Endoscopic examination auxiliary system based on artificial intelligence

A technology of artificial intelligence and auxiliary systems, applied in the field of neural networks, can solve the problems of no unified operating standards, achieve comprehensive and safe inspections, improve inspection quality, and avoid false or missed inspections

Active Publication Date: 2021-08-10
萱闱(北京)生物科技有限公司 +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, in the course of practice, it is found that during the use of endoscopes, the inspection quality is completely controlled by the operator, and there is no unifor

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  • Endoscopic examination auxiliary system based on artificial intelligence
  • Endoscopic examination auxiliary system based on artificial intelligence
  • Endoscopic examination auxiliary system based on artificial intelligence

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Embodiment Construction

[0047] The principle and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and implement the present invention, rather than to limit the scope of the present invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0048]Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, device, method or computer program product. Therefore, the present disclosure may be embodied in the form of complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0049] According to an embodiment of the present invention, an artificial intelligence-base...

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Abstract

The invention provides an endoscopic examination auxiliary system based on artificial intelligence. The system comprises: an inspection data acquisition module configured to obtain inspection data through an endoscope device, the inspection data at least comprising inspection time data and inspection image data; the inspection quality monitoring module, which is configured to perform data processing on the inspection data, wherein the data processing at least comprises data statistics and data classification, and then the inspection data of a specific type are respectively input into a corresponding quality monitoring model constructed based on a neural network to determine the inspection quality; an examination auxiliary module, which is configured to input the examination image data into a corresponding recognition model constructed based on a neural network to obtain an identifier for auxiliary operation based on a part or an object corresponding to the examination image data, and output the identifier and the corresponding examination image data according to a preset mode.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of neural networks, and more specifically, embodiments of the present invention relate to an artificial intelligence-based endoscopy assistance system. Background technique [0002] This section is intended to provide a background or context for implementations of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] At present, in order to accurately diagnose diseases in the human body (such as the digestive tract, etc.), the images in the digestive tract collected by the endoscope are usually used to assist doctors in identifying the lesions in the digestive tract, and based on the identified lesions, the diseases of the digestive tract are diagnosed. diagnosis. However, in the course of practice, it is found that during the use of endoscopes, the inspection quality is completely controlled by th...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0012G06N3/04G06N3/08G06T2207/10068G06T2207/20081
Inventor 乔元风曾凡
Owner 萱闱(北京)生物科技有限公司
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