Medical image report automatic quality control error correction system and method
Through an automated quality control error correction system, the similarity detection and error correction generation model of feature vectors and semantic vectors are used to solve the problem of time-consuming and labor-intensive manual review of existing medical image reports, and efficient and accurate image and report content consistency inspection and error correction are achieved.
CN119724466APending Publication Date: 2025-03-28JIANGSU JINMA YANGMING INFORMATION TECH
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Patent Information
- Application Number
- CN202411793844.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-09
- Publication Date
- 2025-03-28
AI Technical Summary
Technical Problem
现有医学图像报告依赖人工审核,耗时费力,难以及时发现报告中的潜在错误,增加了医疗差错的风险,影响诊断报告的准确性和医疗决策的效率。
Method used
Design an automated quality control error correction system for medical image reporting. By extracting the feature vectors of medical image data and the semantic vectors of report text data, the similarity between the two is calculated, the content consistency between the image and the report is automatically detected, and the pre-trained automatic error correction generation model is used for error correction.
Benefits of technology
It significantly improves the level of quality control automation of medical images and reports, reduces errors in diagnosis, ensures the accuracy and completeness of diagnostic reports, and improves diagnostic efficiency and quality.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of medical image report error correction, in particular to a medical image report automatic quality control error correction system and method. The method comprises the steps of importing medical image data, analyzing the medical image data by using an open source tool, performing standardization processing and local contrast enhancement on an image, performing automatic segmentation of a target area through a UNet model, and calculating density features of a key area to obtain feature vectors; meanwhile, the report text data is subjected to word segmentation, standardization processing and keyword extraction steps, the word importance is evaluated by utilizing a BERT model, a semantic vector of the report is generated through a text model, finally, the consistency of the image and the report is detected by calculating the similarity between a feature vector and the semantic vector, and an automatic error correction program is started when the image and the report are inconsistent. And error types are analyzed and corrected. According to the method, medical image processing, natural language processing and deep learning technologies are combined, so that human errors are reduced, and the diagnosis efficiency and accuracy are improved.
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