Method, system, device and storage medium for determining the surface entry point of kidney stones

Through three-dimensional CT image processing and model construction, the optimal surface entry point for kidney stones is calculated, which solves the inaccuracy and time-consuming problems of entry point selection in traditional methods and realizes efficient and safe kidney stone lithotripsy surgery.

CN119970266BActive Publication Date: 2025-09-16TONGJI HOSPITAL ATTACHED TO TONGJI MEDICAL COLLEGE HUAZHONG SCI TECH
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Patent Information

Application Number
CN202510454614.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-04-11
Publication Date
2025-09-16
Estimated Expiration
2045-04-11

AI Technical Summary

Technical Problem

In traditional extracorporeal shock wave lithotripsy for kidney stones, the selection of the entry point relies on the doctor's experience, which has limited accuracy and is time-consuming. It is difficult to fully consider the patient's anatomical structure, which may lead to poor surgical results or additional injuries.

Method used

By acquiring three-dimensional CT images of patients with kidney stones, slicing and binarizing them, extracting bone point cloud and lung gas point cloud data, constructing an avoidance constraint function and an incident point prediction model, and using gradient edge detection and shock wave focal length constraints to calculate the optimal surface incident point.

Benefits of technology

It improves the accuracy of planning the entry point of kidney stones on the body surface, reduces the difficulty of surgery, improves the efficiency of lithotripsy, and can avoid risk areas.

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Abstract

The present invention discloses a method, system, device, and storage medium for determining the surface entry point of a kidney stone. The method comprises: slicing a three-dimensional CT image of a kidney stone patient to obtain multiple two-dimensional CT images; binarizing the multiple two-dimensional CT images using an Ostu threshold algorithm, and determining bone point cloud data and lung gas point cloud data based on the multiple binarized images; obtaining surface contour point cloud data based on the multiple two-dimensional CT images using a gradient edge detection Sobel algorithm; and inputting the surface contour point cloud data, bone point cloud data, lung gas point cloud data, and stone point prediction coordinates into a stone surface entry point prediction model to obtain the optimal surface entry point of the kidney stone. The present invention, through the stone surface entry point prediction model, greatly improves the accuracy of kidney stone surface entry point planning, reduces the practical difficulty of entry point selection in extracorporeal lithotripsy surgery, and improves the efficiency of lithotripsy surgery.
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