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The kidneys are a pair of bean-shaped organs in the human body that play a critical role in maintaining overall health. They filter out waste products from the blood, regulate blood pressure, maintain electrolyte balance, and stimulate the production of red blood cells.
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Deep learning for kidney trauma detection: CT image algorithm performance and external validation: experimental

Chien-Hung Liao¹, Chun-Hsiang Ouyang¹, Chih-Chi Chen^1,2,3,4,5,6

¹Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan.

International Journal of Surgery (London, England)

|January 27, 2025

Summary

This summary is machine-generated.

A novel deep learning (DL) algorithm, RenoTrNet, accurately detects kidney trauma on CT scans. This tool shows promise for improving trauma diagnosis in clinical settings.

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Kidney trauma detection on CT scans is challenging and often overlooked.
Deep learning (DL) applications in kidney injury detection are underexplored.
This study addresses the need for improved kidney trauma detection using DL.

Purpose of the Study:

Develop and validate a DL algorithm for detecting kidney trauma.
Utilize institutional trauma data for training.
Perform external validation using the Radiological Society of North America (RSNA) dataset.

Main Methods:

Developed RenoTrNet, a DL model trained on institutional data.
Evaluated performance using accuracy, sensitivity, specificity, PPV, and NPV.
Employed heatmap visualizations for model interpretability.

Main Results:

Internal validation: 0.88 accuracy, 0.75 sensitivity, 0.95 specificity.
External RSNA validation: 0.93 accuracy, 0.73 sensitivity, 0.94 specificity.
External validation showed high NPV (0.98) but lower PPV (0.45).

Conclusions:

RenoTrNet DL algorithm demonstrates high accuracy for kidney trauma detection on CT scans.
The model shows potential for clinical deployment in real-world trauma diagnosis.
Optimizing image segmentation and computational efficiency can enhance clinical utility.