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Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera

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Abstract

Objective

Chronic kidney disease (CKD) is evaluated based on glomerular filtration rate (GFR) using a gamma camera in the nuclear medicine center or hospital in a routine procedure, but the gamma camera does not provide the accurate stages of the diseases. Therefore, this research aimed to find out the normal or abnormal stages of CKD based on the value of GFR using an artificial neural network (ANN).

Methods

Two hundred fifty (Training 188, Testing 62) kidney patients who underwent the ultrasonography test to diagnose the renal test in our nuclear medical centre were scanned using gamma camera. The patients were injected with 99mTc-DTPA before the scanning procedure. After pushing the syringe into the patient's vein, the pre-syringe and post syringe radioactive counts were calculated using the gamma camera. The artificial neural network uses the softmax function with cross-entropy loss to diagnose CKD normal or abnormal labels depending on the value of GFR in the output layer.

Results

The results showed that the accuracy of the proposed ANN model was 99.20% for K-fold cross-validation. The sensitivity and specificity were 99.10% and 99.20%, respectively. The Area under the curve (AUC) was 0.9994.

Conclusion

The proposed model using an artificial neural network can classify the normal or abnormal stages of CKD. After implementing the proposed model clinically, it may upgrade the gamma camera to diagnose the normal or abnormal stages of the CKD with an appropriate GFR value.

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Data availability

The institution at which the work was performed—Institute of Nuclear Medicine and Allied Sciences, Rajshahi-6000, Bangladesh.

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Author information

Author notes

  1. Alamgir Hossain

    Present address: Department of Physics, University of Rajshahi, Rajshahi, 6205, Bangladesh

Authors and Affiliations

  1. Department of Physics, University of Rajshahi, Rajshahi, 6205, Bangladesh

    Shupti Sarker

  2. Institute of Nuclear Medicine and Allied Sciences, Bangladesh Atomic Energy Commission, Rajshahi, 6000, Bangladesh

    Shariful Islam Chowdhury & Mostofa Shamim Ahsan

  3. Kyushu University, Fukuoka, Japan

    Alamgir Hossain

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  1. Alamgir Hossain
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  2. Shariful Islam Chowdhury
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Supplementary Information

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12149_2021_1676_MOESM1_ESM.tif

Supplementary file1 Supplementary Figure 1. Predicted pseudo probability curve for the stages of chronic kidney diseases. (TIF 66 kb)

12149_2021_1676_MOESM2_ESM.tif

Supplementary file2 Supplementary Figure 2: Output values for the artificial neural network using cross-entropy and softmax function in the output layers based on output label normal (TIF 127 kb)

12149_2021_1676_MOESM3_ESM.tif

Supplementary file3 Supplementary Figure 3: Output values for the artificial neural network using cross-entropy and softmax function in the output layers based on output label abnormal (TIF 101 kb)

Supplementary file4 (DOCX 12 kb)

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Hossain, A., Chowdhury, S.I., Sarker, S. et al. Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera. Ann Nucl Med 35, 1342–1352 (2021). https://doi.org/10.1007/s12149-021-01676-7

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  • DOI: https://doi.org/10.1007/s12149-021-01676-7

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