ArticleEJNMMI physics2024
Deep learning-based measurement of split glomerular filtration rate with
Article in EJNMMI physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo develop a deep learning (DL) model for generating automated regions of interest (ROIs) on
methodsManually-drawn ROIs retrieved from a Picture Archiving and Communications System were used as ground-truth (GT) labels. A two-dimensional U-Net convolutional neural network architecture with multichannel input was trained to generate DL ROIs. The agreement between GFR values from GT and DL ROIs was evaluated using Lin's concordance correlation coefficient (CCC) and slope coefficients for linear regression analyses. Bias and 95% limits of agreement (LOA) were assessed using Bland-Altman plots.
resultsA total of 24,364 scans (12,822 patients) were included. Excellent concordance between GT and DL GFR was found for left (CCC 0.982, 95% confidence interval [CI] 0.981-0.982; slope 1.004, 95% CI 1.003-1.004), right (CCC 0.969, 95% CI 0.968-0.969; slope 0.954, 95% CI 0.953-0.955) and both kidneys (CCC 0.978, 95% CI 0.978-0.979; slope 0.979, 95% CI 0.978-0.979). Bland-Altman analysis revealed minimal bias between GT and DL GFR, with mean differences of - 0.2 (95% LOA - 4.4-4.0), 1.4 (95% LOA - 3.5-6.3) and 1.2 (95% LOA - 6.5-8.8) mL/min/1.73 m² for left, right and both kidneys, respectively. Notably, 19,960 scans (81.9%) showed an absolute difference in GFR of less than 5 mL/min/1.73 m².
conclusionOur DL model exhibited excellent performance in the generation of ROIs on
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