CN111539342A - Identification system of infusion seepage - Google Patents

Identification system of infusion seepage Download PDF

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CN111539342A
CN111539342A CN202010340702.XA CN202010340702A CN111539342A CN 111539342 A CN111539342 A CN 111539342A CN 202010340702 A CN202010340702 A CN 202010340702A CN 111539342 A CN111539342 A CN 111539342A
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CN111539342B (en
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蔡江
肖明瑞
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    • G—PHYSICS
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Abstract

The invention belongs to the technical field of machine vision, and discloses an identification system for infusion leakage, which comprises an image acquisition module and an analysis identification module; the image acquisition module is used for acquiring a first image and a second image, wherein the first image and the second image respectively correspond to a hand image of the same person before infusion and a hand image during infusion; the analysis and identification module is used for judging whether infusion leakage occurs according to the first image and the second image. The invention solves the problem that the infusion leakage cannot be identified in time in the prior art, can automatically compare hand images of a patient before and during infusion, and can more accurately and efficiently judge the early infusion leakage.

Description

Identification system of infusion seepage
Technical Field
The invention relates to the technical field of machine vision, in particular to an identification system for infusion leakage.
Background
Intravenous infusion is one of the most basic clinical care operations and is an important means for treating and rescuing patients in hospitals. The phenomenon that liquid medicine leaks out of blood vessels sometimes occurs in the infusion process, the leaked liquid can stimulate or damage the skin and tissues, certain pain is brought to patients, and the skin and tissues can be necrotized in serious cases.
At present, the diagnosis of the leakage of the intravenous infusion generally depends on the patrol of nurses, but the early symptoms of the leakage are not obvious, so that the difficulty of the visual observation is high, and the leakage is difficult to be found in time.
Disclosure of Invention
The embodiment of the application solves the problem that in the prior art, the infusion leakage cannot be identified in time by providing the identification system for the infusion leakage.
The embodiment of the application provides an identification system of infusion seepage includes:
the image acquisition module is used for acquiring a first image and a second image; the first image and the second image respectively correspond to a hand image of the same person before infusion and a hand image during infusion;
and the analysis and identification module is used for judging whether infusion leakage occurs according to the first image and the second image.
Preferably, the infusion leak identification system further comprises: an alarm module;
after the analysis and identification module judges that infusion leakage occurs, the analysis and identification module sends alarm information to the alarm module, and the alarm module is used for giving an alarm according to the alarm information.
Preferably, the infusion leak identification system further comprises: a display module;
the display module is used for displaying the first image and the second image;
or the display module is used for displaying the alarm information;
or the display module is used for displaying the alarm information, the first image and the second image.
Preferably, the infusion leak identification system further comprises: a key module;
the key module is used for keying in at least one item of information of infusion number information and image acquisition command information.
Preferably, the image acquisition module includes:
the image receiving module is used for acquiring the first image and the second image through shooting;
and the image preprocessing module is used for preprocessing the first image and the second image, and the preprocessing comprises the step of normalizing the brightness and the contrast of the images.
Preferably, the following formula is adopted for the normalization processing of the brightness and the contrast of the image:
Figure BDA0002468349490000021
wherein x is original image pixel information, y is normalized image pixel information, and μxRepresenting the mean value, σ, of the luminance of the pixels of the original imagexRepresenting the standard deviation of the brightness of the pixels of the original image;
Figure BDA0002468349490000022
Figure BDA0002468349490000023
in the formula, xiAnd the pixel value of the ith pixel point of the image is represented, and N represents the number of the pixel points of the image.
Preferably, the analysis and identification module obtains the feature vector before infusion corresponding to the first image and the feature vector during infusion corresponding to the second image based on a convolutional neural network, and determines whether infusion leakage occurs according to the difference between the feature vector before infusion and the feature vector during infusion.
