Development of a deep learning algorithm for the detection of renal image and luminal emptying in diuretic renography

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.