There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Purpose: Use deep learning (DL) to automate the measurement and tracking of kidney
stone burden over serial CT scans. Materials and Methods: This retrospective study
included 259 scans from 113 symptomatic patients being treated for urolithiasis at
a single medical center between 2006 and 2019. These patients underwent a standard
low-dose noncontrast CT scan followed by ultra-low-dose CT scans limited to the level
of the kidneys. A DL model was used to detect, segment, and measure the volume of
all stones in both initial and follow-up scans. The stone burden was characterized
by the total volume of all stones in a scan (SV). The absolute and relative change
of SV, (SVA and SVR, respectively) over serial scans were computed. The automated
assessments were compared with manual assessments using concordance correlation coefficient
(CCC), and their agreement was visualized using Bland-Altman and scatter plots. Results:
Two hundred twenty-eight out of 233 scans with stones were identified by the automated
pipeline; per-scan sensitivity was 97.8% (95% confidence interval [CI]: 96.0-99.7).
The per-scan positive predictive value was 96.6% (95% CI: 94.4-98.8). The median SV,
SVA, and SVR were 476.5 mm3, -10 mm3, and 0.89, respectively. After removing outliers
outside the 5th and 95th percentiles, the CCC measuring agreement on SV, SVA, and
SVR were 0.995 (0.992-0.996), 0.980 (0.972-0.986), and 0.915 (0.881-0.939), respectively
Conclusions: The automated DL-based measurements showed good agreement with the manual
assessments of the stone burden and its interval change on serial CT scans.
Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.
The last nationally representative assessment of kidney stone prevalence in the United States occurred in 1994. After a 13-yr hiatus, the National Health and Nutrition Examination Survey (NHANES) reinitiated data collection regarding kidney stone history. Describe the current prevalence of stone disease in the United States, and identify factors associated with a history of kidney stones. A cross-sectional analysis of responses to the 2007-2010 NHANES (n=12 110). Self-reported history of kidney stones. Percent prevalence was calculated and multivariable models were used to identify factors associated with a history of kidney stones. The prevalence of kidney stones was 8.8% (95% confidence interval [CI], 8.1-9.5). Among men, the prevalence of stones was 10.6% (95% CI, 9.4-11.9), compared with 7.1% (95% CI, 6.4-7.8) among women. Kidney stones were more common among obese than normal-weight individuals (11.2% [95% CI, 10.0-12.3] compared with 6.1% [95% CI, 4.8-7.4], respectively; p<0.001). Black, non-Hispanic and Hispanic individuals were less likely to report a history of stone disease than were white, non-Hispanic individuals (black, non-Hispanic: odds ratio [OR]: 0.37 [95% CI, 0.28-0.49], p<0.001; Hispanic: OR: 0.60 [95% CI, 0.49-0.73], p<0.001). Obesity and diabetes were strongly associated with a history of kidney stones in multivariable models. The cross-sectional survey design limits causal inference regarding potential risk factors for kidney stones. Kidney stones affect approximately 1 in 11 people in the United States. These data represent a marked increase in stone disease compared with the NHANES III cohort, particularly in black, non-Hispanic and Hispanic individuals. Diet and lifestyle factors likely play an important role in the changing epidemiology of kidney stones. Published by Elsevier B.V.
[1
]Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology
and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland,
USA.
[2
]Department of Radiology, The University of Wisconsin School of Medicine and Public
Health, Madison, Wisconsin, USA.
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.