Automatic classification of kidney stone components based on smartphone microscopy and the GoogLeNet model

Yuxuan Du; Yanbing Liang; Ping Li; Yangang Xu; Xingshu Zhen; Liuqiang Li; Mei Huang; Yifei Zhan; Guanyang Li; Zichi Wu; Chan Hiu Fung Anson; Chenyu Zhou; Ayizeba Aimaierjiang; Jiaqi Chen; Kaiyue Tang; Yaochuan Guo; Zhiyuan Guo; Jun Bian; Dehui Lai

doi:10.1186/s12894-026-02080-x

Key Points

The research aims to create a system for classifying kidney stone compositions using a smartphone microscope and a deep learning model.
Developed a smartphone-based microscope for capturing images of kidney stones.
Applied the GoogLeNet deep learning model for image classification.
Evaluated classification accuracy across different stone types, including uric acid and magnesium ammonium phosphate hexahydrate.
Achieved an overall classification accuracy of 85.7%.
Showed strong performance specifically in classifying uric acid and magnesium ammonium phosphate hexahydrate stones.
Demonstrated practicality for use in resource-limited regions due to low cost and portability.

Implication

Develops a system that classifies kidney stone components using smartphone microscopy, suggesting practical diagnostics for resource-limited areas.

Abstract

This study successfully developed a kidney stone composition classification system integrating a smartphone-based microscope with a deep learning model, achieving an overall classification accuracy of 85.7%. The system exhibited strong performance in classifying uric acid and magnesium ammonium phosphate hexahydrate stones. With its low cost, efficiency, and portability, this system offers an economical and practical diagnostic solution for resource-limited regions.

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Cite This Study

Du et al. (2026) studied this question.

synapsesocial.com/papers/699010ce2ccff479cfe56f81 https://doi.org/10.1186/s12894-026-02080-x

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Automatic classification of kidney stone components based on smartphone microscopy and the GoogLeNet model

Key Points

Implication

Abstract

Discussion

Cite This Study

Automatic classification of kidney stone components based on smartphone microscopy and the GoogLeNet model

Key Points

Implication

Abstract

Discussion

Cite This Study

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