  The collection and the analysis of kidney stone morphological criteria are
essential for an aetiological diagnosis of stone disease. However, in-situ
LASER-based fragmentation of urinary stones, which is now the most established
chirurgical intervention, may destroy the morphology of the targeted stone. In
the current study, we assess the performance and added value of processing
complete digital endoscopic video sequences for the automatic recognition of
stone morphological features during a standard-of-care intra-operative session.
To this end, a computer-aided video classifier was developed to predict in-situ
the morphology of stone using an intra-operative digital endoscopic video
acquired in a clinical setting.
  The proposed technique was evaluated on pure (i.e. include one morphology)
and mixed (i.e. include at least two morphologies) stones involving "Ia/Calcium
Oxalate Monohydrate (COM)", "IIb/ Calcium Oxalate Dihydrate (COD)" and
"IIIb/Uric Acid (UA)" morphologies. 71 digital endoscopic videos (50 exhibited
only one morphological type and 21 displayed two) were analyzed using the
proposed video classifier (56840 frames processed in total). Using the proposed
approach, diagnostic performances (averaged over both pure and mixed stone
types) were as follows: balanced accuracy=88%, sensitivity=80%,
specificity=95%, precision=78% and F1-score=78%.
  The obtained results demonstrate that AI applied on digital endoscopic video
sequences is a promising tool for collecting morphological information during
the time-course of the stone fragmentation process without resorting to any
human intervention for stone delineation or selection of good quality steady
frames. To this end, irrelevant image information must be removed from the
prediction process at both frame and pixel levels, which is now feasible thanks
to the use of AI-dedicated networks.
