Detection of rEPO administration using deep learning on blood smears

Code: 23D05SV

The aim of this study is to use artificial intelligence (AI) to evaluate blood smears as a potential matrix to detect doping with recombinant EPO (rEPO). Previous publications reported changes in the Red blood Cell Morphology after rEPO administration (e.g. Macrocytes, Stomatocytes). During these times automated haematology analysers and manual microscopy were used for the estimation of these parameters. Recently, digital Morphology is a developing field in Haematology which enables the automated analysis of blood smears by artificial intelligence. With this technology it is possible to evaluate morphological changes with an increased precision, based on a higher number of cells and to discover even minor changes in cellular shapes. AI and deep learning is capable of revealing new insights which conventional approaches were lacking so far, like predicting molecular changes on cytomorphology. Therefore, the goal of this study is to identify relevant changes in cell morphology during rEPO administration which are not addressed using current state of the art techniques. In the long term, a blood smear-based athlete blood passport providing an individual erythrocyte signature might be a prospective application of monitoring athletes by using artificial intelligence based on this postulated deep learning model.