How Reliable Is Endoscopic Stone Recognition? A Comparison Between Visual Stone Identification and Formal Stone Analysis

Henderickx, M.M.E.L.; Stoots, Simone J M; Bruin, Daniël M. de; Wijkstra, Hessel; Freund, Jan Erik; Wiseman, Oliver; Ploumidis, Achilles; Skolarikos, Andreas; Somani, Bhaskar K.; Şener, Tarık Emre; Emiliani, Esteban; Dragoş, L.; Villa, Luca; Talso, Michele; Daudon, Michel; Traxer, Olivier; Kronenberg, Peter; Doizi, Steeve; Tailly, Thomas; Tefik, Tzevat; Hendriks, Nora; Beerlage, Harrie P.; Baard, Joyce; Kamphuis, Guido

doi:10.1089/end.2022.0217

Cited by 4 publications

(2 citation statements)

References 32 publications

(55 reference statements)

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“…However, it is worth noting that while stones are typically described as a single or mixed mineral type, in fact, they contain many organic substances, primarily proteins. The effect that these may have on collapse mechanisms, their variability between stones described as being of the same mineral component, and their spatial orientation leading to stone heterogeneity will all undoubtedly play a significant role in stone fragmentation [ 59 – 61 ]. Those of the A-series (weddellite) show a dominance of wild avalanches with similar characteristics to other brittle materials.…”

Section: Discussionmentioning

confidence: 99%

Acoustic emission of kidney stones: a medical adaptation of statistical breakdown mechanisms

Eckstein,

Wiseman,

Carpenter

et al. 2024

Urolithiasis

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Kidney stones have a prevalence rate of > 10% in some countries. There has been a significant increase in surgery to treat kidney stones over the last 10 years, and it is crucial that such techniques are as effective as possible, while limiting complications. A selection of kidney stones with different chemical and structural properties were subjected to compression. Under compression, they emit acoustic signals called crackling noise. The variability of the crackling noise was surprisingly great comparing weddellite, cystine and uric acid stones. Two types of signals were found in all stones. At high energies of the emitted sound waves, we found avalanche behaviour, while all stones also showed signals of local, uncorrelated collapse. These two types of events are called ‘wild’ for avalanches and ‘mild’ for uncorrelated events. The key observation is that the crossover from mild to wild collapse events differs greatly between different stones. Weddellite showed brittle collapse, extremely low crossover energies (< 5 aJ) and wild avalanches over 6 orders of magnitude. In cystine and uric acid stones, the collapse was more complicated with a dominance of local “mild” breakings, although they all contained some stress-induced collective avalanches. Cystine stones had high crossover energies, typically $$\sim$$ ∼ 750 aJ, and a narrow window over which they showed wild avalanches. Uric acid stones gave moderate values of crossover energies, $$\sim$$ ∼ 200 aJ, and wild avalanche behaviour for $$\sim$$ ∼ 3 orders of magnitude. Further research extended to all stone types, and measurement of stone responses to different lithotripsy strategies, will assist in optimisation of settings of the laser and other lithotripsy devices to insight fragmentation by targeting the ‘wild’ avalanche regime.

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Section: Discussionmentioning

confidence: 99%

Acoustic emission of kidney stones: a medical adaptation of statistical breakdown mechanisms

Eckstein,

Wiseman,

Carpenter

et al. 2024

Urolithiasis

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“…This issue may hamper the clinical adoption of ESR (Estrade et al 2017(Estrade et al , 2021. Besides, it has been recently shown that the diagnostic accuracy of endoscopic stone recognition with insufficiently trained endourologists is limited by poor intra-observer agreement (Henderickx et al 2022).…”

Section: Introductionmentioning

confidence: 99%

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

Estrade¹,

Daudon²,

Richard³

et al. 2022

Phys. Med. Biol.

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Objective: To 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. Approach: 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. Using dedicated artificial intelligence (AI) networks, the proposed pipeline selects adequate frames in steady sequences of the video, ensures the presence of (potentially fragmented) stones and predicts the stone morphologies on a frame-by-frame basis. The automatic endoscopic stone recognition (A-ESR) is subsequently carried out by mixing all collected morphological observations. Main results: 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. The gold standard ESR was provided by a trained endo-urologist and confirmed by microscopy/infra-red spectroscopy. For the AI-training, 585 static images were collected (349 and 236 observations of stone surface and section, respectively) and used. Using the proposed video classifier, 71 digital endoscopic videos were analyzed: 50 exhibited only one morphological type and 21 displayed two. Taken together, both pure and mixed stone types yielded a mean diagnostic performances as follows: balanced accuracy=[88 ± 6] (min=81) %, sensitivity=[80 ± 13] (min=69) %, specificity=[95 ± 2] (min=92) %, precision=[78 ± 12] (min=62) % and F1-score=[78 ± 7] (min=69) %. Significance: These 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 the selection of adequate steady frames.

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Advancements in stone classification: unveiling the beauty of urolithiasis

De Coninck,

Skolarikos,

Juliebø-Jones

et al. 2024

World J Urol

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How Reliable Is Endoscopic Stone Recognition? A Comparison Between Visual Stone Identification and Formal Stone Analysis

Cited by 4 publications

References 32 publications

Acoustic emission of kidney stones: a medical adaptation of statistical breakdown mechanisms

Acoustic emission of kidney stones: a medical adaptation of statistical breakdown mechanisms

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

Advancements in stone classification: unveiling the beauty of urolithiasis

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