the median values from the 13 investigators (50.4, 41.0, and 61.7 a.u). Regarding its sensitivity, N = 310 nuclei scored are needed to obtain a median relative difference of 10% between the score for N nuclei and the score based on all nuclei available, with a [0.4%–42.1%] 95% confidence interval.

AIComet is a standardized, reproducible, time-efficient technique for comet assay scoring, reducing operator-dependent variability while improving sensitivity for detecting subtle differences in DNA damage. These findings could be important, primarily in biomonitoring studies with low-exposure settings, but also for reproducing comet assay results overall.