Artificial intelligence wound assessment method and intelligent terminal

Abstract

The invention relates to an artificial intelligence wound assessment method and an intelligent terminal. The method comprises the following steps: T1, establishing communication between the artificialintelligence wound assessment intelligent terminal and an artificial intelligence wound assessment area measuring and calculating device; T2, commanding the artificial intelligence wound assessment area measuring and calculating device to shoot a wound picture by the artificial intelligence wound assessment intelligent terminal; T3, inputting a shot wound picture into the trained feature extraction network and the convolutional neural network by the artificial intelligence wound evaluation area measuring and calculating device; t4, the artificial intelligence wound assessment area measuring and calculating device performing wound assessment area measurement and calculation on the shot wound picture to obtain wound information; and T5, the artificial intelligence wound evaluation area measuring and calculating device generating an artificial intelligence wound evaluation report based on the wound information and sending the artificial intelligence wound evaluation report to the artificial intelligence wound evaluation intelligent terminal. The method has the advantages that through an artificial intelligence algorithm, human resources are saved, and efficiency and accuracy are improved.

Description

【Technical field】 [0001] The invention relates to the field of wound image recognition, in particular to an artificial intelligence wound assessment method and an intelligent terminal. 【Background technique】 [0002] At present, clinical medical personnel use a ruler to measure the wound size, record the length and width of the wound, and calculate its area. Disadvantages of this method: (1) The measurement tool needs to be in contact with the wound, and there is a risk of cross-infection; (2) The smallest unit of ruler measurement is centimeters, and the centimeter measurement is not fine enough in the later stage to judge the progress of the wound; (3) For wounds with irregular wound edges, it is more difficult to measure the length and width. [0003] At present, there is a "wound measurement" APP ("wound measurement record"), whose functions include: taking pictures of wounds, calculating the area, and entering basic patient data. Disadvantages of this method: (1) The ...

AI Technical Summary

Problems solved by technology

Disadvantages of this method: (1) The measurement tool needs to be in contact with the wound, and there is a risk of cross-infection; (2) The smallest unit of ruler measurement is centimeters, and the centimeter measurement is not fine enough in the later stage to judge the progress of the wound; (3) For wounds with irregular wound edges, it is more difficult to measure the length and width

Disadvantages of this method: (1) The calculation of the area is based on manual calculation of the length and width of the wound; (2) The wound assessment only stays in the calculation of the wound area; (3) The clinical use of medical staff is not much

Disadvantages of this device: only the accurate measurement of the wound area is achieved, but in other aspects of wound assessment, such as the patient's whole body assessment, local assessment, etc., the assessment cannot be achieved

After searching, it was found that the wound assessment equipment and system based on artificial intelligence algorithm had not been reported

[0006] The multi-receptive pyramid network PSPNet (Pyramid Scene Parsing Network) is a multi-scale estimation network that integrates context information improved on the Feature Pyramid Network (FPN), and introduces more context information. When the segmentation layer has more When global information is used, the probability of mis-segmentation will be lower; this idea is currently applied in many image fields, and there are many ways to introduce more context information, such as: 1. Increase the receptive field of the separation layer, which The first method is the most intuitive, the wider the field of view, the more things you can see; there are many ways to increase the receptive field, such as dilated convolution, which is successfully applied to the deeplab algorithm; the global mean Pooling operation, PSPNet's global mean pooling operation is also a way to increase the receptive field; 2. The fusion of deep features and shallow features increases the semantic information of shallow features, so that there is enough for shallow segmentation. Context information, as well as detailed information of the target, this approach has existed in FCN as early as, but there is a certain room for optimization including the selection of fusion strategy and segmentation layer

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