Urinary stone component analysis method and apparatus, and electronic device and storage medium

By collecting stone pictures for data enhancement and rebalancing processing, and training deep neural network models, the shortcomings of infrared spectroscopy and dual-energy CT are solved, and the accuracy and applicability of body stone composition analysis is improved, and labor costs are reduced.

WO2025167580A1 Publication Date: 2025-08-14BEIJING TSINGHUA CHANGGUNG HOSPITAL
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Patent Information

Application Number
PCT/CN2025/073565
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-02-05
Filing Date
2025-01-21
Publication Date
2025-08-14

AI Technical Summary

Technical Problem

In the prior art, infrared spectroscopy can only be used for the analysis of stone components of ex vivo samples after surgery, which requires professional operation and increase labor costs. Local stone sampling leads to misjudgment. The dual-energy CT method is limited by the equipment resolution and cannot analyze mixed ingredient stones, which reduces the accuracy and applicability of stone component analysis.

Method used

By collecting stone pictures, performing data enhancement and rebalancing processing, training deep neural network models, and deploying them to the server for stone component analysis, realizing the component recognition and simultaneous description of the original stone composition.

Benefits of technology

It improves the accuracy and applicability of stone composition analysis, can perform stone composition analysis in vitro, reduces labor costs, and improves the efficiency and accuracy of stone composition recognition.

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Abstract

The present disclosure relates to the technical field of stone analysis, and particularly relates to a urinary stone component analysis method and apparatus, and an electronic device and a storage medium. The method comprises: classifying analyzed stone components of an original stone, so as to obtain a stone data set; performing data enhancement and re-balancing processing to obtain a processed data set; and training a deep neural network model to obtain a trained model, and deploying the trained model to a server to receive urinary stone components sent by the server, so as to obtain an analysis result.
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