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Analytical Data Review on an Artificial Intelligence Platform for Doping Control in Horse Racing.

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Artificial intelligence (AI) can classify horse doping control chromatograms as positive or negative for prohibited substances (PS). This AI model achieved over 90% accuracy, improving efficiency but facing challenges in analysis time and workflow flexibility.

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Area of Science:

  • Analytical Chemistry
  • Forensic Science
  • Biotechnology

Background:

  • Doping control in horse racing generates vast numbers of chromatograms from GC/MS and LC/MS analyses.
  • Manual review of these chromatograms for prohibited substances (PS) is labor-intensive and time-consuming.
  • Artificial Intelligence (AI) offers potential for automating image-based data analysis, including chromatograms.

Purpose of the Study:

  • To explore the feasibility of using AI for the initial analysis of chromatograms in horse doping control.
  • To develop and evaluate an AI model for classifying chromatograms as positive (POS) or negative (NEG) for PS.
  • To assess the potential of AI to enhance the efficiency and accuracy of doping control data vetting.

Main Methods:

  • Developed a predictive model using Alteryx Designer's image recognition tool.
  • Trained the model on over 6000 manually classified chromatograms (POS/NEG).
  • Evaluated model accuracy on approximately 700 manually classified chromatograms, achieving over 90% prediction accuracy.

Main Results:

  • The AI model accurately identified suspicious/positive and negative chromatograms with no false negatives across two screening methods.
  • The model demonstrated high accuracy in classifying chromatograms from LC/MS analysis of horse urine.
  • Application to screening methods covering over 300 drug targets showed reliable performance.

Conclusions:

  • AI holds significant potential for improving the efficiency of initial chromatogram analysis in horse doping control.
  • The developed AI model demonstrates high accuracy and no false negatives, aiding in faster data vetting.
  • Challenges remain in reducing analysis time and increasing workflow flexibility for widespread implementation in routine testing.