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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

Artificial intelligence model based on CT imaging for predicting infected upper urinary tract calculi.

Shichao Song¹, Tao Ma¹, Jiandong Wang²

¹Department of Urology, Affiliated Hospital of Hebei University, Baoding, Hebei, China.

Frontiers in Surgery

|May 1, 2026

Summary

This study developed an artificial intelligence (AI) model using Computed Tomography (CT) scans to predict infected upper urinary tract calculi. The AI model shows high specificity, aiding in the preoperative identification of potential infectious stones.

Keywords:

artificial intelligence deep learning infectious stones stone composition urinary tract calculi

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Area of Science:

Medical Imaging
Artificial Intelligence
Urology

Background:

Infected upper urinary tract calculi pose diagnostic challenges.
Accurate preoperative identification is crucial for effective treatment planning.

Purpose of the Study:

To develop and evaluate an AI model using CT imaging for predicting infected upper urinary tract calculi.
To assess the model's efficacy in preoperative diagnosis.

Main Methods:

A RetinaNet model was trained on CT images from 465 patients with urinary tract calculi.
Infectious stones were defined by >25% struvite or carbonate apatite content.
Model performance was evaluated using metrics like Mean Average Precision (mAP) and accuracy on a separate test set.

Main Results:

The AI model achieved high specificity (93.09%) in predicting infectious stones.
Moderate sensitivity (72.78%) was observed, indicating limitations for ruling out infections.
The model demonstrated significant consistency with infrared spectroscopy (kappa=0.679).

Conclusions:

The AI model demonstrates high specificity for predicting infectious upper urinary tract calculi, valuable for preoperative identification.
Its moderate sensitivity suggests it should be used as a complementary tool alongside laboratory tests.
The model offers auxiliary guidance for treatment and surgical decisions in patients with urinary tract calculi.