# Artificial Intelligence in Kidney Stone Imaging: Enhancing Classification and Detection for Improved Diagnostic Accuracy

## TL;DR

This paper introduces an AI model that accurately detects and classifies kidney stones in medical images, achieving near-perfect performance and offering potential for improved diagnostic efficiency.

## Contribution

The novel contribution is a highly accurate AI model for kidney stone detection using cloud-based training and achieving 100% correct classification.

## Key findings

- The AI model achieved 100% correct classification of kidney stone and normal images.
- The model demonstrated 99.9% precision and recall in detecting kidney stones.
- Cloud-based training enabled a cost-efficient and sustainable workflow without physical hardware.

## Abstract

This study presents an advanced artificial intelligence (AI) model designed to accurately classify and detect kidney stones using medical imaging. Leveraging cloud-based computational resources, the model was trained to differentiate between stone-containing and normal kidneys while simultaneously identifying the precise localization of stones within images. The dataset consisted of 6,720 radiologic images representing clinically relevant stone cases and normal renal anatomy, with an 80-10-10 split for training, validation, and testing to ensure reliable assessment. Notably, the model achieved exceptional diagnostic performance, reflected by an average precision of 1.00 and both precision and recall reaching 99.9%. A perfect confusion matrix, demonstrating 100% correct classification of both stone and non-stone images, further underscores the robustness of the model. Model development required no physical hardware investment due to the use of cloud infrastructure, ensuring a cost-efficient and environmentally sustainable workflow. While results demonstrate strong clinical potential for automated nephrolithiasis detection, further evaluation on larger, multi-institutional datasets and across varied imaging modalities is recommended to strengthen generalizability. This work highlights the growing role of AI-enhanced diagnostic tools in urologic imaging, offering the promise of faster interpretation, improved workflow efficiency, and earlier identification of kidney stone disease.