Development of a deep learning algorithm for the detection of renal image and luminal emptying in diuretic renography
Baran Tokar1
, Ozer Celik2, Nuran Cetin3, Tehran Abbasov1, Ilknur Ak Sivrikoz4
1Department of Pediatric Surgery, Division of Pediatric Urology, Eskişehir Osmangazi University Faculty of Medicine, Eskiyehir, Türkiye
2Department of Mathematics-Computer, Eskisehir Osmangazi University, Center of Research and Application for Computer-Aided Diagnosis and Treatment in Health, Eskişehir, Türkiye
3Department of Pediatric Nephrology, Eskişehir Osmangazi University Faculty of Medicine, Eskiyehir, Türkiye
4Department of Nuclear Medicine, Eskişehir Osmangazi University Faculty of Medicine, Eskiyehir, Türkiye
Keywords: Artificial intelligence, children, deep learning, diuretic renography, nuclear medicine.
Abstract
Objectives: This study aimed to determine the accuracy of deep learning (DL) in kidney detection and differentiation of luminal emptying in pediatric diuretic renography.
Patients and methods: In the retrospective study, labeling was performed on 1,260 diuretic renography images of 36 children with unilateral or bilateral hydronephrosis between January 2020 and December 2020. The Tensorflow Object Detection API was used to deploy object detection models. Sensitivity, precision, and F1 score were determined for the detection of the right or left kidney as an object. Supervised training was applied for the differentiation of filled and empty renal pelvis and calyxes.
Results: In 1,260 labeled renal images, the left or right kidney was detected by the machine with 94% sensitivity, 96% precision, and 95% F1 score. The accuracy for differentiation was 88% for filled renal pelvis and calyxes and 66% for empty renal pelvis and calyxes.
Conclusion: The machine using DL algorithms with a large data set training may differentiate the kidney, its location, and the contrast-filled lumen. Low contrast and unclear boundaries in an empty lumen may affect the quality of annotation. The DL model used in this study could be adapted to other urinary system pathologies in medical scans.
Citation: Tokar B, Celik O, Cetin N, Abbasov T, Ak Sivrikoz I. Development of a deep learning algorithm for the detection of renal image and luminal emptying in diuretic renography. Turkish J Ped Surg 2024;38(1):22-26. doi: 10.62114/ JTAPS.2024.19.
The study protocol was approved by the Eskişehir Osmangazi University NonInterventional Clinical Studies Institutional Review Board (no: 241/2021). The study was conducted in accordance with the principles of the Declaration of Helsinki.
A written informed consent was obtained from the parents and/or legal guardians of the patients.
Data Sharing Statement:
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Project development, data collection, andlabeling, manuscript writing/editing: B.T.; Project development, data management, manuscript writing: O.C.; Project development, manuscript writing: N.C.; Data collection: T.A.; Project development, data collection and labeling, manuscript writing/editing: I.A.
The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.
The authors received no financial support for the research and/or authorship of this article.