Intelligent auxiliary diagnosis method and system for frozen section in thyroid cancer operation

By combining terahertz near-field imaging technology with deep learning models, the problems of ice crystal artifacts and subjectivity in intraoperative frozen section diagnosis of thyroid cancer have been solved, enabling rapid and objective identification and segmentation of cancer cells, and improving the accuracy and consistency of diagnosis.

CN122063073APending Publication Date: 2026-05-19INST OF ENERGY HEFEI COMPREHENSIVE NAT SCI CENT (ANHUI ENERGY LAB)
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Patent Information

Application Number
CN202610176104.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-06
Publication Date
2026-05-19

AI Technical Summary

Technical Problem

Existing intraoperative frozen section diagnostic techniques for thyroid cancer suffer from problems such as interference from ice crystal artifacts, strong subjective diagnosis, and high time pressure, resulting in low diagnostic accuracy, poor reliability, and difficulty in meeting the needs of rapid diagnosis.

Method used

Terahertz near-field imaging technology is used to acquire images of unstained frozen sections, and a deep learning model is used for automatic identification and segmentation to generate a visual report to assist in diagnosis, eliminating the need for traditional staining and mounting steps.

Benefits of technology

It improves the identification of key nuclear features of thyroid cancer cells, reduces reliance on the subjective experience of pathologists, enhances the consistency and accuracy of diagnosis, and meets the time requirements for rapid intraoperative diagnosis.

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Abstract

The invention relates to the technical field of medical image and artificial intelligence technology crossing, in particular to an intelligent auxiliary diagnosis method and system for a frozen section in a thyroid cancer operation. According to the technical scheme, the method comprises the following steps that a thyroid tissue intraoperative frozen section to be diagnosed is prepared, and the frozen section does not need to be dyed or subjected to section sealing treatment; and scanning the frozen section by using a terahertz near-field imaging system to obtain terahertz near-field image data of the frozen section. According to the method, the terahertz near-field imaging technology is adopted, the situation that cell nucleus morphology is difficult to observe due to ice crystal artifacts in a traditional frozen section is avoided, cancer cells are automatically recognized in combination with an AI model trained based on large-scale labeling data, and the objectivity and consistency of diagnosis are remarkably improved; meanwhile, the steps of dyeing and mounting are omitted, and rapid imaging and analysis are realized, so that the intraoperative diagnosis time is greatly shortened, and a generated visual contour report also provides visual decision support for pathologists.
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