title: Performance and blood monitoring in sports: the artificial intelligence evoking target testing in antidoping (AR.I.E.T.T.A.) project.
abstract: OBJECTIVE
  Substances and methods used to increase oxygen blood transport and physical performance can be detected in the blood, but the screening of the athletes to be tested remains a critical issue for the International Federations. This project, AR.I.E.T.T.A., aimed to develop a software capable of analysing athletes' hematological and performance profiles to detect abnormal patterns.
  
  
  METHODS
  One-hundred eighty athletes belonging to the International Biathlon Union gave written informed consent to have their hematological data, previously collected according to anti-doping rules, used to develop the AR.I.E.T.T.A. software.
  
  
  RESULTS
  Software was developed with the included sections: 1) log-in; 2) data-entry: where data are loaded, stored and grouped; 3) analysis: where data are analysed, validated scores are calculated, and parameters are simultaneously displayed as statistics, tables and graphs, and individual or subpopulation profiles; 4) screening: where an immediate evaluation of the risk score of the present sample and/or the athlete under study is obtained. The sample risk score or AR.I.E.T.T.A. score is calculated by a simple computational system combining different parameters (absolute values and intra-individual variations) considered concurrently. The AR.I.E.T.T.A. score is obtained by the sum of the deviation units derived from each parameter, considering the shift of the present value from the reference values, based on the number of standard deviations.
  
  
  CONCLUSIONS
  AR.I.E.T.T.A. enables a quick evaluation of blood results assisting surveillance programs and perform timely target testing controls on athletes by the International Federations. Future studies aiming to validate the AR.I.E.T.T.A. score and improve the diagnostic accuracy will improve the system.
authors.affiliation: International Biathlon Union, Salzburg, Austria. mdf@unife.it
authors.forename: A F
authors.initials: AF
authors.lastname: Manfredini
authors.forename: A M
authors.initials: AM
authors.lastname: Malagoni
authors.forename: H
authors.initials: H
authors.lastname: Litmanen
authors.forename: L
authors.initials: L
authors.lastname: Zhukovskaja
authors.forename: P
authors.initials: P
authors.lastname: Jeannier
authors.forename: D
authors.initials: D
authors.lastname: Dal Follo
authors.forename: M
authors.initials: M
authors.lastname: Felisatti
authors.forename: A
authors.initials: A
authors.lastname: Besseberg
authors.forename: M
authors.initials: M
authors.lastname: Geistlinger
authors.forename: P
authors.initials: P
authors.lastname: Bayer
authors.forename: J E
authors.initials: JE
authors.lastname: Carrabre
mesh_terms: D001185:Artificial Intelligence; D054874:Athletic Performance / Q000502:physiology* / Q000706:statistics & numerical data*; D004300:Doping in Sports / Q000517:prevention & control*; D005260:Female; D006403:Hematologic Tests / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D012984:Software; D013177:Sports; D013647:Task Performance and Analysis