Yong Pi1, Zhen Zhao2, Pei Yang2, Junjun Cheng2, Lisha Jiang2, Jianan Wei1, Xiaolei Chen3, Huawei Cai4, Zhang Yi5. 1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, China. 2. Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No.37 Guo Xue Alley, Chengdu, 610041, China. 3. Department of Nephrology, West China Hospital, Sichuan University, Chengdu, 610041, China. 4. Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No.37 Guo Xue Alley, Chengdu, 610041, China. hw.cai@yahoo.com. 5. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, China. zhangyi@scu.edu.cn.
Abstract
OBJECTIVES: To develop and evaluate an artificial intelligence (AI) system that can automatically calculate the glomerular filtration rate (GFR) from dynamic renal imaging without manually delineating the regions of interest (ROIs) of kidneys and the corresponding background. METHODS: This study was a single-center retrospective analysis of the data of 14,634 patients who underwent 99mTc-DTPA dynamic renal imaging. Two systems based on convolutional neural networks (CNN) were developed and evaluated: sGFRa predicts the radioactive counts of ROIs and calculates GFR using the Gates equation and sGFRb directly predicts GFR from dynamic renal imaging without using other information. The root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 were used to evaluate the performance of our approach. RESULTS: sGFRa achieved an RMSE of 5.05, MAE of 4.03, MAPE of 6.07%, and R2 of 0.93 for total GFR while sGFRb achieved an RMSE of 7.61, MAE of 5.92, MAPE of 8.92%, and R2 of 0.85 for total GFR. The accuracy of sGFRa and sGFRb in determining the stage of chronic kidney disease was 87.41% and 82.44%, respectively. CONCLUSIONS: The findings of sGFRa show that automatic GFR calculation based on CNN and using dynamic renal imaging is feasible and efficient and, additionally, can aid clinical diagnosis. Furthermore, the promising results of sGFRb demonstrate that CNN can predict GFR from dynamic renal imaging without additional information. KEY POINTS: • Our CNN-based AI systems can automatically calculate GFR from dynamic renal imaging without manually delineating the ROIs of kidneys and the corresponding background. • sGFRa accurately predicted the radioactive counts of ROIs and calculated GFR using the Gates method. • sGFRb-predicted GFR directly without any parameters related to the Gates equation.
OBJECTIVES: To develop and evaluate an artificial intelligence (AI) system that can automatically calculate the glomerular filtration rate (GFR) from dynamic renal imaging without manually delineating the regions of interest (ROIs) of kidneys and the corresponding background. METHODS: This study was a single-center retrospective analysis of the data of 14,634 patients who underwent 99mTc-DTPA dynamic renal imaging. Two systems based on convolutional neural networks (CNN) were developed and evaluated: sGFRa predicts the radioactive counts of ROIs and calculates GFR using the Gates equation and sGFRb directly predicts GFR from dynamic renal imaging without using other information. The root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 were used to evaluate the performance of our approach. RESULTS: sGFRa achieved an RMSE of 5.05, MAE of 4.03, MAPE of 6.07%, and R2 of 0.93 for total GFR while sGFRb achieved an RMSE of 7.61, MAE of 5.92, MAPE of 8.92%, and R2 of 0.85 for total GFR. The accuracy of sGFRa and sGFRb in determining the stage of chronic kidney disease was 87.41% and 82.44%, respectively. CONCLUSIONS: The findings of sGFRa show that automatic GFR calculation based on CNN and using dynamic renal imaging is feasible and efficient and, additionally, can aid clinical diagnosis. Furthermore, the promising results of sGFRb demonstrate that CNN can predict GFR from dynamic renal imaging without additional information. KEY POINTS: • Our CNN-based AI systems can automatically calculate GFR from dynamic renal imaging without manually delineating the ROIs of kidneys and the corresponding background. • sGFRa accurately predicted the radioactive counts of ROIs and calculated GFR using the Gates method. • sGFRb-predicted GFR directly without any parameters related to the Gates equation.
Authors: M Donald Blaufox; Diego De Palma; Andrew Taylor; Zsolt Szabo; Alain Prigent; Martin Samal; Yi Li; Andrea Santos; Giorgio Testanera; Mark Tulchinsky Journal: Eur J Nucl Med Mol Imaging Date: 2018-08-30 Impact factor: 9.236
Authors: Pauli Virtanen; Ralf Gommers; Travis E Oliphant; Matt Haberland; Tyler Reddy; David Cournapeau; Evgeni Burovski; Pearu Peterson; Warren Weckesser; Jonathan Bright; Stéfan J van der Walt; Matthew Brett; Joshua Wilson; K Jarrod Millman; Nikolay Mayorov; Andrew R J Nelson; Eric Jones; Robert Kern; Eric Larson; C J Carey; İlhan Polat; Yu Feng; Eric W Moore; Jake VanderPlas; Denis Laxalde; Josef Perktold; Robert Cimrman; Ian Henriksen; E A Quintero; Charles R Harris; Anne M Archibald; Antônio H Ribeiro; Fabian Pedregosa; Paul van Mulbregt Journal: Nat Methods Date: 2020-02-03 Impact factor: 28.547