Medical imaging diagnosis report auxiliary generation method and device

A technology for medical imaging and diagnosis report, applied in the field of auxiliary generation method and device for medical imaging diagnosis report, can solve the problems of correctly diagnosing potential safety hazards of patients, poor accuracy and low reliability of medical imaging diagnosis report, and avoiding the diagnosis conclusion. Inconsistency, improve the efficiency of diagnosis, avoid the effect of omission of conclusions

Active Publication Date: 2019-05-10
INFERVISION MEDICAL TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This patented describes an improved way that helps generate accurate or reliable healthcare documentation from patient's own digital data (images) taken during examinations without any human interference. It uses machine learning techniques instead of manually annotated notes, which can lead to issues with incorrect interpretings and potential mistakes made through subjective interpretation.

Problems solved by technology

This technical problem addressed by this patented method relates to improving the efficiency at analyzing and comparing patient's own film with x-ray photos during radiation therapy treatment planning procedures. Current methods require multiple steps such as selecting templates before entering their details into an electronic device that can be used instead of physically recording these details on paper documents. Additionally, there has been no reliable way to accurately determine if any doctor obtained incorrect results when making recommendations based solely upon those entered onto physical documentation.

Method used

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  • Medical imaging diagnosis report auxiliary generation method and device
  • Medical imaging diagnosis report auxiliary generation method and device
  • Medical imaging diagnosis report auxiliary generation method and device

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Embodiment 1

[0071] Example 1: Reference figure 2 , the first embodiment of the present invention provides a method for auxiliary generation of medical imaging diagnosis report, including:

[0072] Step S1000, obtaining medical image description information input by a doctor;

[0073] It is the basic work of front-line radiologists to describe medical images objectively and accurately, and give scientific and professional diagnostic opinions based on this for clinicians' reference. In this embodiment, the medical image can be derived from various imaging methods such as DR, CT, and MRI, and can be aimed at various parts of the human body such as the chest, abdomen, and head. According to the obtained medical image, the doctor can objectively describe the situation, status, characteristics and degree of the image, which is the medical image description information.

[0074] For example, diagnostic description 1: right pneumonia with right pleural effusion, it is recommended to review aft...

Embodiment 2

[0083] Example 2: Reference Figure 3-9 , the second embodiment of the present invention provides a method for auxiliary generation of medical imaging diagnosis report, based on the above figure 2 In the illustrated first embodiment, the step S3000, "use a pre-trained segmentation recognition model to identify the image semantic segments, and obtain a diagnostic opinion segment corresponding to each of the image semantic segments" includes:

[0084] Step S3100, based on the multi-layer cyclic neural network, establish a multi-layer cyclic neural network model;

[0085]As described above, in the multi-layer cyclic neural network described in this embodiment, the cyclic neural network is an RNN (Recurrent Neural Network). A neural network is an artificial neural network in which nodes are connected in a ring. The internal state of such a network can exhibit dynamic timing behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process input sequen...

Embodiment 3

[0162] Embodiment 3: refer to Figure 10 , the third embodiment of the present invention provides a medical imaging diagnostic report auxiliary generation method, based on the above figure 2 In the first embodiment shown, the step S2000, "perform semantic segmentation on the medical image description information to obtain multiple image semantic sections" includes:

[0163] Step S2100, converting the medical image description information into a unit sequence including continuous word units;

[0164] The word unit mentioned above is the converted medical image description, each of which, for example, "right pneumonia with right pleural effusion", where the word units are respectively right lung, inflammation, companion, right side , pleural cavity, effusion. They are arranged together sequentially to form a unit sequence.

[0165] Step S2200, in the unit sequence, converting each word unit into a corresponding semantic vector representing its semantic features;

[0166] St...

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Abstract

The invention provides a medical imaging diagnosis report auxiliary generation method and device. The method comprises the following steps: acquiring medical image description information input by a doctor; Performing semantic segmentation on the medical image description information to obtain a plurality of image semantic segments; Identifying the image semantic segments by using a pre-trained segment identification model to obtain a diagnosis opinion segment corresponding to each image semantic segment; And combining all the diagnosis opinions section by section to obtain diagnosis opinion information, and generating a medical image diagnosis report according to the diagnosis opinion information. The diagnosis report is automatically generated in an assisted manner when a doctor writes diagnosis suggestions based on the neural network technology, the efficiency of diagnosing a patient based on a medical image is improved, and the accuracy, consistency and credibility of medical diagnosis are improved.

Description

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Claims

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Application Information

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Owner INFERVISION MEDICAL TECH CO LTD
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