Preferably, the convolutional neural network includes: the device comprises a convolution layer, a pooling layer, a first full-connection layer and a second full-connection layer;
the convolution layer and the pooling layer are used for acquiring high-level semantic features of the image;
the first full-connection layer is used for summarizing the characteristics of the images and generating the characteristic vector before infusion and the characteristic vector during infusion;
and the second full-connection layer is used for comparing the characteristic vector before infusion and the characteristic vector during infusion and judging whether infusion leakage occurs or not.
Preferably, the image acquisition module adopts an Open-MV camera, and the analysis and identification module adopts an embedded GPU development board.
Preferably, the infusion leak identification system further comprises: an SD card;
the SD card is used for storing a Linux system and an AI environment configuration file of the embedded GPU development board;
or the SD card is used for storing the Linux system and the AI environment configuration file of the embedded GPU development board, the first image and the second image.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
in the embodiment of the application, firstly, a hand image of a patient before infusion is acquired and recorded as a first image through an image acquisition module, a hand image of the patient during infusion is acquired and recorded as a second image, and the first image and the second image respectively correspond to the same patient. And then judging whether the infusion leakage occurs or not according to the first image and the second image through an analysis and identification module. The system provided by the invention can be used by nurses during patrol, and can automatically compare hand images of patients before and during transfusion, so that early transfusion leakage can be judged more accurately and efficiently.
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In order to more clearly illustrate the technical solution in the present embodiment, the drawings needed to be used in the description of the embodiment will be briefly introduced below, and it is obvious that the drawings in the following description are one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic diagram of an identification system for infusion leakage according to an embodiment of the present invention;
FIG. 2 is a block diagram of an infusion leak identification system according to an embodiment of the present invention;
FIG. 3 is a flow chart of an algorithm employed in an infusion leak identification system according to an embodiment of the present invention;
fig. 4 is a data flow diagram of an algorithm used in an infusion leak identification system according to an embodiment of the present invention.
The system comprises a 1-Open-MV camera, a 2-display, a 3-key module, a 4-buzzer alarm, a 5-handheld grip, a 6-NVIDIA JETSON NANO embedded GPU development board and a 7-SD card, wherein the 1-Open-MV camera is connected with the display through a network;
the device comprises an image receiving module 11, an image preprocessing module 12, a liquid leakage judging module 61 and an alarm output module 62.
Detailed Description
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Example 1:
embodiment 1 provides an identification system of infusion leakage, comprising: the device comprises an image acquisition module and an analysis and identification module.
The image acquisition module is used for acquiring a first image and a second image; the first image and the second image respectively correspond to a hand image of the same person before infusion and a hand image during infusion. The analysis and identification module is used for judging whether infusion leakage occurs according to the first image and the second image.
Wherein the image acquisition module comprises: the device comprises an image receiving module and an image preprocessing module.
The image receiving module is used for acquiring the first image and the second image through shooting; the image preprocessing module is used for preprocessing the first image and the second image, and the preprocessing comprises the step of normalizing the brightness and the contrast of the images.
Specifically, the following formula is adopted for normalization processing of the brightness and the contrast of the image:
Figure BDA0002468349490000041
wherein x is original image pixel information, y is normalized image pixel information, and μxRepresenting the mean value, σ, of the luminance of the pixels of the original imagexRepresenting the standard deviation of the brightness of the pixels of the original image;
Figure BDA0002468349490000051
Figure BDA0002468349490000052
in the formula, xiAnd the pixel value of the ith pixel point of the image is represented, and N represents the number of the pixel points of the image.
The analysis and identification module obtains a feature vector before infusion corresponding to the first image and a feature vector during infusion corresponding to the second image based on a convolutional neural network, and judges whether infusion leakage occurs or not according to the difference between the feature vector before infusion and the feature vector during infusion.
The convolutional neural network mainly comprises: the multilayer structure comprises a convolution layer, a pooling layer, a first full-connection layer and a second full-connection layer. The convolution layer and the pooling layer are used for acquiring high-level semantic features of the image; the first full-connection layer is used for summarizing the characteristics of the images and generating the characteristic vector before infusion and the characteristic vector during infusion; and the second full-connection layer is used for comparing the characteristic vector before infusion and the characteristic vector during infusion and judging whether infusion leakage occurs or not.
