“Artificial Intelligence Evoking Target Testing in Antidoping” (AR.I.E.T.T.A.)

Different substances and methods can be used to increase the oxygen carrying capacity of blood, thereby improving an athlete's ability to perform. Doping control procedures are expensive and the problem always exists of who we should test, by what criteria and when. Research groups have been developing criteria to detect these substances and methods (blood doping, human recombinant erythropoietin, oxygen carriers, the off/on model) International federations, including Biathlon, currently choose athletes based on random selection, standings, high hemoglobin and/or hematocrit and/or reticulocyte counts, off model scores, etc. There is currently no accurate integrated way to combine all variables (individual performance change and laboratory values), to estimate which athletes should be selected at the optimal time for anti-doping tests.This project aims to develop an intelligent system which is able to identify those athletes whose haematological and multiple variables reflect a pattern consistent with the use of banned substances or methods. These athletes could then be chosen at the optimal time for target testing. The focus of this project is the creation of a software program that will consider haematological values abnormal not only on the basis of high values, but also on the basis of raw data considered concurrently (haematological data in relation to the reference population, intraindividual haematological variations including abnormal low data, performance variations, ranking, nation). This system will produce classes of results associated to a diagnostic probability, useful for targeted selection for both in and out of competition controls. The system aims to be fast (analysing multiple data simultaneously), unpredictable and self-learning (the new informations will be automatically included to improve the knowledge). The project aims to provide a strong deterrent against doping, reducing the risk of evasion by manipulation, and to be cost-effective, ensuring that anti-doping budgets are spent in an evidence based fashion.