Ricerc@Sapienza

The GENIUS project aims at developing a machine learning-based model that can be used for the comprehensive and objective evaluation of medical training during the acquisition of skills, based on available data sources (e.g. behavioral data, neurophysiological signals, etc.). In particular, making errors could have important consequences in some operational environments, such as in microsurgery field by using robotic assistance. Robots for assisted surgery (i.e. da Vinci system) are far from being user-friendly and require extensive training. In this regard, it is clear as an effective training procedure and skills maintenance assessment is of high relevance to guarantee patient safety and avoid or at least minimize errors probability. Traditional training assessment methods rely on Instructor (i.e. expert physician) supervision and task performance evaluation of trainees during repeated practice. Nevertheless, although the results of different operators in terms of performance should be the same, cognitive demands could be not (e.g. less mental workload, less allocated attentional resources, improved situation awareness/decision making, etc.). This aspect highlights as nowadays one of the current limitations of the standard training assessment procedures is the lack of objective information regarding the actual amount of employed cognitive resources that could significantly affect expertise assessment during training. The aim of the GENIUS project is to develop a machine learning-based model to arise and track informative training-dependent features and, at the same time, to combine the available information to generate an objective and even real-time "Comprehensive Training Index".
In a long-term vision, the GENIUS model will be used as i) a prediction tool for the Instructor to plan and a better tailoring of medical training, ii) an evaluation instrument to assess surgeon skills maintenance over time.