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Kidney Structure

Kidney Structure

The kidneys are two large bean-shaped organs located in the upper abdomen. They filter the blood several times a day to remove toxins and rebalance water and electrolytes of the circulatory system via the renal veins. The kidneys receive blood directly from the heart via the renal arteries. These arteries enter the kidney at the hilum, the concave surface of the bean, where they branch and divide into smaller vessels and capillaries.

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Updated: May 21, 2025

Unilateral Ureteral Obstruction Model for Investigating Kidney Interstitial Fibrosis

Unilateral Ureteral Obstruction Model for Investigating Kidney Interstitial Fibrosis

Published on: April 25, 2025

Multiple kidney stones prediction with efficient RT-DETR model.

Ponduri Vasanthi¹, Lingamallu Naga Srinivasu², Ventrapragada Teju³

¹Eswar College of Engineering, India.

Computers in Biology and Medicine

|March 19, 2025

Summary

The RT-DETR model offers efficient and accurate kidney stone detection, outperforming existing methods in identifying multiple stones in CT scans. This advancement improves diagnostic capabilities and patient treatment strategies.

Keywords:

Cross-scale feature-fusion Intrascale feature interaction Kidney stone detection Real time detection transformer Small scale object detection

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Area of Science:

Medical Imaging
Artificial Intelligence
Nephrology

Background:

Kidney stone detection (KSD) is crucial for effective treatment planning in medical imaging.
Current models struggle with multiple stones per CT slice and slow prediction times.
Precise identification of kidney stone types is essential for tailored therapeutic approaches.

Purpose of the Study:

Introduce the RT-DETR model for efficient and accurate multi-kidney stone detection.
Address limitations of existing models in handling multiple stones and prediction speed.
Enhance diagnostic capabilities in kidney stone identification.

Main Methods:

Utilized RT-DETR with a hybrid encoder featuring Attention-based Intra-Scale Feature Interaction (AIFI) and Cross-Scale Feature-fusion Module (CSFM).
Incorporated an Intersection over Union (IoU)-aware query selection mechanism for improved heterogeneous kidney stone detection.
Evaluated model performance on a dataset of annotated CT images.

Main Results:

RT-DETR achieved 74.3% precision, 91% recall, 73.3% mAP, and 82.65% accuracy.
Detection time was 1.043 seconds for small kidney stones.
RT-DETR significantly outperformed existing models (OF, KC, ED19, DL, EL, YOLOv5, YOLOv8) in statistical tests.

Conclusions:

RT-DETR model significantly advances kidney stone detection with improved efficiency and accuracy.
Effective management of multi-scale features and IoU-aware queries enhance diagnostic capabilities.
Potential to streamline clinical workflows and improve patient outcomes through precise, timely diagnosis.