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Multi-modal intelligent auxiliary diagnosis and treatment system for medical images

A medical imaging and diagnosis and treatment system technology, applied in the medical field, can solve the problems of inability to predict early cancer and low efficiency of hospital film reading, and achieve the effects of improving accuracy and repeatability, improving survival rate, and improving quality of life

Pending Publication Date: 2021-05-14
上海集迈医疗科技有限公司
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention discloses a multi-modal medical imaging intelligent auxiliary diagnosis and treatment system, which is used to solve the problems of low efficiency of hospital film reading and inability to predict early cancers; the present invention is realized through the following technical solutions:

Method used

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  • Multi-modal intelligent auxiliary diagnosis and treatment system for medical images
  • Multi-modal intelligent auxiliary diagnosis and treatment system for medical images
  • Multi-modal intelligent auxiliary diagnosis and treatment system for medical images

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Experimental program
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Embodiment 1

[0026] The multi-modal medical image intelligent auxiliary diagnosis and treatment system includes an acquisition unit 2 and a diagnosis and treatment information management unit 6. The diagnosis and treatment information management unit 6 is provided with an analysis unit 7 and a diagnosis and treatment unit 8. The acquisition unit 3 collects a plurality of comparison cases, and synthesizes them through processing Unit 4 performs image processing and category analysis on the comparative cases and sends them to the analysis unit 7. The processing and integration unit 4 interacts with data from various imaging devices through a standardized interface to meet the fusion of high-definition images of different modalities. After the optimization unit 10 analyzes the biological characteristics of the comparative case, the analysis unit 7 performs AI prognosis algorithm detection on the comparative case through the algorithm unit 9 . The optimization unit 10 is equipped with a convolu...

Embodiment 2

[0034] This embodiment provides the multi-modal medical imaging intelligent auxiliary diagnosis and treatment system of Embodiment 1. The background storage unit 14 runs a security antivirus module and a security check module, and the security check module is specifically used to call the security The antivirus module conducts a security check on the medical image files in the storage area.

[0035] The safety check module is used to preserve and update the virus database, and sends the updated virus database to the safety antivirus module, and the safety antivirus module is used for real-time monitoring of the data content transmitted through the interface of the background storage unit 14, And compare the monitored data content with the viruses in the virus database to detect whether there is a virus in the data content, if it is detected that there is a virus in the data content, then clear the data content and re-store the data. If it is detected that there is no virus in ...

Embodiment 3

[0038] In addition to the multi-modal medical image intelligent auxiliary diagnosis and treatment system of Embodiment 2 provided in this embodiment, the analysis unit 7 is also provided with a learning unit 11, and the learning unit 11 has a good understanding of the algorithm unit 9 and the optimization unit 10. Test results for case studies. The learning unit 11 realizes the association of AI analysis of raw data images and text materials, realizes lesion extraction, labeling and classification, connects with deep learning programs, and establishes individualized and visualized prediction nomogram library.

[0039] Realize two-way communication between hospital scientific research and education management. Experts or trainees can not only understand cancer information in multiple directions, but also compare the patient's medical record information, radiation prediction carrier scores and diagnosis reports to understand the cause of disease diagnosis more comprehensively and...

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Abstract

The invention belongs to the technical field of medical treatment and relates to a multi-modal intelligent auxiliary diagnosis and treatment system formedical images. The system comprises an acquisition unit and a diagnosis and treatment information management unit; the diagnosis and treatment information management unit is provided with an analysis unit and a diagnosis and treatment unit; the acquisition unit acquires a plurality of comparative cases; image processing and category analysis are carried out on the comparative cases through a processing comprehensive unit; the comparative cases are sent to an analysis unit; after biological characteristics of the comparative cases are analyzed through an optimization unit; the analysis unit carries out AI prognosis algorithm detection on the comparative cases through an algorithm unit; the acquisition unit acquires diagnosis and treatment data and medical record information of a patient through a diagnosis and treatment instrument; an image preprocessing unit preprocesses the diagnosis and treatment data and sends the data to the diagnosis and treatment unit; a comparison unit compares detection data of the algorithm unit; and the score and the diagnosis report of a radiation prediction carrier are obtained through a detection unit. The problems of low film reading efficiency of a hospital and failure of the hospital to pre-judge early-stage cancers are solved.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to a multimodal medical image intelligent auxiliary diagnosis and treatment system. Background technique [0002] Statistics show that some tertiary hospitals generate more than 10T of imaging data a year, and all of them are read manually, which means that radiologists need to deal with thousands or even tens of thousands of images every day. According to incomplete statistics, current medical imaging data accounts for 85% to 90% of hospital medical big data. With the improvement of the accuracy of medical imaging equipment, images with higher density and higher pixels will bring higher diagnostic accuracy. It also means that radiologists have an increasingly heavy reading workload. [0003] The skills and experience of each radiologist are different, and the film reading work adopts a random allocation model. Experienced doctors may not necessarily see complex films, and similar...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/187
CPCG06T7/0012G06T7/11G06T7/187G06T2207/10081G06T2207/10116G06T2207/10132G06T2207/10088G06T2207/10104G06T2207/20081G06T2207/20084G06T2207/30068
Inventor 赵能全
Owner 上海集迈医疗科技有限公司
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