SDG Studio collaborates together with Esaote, FlairBit and Akronos in the UTMOST project, a project whose objective is the possibility of using Machine Learning algorithms for the early diagnosis of sarcopenia, i.e. the normal reduction in muscle mass due to aging.
SDG Studio mainly deals with the processing of data coming from dedicated hardware (ultrasound) in order to send them to supervised Machine Learning algorithms, in order to train the algorithm with certain data (supervised by specialized personnel) and be able to subsequently use it in predictive mode on real cases.
SDG Studio mainly deals with the processing of data coming from dedicated hardware (ultrasound) in order to send them to supervised Machine Learning algorithms, in order to train the algorithm with certain data (supervised by specialized personnel) and be able to subsequently use it in predictive mode on real cases.
