ESTRADE, Vincent; DENIS DE SENNEVILLE, Baudouin; FACQ, Laurent; DAUDON, Michel

doi:10.5802/crchim.162

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ESTRADE, Vincent
CHU de Bordeaux Pellegrin [Bordeaux]

DENIS DE SENNEVILLE, Baudouin
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]

FACQ, Laurent
Institut de Mathématiques de Bordeaux [IMB]

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Comptes Rendus. Chimie. 2022-11, vol. 25, n° S1, p. 407-416

Académie des sciences (Paris)

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Examination of stone morphologies shows a great potential for the etiological diagnosis of stone disease. Endoscopic Stone Recognition (ESR) can thereby provide essential morpho-constitutional analysis of stones in-situ, and becomes a method of choice for an effective management of patients with kidney stones. Here, we show that both visual and automatic ESR can be performed within a LASER-induced spraying session. ESR may therefore be beneficial to still apply an etiological approach in lithiasis. We discuss the added value of Artificial Intelligence in the entire patient care process. Prospects and requirements for widespread applications of ESR in a clinical practice are evaluated.< Réduire

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Endoscopic in-situ recognition of urinary stones during LASER-induced stone fragmentation: a modern, effective and essential approach in the diagnostic process in urolithiasis

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