ArticleAnnals of nuclear medicine2021
Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera.
Article in Annals of nuclear medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care.BMJ health & care informatics · 2026Article
- Radiomic Feature Extraction Based on Computed Tomography for Stroke Patients.Journal of medical physicsArticle
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveChronic 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).
methodsTwo 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
resultsThe 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.
conclusionThe 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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