To develop a deep learning (DL) model for generating automated regions of interest (ROIs) on 99mTc-diethylenetriamine pentaacetic acid (DTPA) renal scans for glomerular filtration rate (GFR) measurement. Manually-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. A 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. Our DL model exhibited excellent performance in the generation of ROIs on 99mTc-DTPA renal scans. This automated approach could potentially reduce manual effort and enhance the precision of GFR measurement in clinical practice.

中文翻译：

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基于深度学习的 99mTc-二乙烯三胺五乙酸肾扫描测量分离肾小球滤过率


开发深度学习 (DL) 模型，用于在 99mTc-二乙烯三胺五乙酸 (DTPA) 肾脏扫描上生成自动感兴趣区域 (ROI)，以测量肾小球滤过率 (GFR)。从图片存档和通信系统中检索的手动绘制的 ROI 用作地面实况 (GT) 标签。训练具有多通道输入的二维 U-Net 卷积神经网络架构来生成 DL ROI。使用 Lin 一致性相关系数 (CCC) 和线性回归分析的斜率系数评估 GT 和 DL ROI 的 GFR 值之间的一致性。使用 Bland-Altman 图评估偏差和 95% 一致性限 (LOA)。总共包含 24,364 次扫描（12,822 名患者）。左侧（CCC 0.982，95% 置信区间 [CI] 0.981–0.982；斜率 1.004，95% CI 1.003–1.004）、右侧（CCC 0.969，95% CI 0.968–0.969；斜率）GT 和 DL GFR 之间具有极好的一致性0.954，95% CI 0.953–0.955）和双肾（CCC 0.978，95% CI 0.978–0.979；斜率 0.979，95% CI 0.978–0.979）。 Bland-Altman 分析显示 GT 和 DL GFR 之间的偏差最小，平均差异为 − 0.2 (95% LOA − 4.4–4.0)、1.4 (95% LOA − 3.5–6.3) 和 1.2 (95% LOA − 6.5–8.8)左肾、右肾和双肾分别为 mL/min/1.73 m²。值得注意的是，19,960 次扫描 (81.9%) 显示 GFR 的绝对差异小于 5 mL/min/1.73 m²。我们的 DL 模型在 99mTc-DTPA 肾脏扫描的 ROI 生成中表现出出色的性能。这种自动化方法可能会减少手动工作并提高临床实践中 GFR 测量的精度。