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Deep regression using 99mTc-DTPA dynamic renal imaging for automatic calculation of the glomerular filtration rate

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

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.

• sGFR a accurately predicted the radioactive counts of ROIs and calculated GFR using the Gates method.

• sGFR b -predicted GFR directly without any parameters related to the Gates equation.

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Abbreviations

AI :

Artificial intelligence

CKD :

Chronic kidney disease

CNN :

Convolutional neural network

DICOM :

Digital imaging and communications in medicine

GFR :

Glomerular filtration rate

MAE :

Mean absolute error

MAPE :

Mean absolute percentage error

RMSE :

Root-mean-square error

ROIs :

Regions of interest

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Funding

This study has received funding by the National Major Science and Technology Projects of China under Grant 2018AAA0100201, Sichuan Science and Technology Program under Grant 2020JDRC0042, and 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYGD18016&2021HXFH033).

Author information

Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, China

    Yong Pi, Jianan Wei & Zhang Yi

  2. Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No.37 Guo Xue Alley, Chengdu, 610041, China

    Zhen Zhao, Pei Yang, Junjun Cheng, Lisha Jiang & Huawei Cai

  3. Department of Nephrology, West China Hospital, Sichuan University, Chengdu, 610041, China

    Xiaolei Chen

Authors
  1. Yong Pi
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  2. Zhen Zhao
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  3. Pei Yang
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  4. Junjun Cheng
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  5. Lisha Jiang
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  6. Jianan Wei
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  7. Xiaolei Chen
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  8. Huawei Cai
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  9. Zhang Yi
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Corresponding authors

Correspondence to Huawei Cai or Zhang Yi.

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Guarantor

The scientific guarantor of this publication is Huawei Cai.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Pi, Y., Zhao, Z., Yang, P. et al. Deep regression using 99mTc-DTPA dynamic renal imaging for automatic calculation of the glomerular filtration rate. Eur Radiol 33, 34–42 (2023). https://doi.org/10.1007/s00330-022-08970-6

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  • DOI: https://doi.org/10.1007/s00330-022-08970-6

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