Supported projects

D202005 – Automated learning for characterization in flow imaging of markers of therapeutic efficacy in patients with sickle cell disease treated by gene therapy

Scientific leader
Dr. Pierre Buffet

The current development of gene therapy in sickle cell disease requires the use of innovative tools to assess treatment efficacy. Our drepAMNIS project proposes to use artificial intelligence (Machine Learning) to allow rapid and reliable quantification of markers of treatment efficacy already validated (irreversible sickle cells – ISC) as well as the development of innovative markers (markers of splenic function, percentage of F-cells). The quantification of these markers before and after gene therapy will allow a rapid evaluation of the treatment efficacy, and therefore better management of sickle cell patients.



Team 4 BioTiGR UMR_S1134

INTS (Institut National de Transfusion Sanguine)

6 rue Alexandre Cabanel

75015 Paris