Specifically, the image acquisition module can adopt an Open-MV camera, namely the Open-MV camera is used for acquiring picture information of a hand of a patient receiving infusion and carrying out preprocessing.
The analysis and identification module can adopt an embedded GPU development board. The GPU development board can be an NVIDIAJETSON NANO embedded GPU development board and is used for comparing the photos of the hands of the patient before and during infusion, comparing the feature vectors of the two photos and judging whether infusion leakage occurs or not according to the difference degree of the feature vectors. Specifically, the calculation of the feature vector difference is realized by the second full-connection layer, the proportion of each parameter of the feature vector is determined during the training of the convolutional neural network, a hand image data set without liquid leakage and a hand image data set with liquid leakage are generated during the training, and the convolutional neural network can automatically determine the proportion of each parameter of the feature vector and the specific size of the threshold value through the training of a large amount of data.
The system is correspondingly provided with the SD card which is used for storing the Linux system and the AI environment configuration file of the embedded GPU development board, and the system is directly loaded and started when the system is initialized. Or the SD card is used for storing the Linux system and the AI environment configuration file of the embedded GPU development board, the first image and the second image.
In the embodiment 1, the early-stage infusion leakage can be accurately and efficiently judged by comparing hand images of a patient before and during infusion, and the judgment result can be obtained for reference and use by nurses or other personnel.
Optimization was performed on the basis of example 1, and other functions were added to obtain example 2.
Example 2:
embodiment 2 provides an identification system of infusion leakage, comprising: the device comprises an image acquisition module, an analysis and identification module, an alarm module, a display module and a key module.
After the analysis and identification module judges that infusion leakage occurs, the analysis and identification module sends alarm information to the alarm module, and the alarm module is used for giving an alarm according to the alarm information.
The analysis and identification module can be understood as being composed of a liquid leakage judgment module and an alarm output module. The leakage judging module is used for judging whether infusion leakage occurs or not, and the alarm output module is used for informing the alarm module and the display module to make corresponding responses to remind medical personnel to take measures in time.
The display module is used for displaying the first image and the second image; or the display module is used for displaying the alarm information; or the display module is used for displaying the alarm information, the first image and the second image.
The key module is used for keying in at least one item of information of infusion number information and image acquisition command information. Namely, the key module is used for man-machine interaction. The system is usually used by nurses, the nurses can take pictures of a plurality of patients by using the system, and can take pictures and input the serial numbers of the patients by the key module, so as to realize the identification and alarm of whether the infusion of the patients leaks.
Specifically, the alarm module can adopt a buzzer alarm, and the buzzer alarm is used for giving an alarm when leakage occurs in infusion. The display module adopts a display.
As shown in fig. 1 and 2, an identification system for infusion leakage may specifically include: the system comprises an Open-MV camera 1, a display 2, a key module 3, a buzzer alarm 4, a handheld grip 5, an NVIDIA JETSON NANO embedded GPU development board 6 and an SD card 7.
The Open-MV camera 1 comprises an image receiving module 11 and an image preprocessing module 12. The NVIDIAJETSON NANO embedded GPU development board 6 comprises a leakage judgment module 61 and an alarm output module 62.
The Open-MV camera 1 is connected with the NVIDIA JETSON NANO embedded GPU development board 6 through a USB interface. The display 2 is connected with the NVIDIA JETSON NANO embedded GPU development board 6 through an HDMI interface.
The corresponding processing flow is shown in fig. 3 and 4, and mainly includes:
the first step is as follows: collecting hand images of a patient before transfusion.
The second step is that: the hand images of the patient during transfusion are collected.
