The combination of mean and maximum Hounsfield Unit allows more accurate prediction of uric acid stones

Qin, Long; Zhou, Jian‐hua; Hu, Wei; Hu, Zhang; Tang, Yunhui; Li, Ming-yong

doi:10.1007/s00240-022-01333-2

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…First, the input variables, including demographic, clinical, and NCCT data, are those readily available in real-world practice, which supports the general applicability of our model. Previously reported stone component classi cation models utilizing imaging data generally require time-consuming manual analysis of HU parameters or additional examination using speci c CT scanner types, such as DECT, which may not be available across all practice settings [8, [17][18][19]. In contrast, our automated model has the potential to be integrated into any electronic medical records system that utilizes coding algorithms to be utilized as a decision support system.…”

Section: Discussionmentioning

confidence: 99%

Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800

Chew,

Wong,

Halawani

et al. 2023

Urolithiasis

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The correct diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical intervention and be offered alkalization therapy. We developed and validated a machine learning (ML)-based model to identify UA stones from non-UA stones. An international, multicenter study was performed on 202 patients who received percutaneous nephrolithotomy for kidney stones with HU < 800. Data from 156 (77.2%) patients were used for model development, while data from 46 (22.8%) patients from a multinational institution were used for external validation. A total of 21,074 kidney and stone contour-annotated computed tomography images were trained with the ResNet-18 Mask R-convolutional neural network algorithm. Finally, this model was concatenated with demographic and clinical data as a fully-connected layer for stone classi cation. Our model was 100% sensitive in detecting kidney stones in each patient, and the delineation of kidney and stone contours was precise within clinically acceptable ranges. The development model provided an accuracy of 99.9%, with 100.0% sensitivity and 98.9% speci city, in distinguishing UA from non-UA stones. On external validation, the model performed with an accuracy of 97.1%, with 89.4% sensitivity and 98.6% speci city. SHAP plots revealed stone density, diabetes mellitus, and urinary pH as the most important features for classi cation. Our ML-based model accurately identi ed and delineated kidney stones and classi ed UA stones from non-UA stones with the highest predictive accuracy reported to date. Our model can be reliably used to select candidates for an earlierdirected alkalization therapy.

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Section: Discussionmentioning

confidence: 99%