  The efficiency of deep machine learning for automatic delineation of tumor
areas has been demonstrated for intraoperative neuronavigation using active
IR-mapping with the use of the cold test. The proposed approach employs a
matrix IR-imager to remotely register the space-time distribution of surface
temperature pattern, which is determined by the dynamics of local cerebral
blood flow. The advantages of this technique are non-invasiveness, zero risks
for the health of patients and medical staff, low implementation and
operational costs, ease and speed of use. Traditional IR-diagnostic technique
has a crucial limitation - it involves a diagnostician who determines the
boundaries of tumor areas, which gives rise to considerable uncertainty, which
can lead to diagnosis errors that are difficult to control. The current study
demonstrates that implementing deep learning algorithms allows to eliminate the
explained drawback.
