Back to Search
Start Over
Fully Automated Longitudinal Assessment of Renal Stone Burden on Serial CT Imaging Using Deep Learning.
- Source :
-
Journal of endourology [J Endourol] 2023 Aug; Vol. 37 (8), pp. 948-955. Date of Electronic Publication: 2023 Jun 30. - Publication Year :
- 2023
-
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 mm <superscript>3</superscript> , -10 mm <superscript>3</superscript> , 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.
Details
- Language :
- English
- ISSN :
- 1557-900X
- Volume :
- 37
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- Journal of endourology
- Publication Type :
- Academic Journal
- Accession number :
- 37310890
- Full Text :
- https://doi.org/10.1089/end.2023.0066