Detection of sample swapping in anti-doping investigations using machine learning.

The substitution of a urine sample that may result in an adverse analytical finding with a previously collected, clean sample is strictly prohibited under the World Anti-Doping Agency (WADA) regulations and is referred to as sample swapping. When an athlete reuses their own clean sample, detection becomes particularly difficult through conventional analytical methods. In this paper, we propose a similarity detection framework that explicitly accounts for pattern complexity in the analysis of urinary steroid profiles. The framework is based on a convolutional network to capture more complex and subtle variations in profile pairs. Using a dataset of 67,651 steroid profiles collected between 2021 and 2023, the framework was evaluated on both synthetic and laboratory-confirmed similar samples, reflecting realistic variability in doping control processes. The results show that the proposed framework outperforms several baseline models, achieving higher accuracy compared to different baselines. These findings demonstrate the potential of machine learning to improve anti-doping workflows by enabling the automated detection of reused or identical urine samples within large-scale sample collection managed by the Athlete Biological Passport.