The third step: the method comprises the steps that hand images of a patient before and during infusion are preprocessed, and due to the fact that images obtained by a camera in different illumination environments are large in difference and noise of different degrees exists in the images, preprocessing of the images is conducted firstly in order to guarantee good detection and recognition effects;
and fourthly, sending the two preprocessed pictures to a pre-trained convolutional neural network, as shown in fig. 4, where the convolutional neural network mainly includes a convolutional layer, an active layer, a pooling layer and a full-link layer (mainly marked as a full-link layer 1 and a full-link layer 2 according to functions), the former convolutional layer and pooling layer are used to collect high-level semantic features of the pictures, the active layer is located behind the convolutional layer (not shown in the figure) and is used for nonlinear mapping of feature vectors, the full-link layer 1 is used to summarize features of the pictures and generate 1 × 1000 feature vectors, and the last full-link layer 2 is used to compare feature vectors of the two pictures and is used to determine whether infusion leakage occurs, if so, returning to 1, otherwise returning to 0.
The fifth step: if the former step judges that the transfusion leakage occurs, an alarm is triggered to inform medical staff, otherwise, the normal transfusion is displayed.
In conclusion, the identification system for the infusion leakage provided by the invention can judge whether the infusion leakage occurs or not in time and inform medical staff to take corresponding actions through the alarm device. The invention has the advantages of convenient use, small size, flexibility, easy popularization and high cost performance.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to examples, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (10)

1. An infusion leak identification system, comprising:
the image acquisition module is used for acquiring a first image and a second image; the first image and the second image respectively correspond to a hand image of the same person before infusion and a hand image during infusion;
and the analysis and identification module is used for judging whether infusion leakage occurs according to the first image and the second image.
2. The infusion leak identification system of claim 1, further comprising: an alarm module;
after the analysis and identification module judges that infusion leakage occurs, the analysis and identification module sends alarm information to the alarm module, and the alarm module is used for giving an alarm according to the alarm information.
3. The infusion leak identification system according to claim 1 or 2, further comprising: a display module;
the display module is used for displaying the first image and the second image;
or the display module is used for displaying the alarm information;
or the display module is used for displaying the alarm information, the first image and the second image.
4. The infusion leak identification system of claim 1, further comprising: a key module;
the key module is used for keying in at least one item of information of infusion number information and image acquisition command information.
5. The infusion leak identification system according to claim 1, wherein the image acquisition module comprises:
the image receiving module is used for acquiring the first image and the second image through shooting;
and the image preprocessing module is used for preprocessing the first image and the second image, and the preprocessing comprises the step of normalizing the brightness and the contrast of the images.
6. The infusion leak identification system according to claim 5, wherein the normalization of the brightness and contrast of the image is performed according to the following formula:
Figure FDA0002468349480000021
wherein x is original image pixel information, y is normalized image pixel information, and μxRepresenting the mean value, σ, of the luminance of the pixels of the original imagexRepresenting the standard deviation of the brightness of the pixels of the original image;
Figure FDA0002468349480000022
Figure FDA0002468349480000023
in the formula, xiAnd the pixel value of the ith pixel point of the image is represented, and N represents the number of the pixel points of the image.
7. The identification system of infusion leakage according to claim 1, wherein the analysis and identification module obtains the pre-infusion feature vector corresponding to the first image and the infusion-time feature vector corresponding to the second image based on a convolutional neural network, and determines whether infusion leakage occurs according to a difference between the pre-infusion feature vector and the infusion-time feature vector.
8. The infusion leak identification system according to claim 7, wherein the convolutional neural network comprises: the device comprises a convolution layer, a pooling layer, a first full-connection layer and a second full-connection layer;
the convolution layer and the pooling layer are used for acquiring high-level semantic features of the image;
the first full-connection layer is used for summarizing the characteristics of the images and generating the characteristic vector before infusion and the characteristic vector during infusion;
and the second full-connection layer is used for comparing the characteristic vector before infusion and the characteristic vector during infusion and judging whether infusion leakage occurs or not.
9. The infusion leakage recognition system according to claim 1, wherein the image acquisition module employs an Open-MV camera, and the analysis and recognition module employs an embedded GPU development board.
10. The infusion leak identification system of claim 9, further comprising: an SD card;
the SD card is used for storing a Linux system and an AI environment configuration file of the embedded GPU development board;
or the SD card is used for storing the Linux system and the AI environment configuration file of the embedded GPU development board, the first image and the second image.
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