AI-based thyroid nodule left and right leaf positioning and ultrasonic report error correction method

A technology of thyroid nodules and error correction methods, applied in the field of image analysis, can solve problems such as manual errors, medical accidents, and low inspection efficiency, and achieve the effect of avoiding mistreatment and ensuring consistency and accuracy

Active Publication Date: 2020-09-11
SHANGHAI CHANGZHENG HOSPITAL
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Problems solved by technology

[0002] Ultrasound is widely used in the inspection of various tissues and organs of the human body due to its non-invasive, convenient, economical, and radiation-free advantages. At present, domestic clinical work is heavy, and a large number of repetitive inspection tasks make the inspection efficiency low. Ultrasound diagnosticians are prone to fatigue. For symmetrical organs The diagnosis of thyroid nodules is highly depende

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  • AI-based thyroid nodule left and right leaf positioning and ultrasonic report error correction method
  • AI-based thyroid nodule left and right leaf positioning and ultrasonic report error correction method

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[0015] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0016] The embodiment of the present invention relates to an AI-based method for locating the left and right thyroid nodules and ultrasonic report error correction method, such as figure 1 As shown, it includes the following steps: establishing a deep convolutional neural network, extracting and classifying the features of the standard short-axis section ultrasound image of the thyroid gland through the deep convolutional neura...

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Abstract

The invention relates to an AI-based thyroid nodule left and right leaf positioning and ultrasonic report error correction method, and the method comprises the following steps: building a deep convolution neural network, extracting the features of a thyroid standard short-axis section ultrasonic image through the deep convolution neural network, and carrying out the classification; and based on aninput box event monitoring mechanism, comparing the classification result with keywords in the ultrasonic report, and if the classification result is inconsistent with the keywords in the ultrasonicreport, giving a prompt. According to the invention, the phenomenon that left and right focuses are reversed due to manual errors is effectively avoided, so that the consistency and accuracy of text reports and pictures are ensured, and adverse medical accidents are reduced.

Description

technical field [0001] The invention relates to the technical field of image analysis, in particular to an AI-based method for locating left and right thyroid nodules and correcting errors in ultrasound reports. Background technique [0002] Ultrasound is widely used in the inspection of various tissues and organs of the human body due to its non-invasive, convenient, economical, and radiation-free advantages. At present, domestic clinical work is heavy, and a large number of repetitive inspection tasks make the inspection efficiency low. Ultrasound diagnosticians are prone to fatigue. For symmetrical organs The diagnosis of thyroid nodules is highly dependent on ultrasound, which at least requires the ultrasonic diagnostic documents to be specific about the location of nodules. Only the accuracy of the information can ensure the correct follow-up treatment. Once the information on the left and right sides of the nodule is wrong, it will often cause serious medical accidents...

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Application Information

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IPC IPC(8): G06T7/00G06T7/70G16H15/00
CPCG06T7/0012G06T7/70G16H15/00G06T2207/10132G06T2207/20081G06T2207/20084
Inventor 张航任宇翔赵佳琦章建全
Owner SHANGHAI CHANGZHENG HOSPITAL
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