Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos

Autor: Estrade, Vincent, Daudon, Michel, Richard, Emmanuel, Bernhard, Jean-Christophe, Bladou, Franck, Robert, Gregoire, Facq, Laurent, de Senneville, Baudouin Denis
Přispěvatelé: CHU de Bordeaux Pellegrin [Bordeaux], Maladies rénales fréquentes et rares : des mécanismes moléculaires à la médecine personnalisée (CoRaKID), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Biothérapies des maladies génétiques et cancers, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), PlaFRIM (https://plafrim.bordeaux.inria.fr/doku.php), Denis De Senneville, Baudouin
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Physics in Medicine and Biology
Physics in Medicine and Biology, 2022, 67 (16), pp.165006. ⟨10.1088/1361-6560/ac8592⟩
ISSN: 0031-9155
1361-6560
DOI: 10.1088/1361-6560/ac8592⟩
Popis: 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.
16 pages, 4 figures, 3 tables
Databáze: OpenAIRE
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