CN111524124A - Digestive endoscopy image artificial intelligence auxiliary system for inflammatory bowel disease - Google Patents
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Abstract
The invention relates to an artificial intelligence auxiliary system for digestive endoscopy images of inflammatory bowel diseases, which belongs to the technical field of medical equipment and comprises an image input module, a signal processing module and a signal processing module, wherein the image input module is used for inputting digestive endoscopy images of intestinal tracts; preprocessing and randomly sequencing the images to generate a training image set; the model construction module is used for constructing a neural network model and training the model based on a training image set; the test model module is used for testing the accuracy of the model; the identification and classification module is used for identifying and classifying the acquired enteroscope image focus to be diagnosed; and the user exchange module is used for forming an auxiliary diagnosis result according to the analysis result of the enteroscope image lesion information and enabling a doctor to confirm, modify or input a medical order so as to form a diagnosis report. The invention utilizes artificial intelligence AI technology, promotes the clinical application value of AI intelligence, helps clinicians quickly, efficiently and accurately identify and diagnose inflammatory bowel diseases under digestive endoscopy, and reduces the missed diagnosis rate of related diseases.
Description
Technical Field
The invention belongs to the technical field of medical equipment, and relates to an artificial intelligence auxiliary system for digestive endoscopy images of inflammatory bowel diseases.
Background
In recent years, the application of artificial intelligence technology in the medical field is becoming more and more extensive. In enteroscopy, artificial intelligence based on deep learning is mainly applied to aspects such as enteroscopy lesion image processing, enteroscopy polyp, intestinal examination quality evaluation and the like, but all of the artificial intelligence relate to classification and diagnosis evaluation of inflammatory diseases and are included in research. Inflammatory bowel Disease is a common Disease of the digestive system, and mainly includes Crohn's Disease (CD), Ulcerative Colitis (UC). The enteroscopy is an important means for determining diagnosis of the intestinal inflammatory diseases, but the enteroscopy of the enteroscopy and the enteroscopy are diversified and difficult to identify, so that the enteroscopy becomes a key problem which puzzles medical staff to improve diagnosis and treatment level of inflammatory bowel diseases and needs to be solved urgently for treating and curing the majority of patients with the intestinal inflammatory diseases. An Artificial Intelligence (AI) technology is combined with machine learning through an AI analysis technology, and compared with a conventional image diagnosis method, the developed image diagnosis method has higher accuracy and predictability, can greatly improve the diagnosis and treatment level of medical services, and improves the prognosis of patients. Therefore, the realization of a diagnostic system for assisting the classification of inflammatory diseases has very important practical significance for the discovery and identification of diseases.
Disclosure of Invention
In view of the above, the present invention aims to provide an efficient and accurate auxiliary inflammatory bowel disease diagnosis and treatment system.
In order to achieve the purpose, the invention provides the following technical scheme:
an artificial intelligence auxiliary system for digestive endoscopy images of intestinal inflammatory lesions comprises an image input module, a training image set and a data processing module, wherein the image input module is used for inputting an intestinal digestive endoscopy image, preprocessing and randomly sequencing the image and generating a training image set; the model construction module is used for constructing a neural network model and training the model based on a training image set; the test model module is used for testing the accuracy of the model; the identification and classification module is used for identifying and classifying the acquired enteroscope image focus to be diagnosed; and the user exchange module is used for forming an auxiliary diagnosis result according to the analysis result of the enteroscope image lesion information and enabling a doctor to confirm, modify or input a medical order so as to form a diagnosis report.
Further, the image preprocessing and sorting module includes cropping extraneous portions, changing the size of the image, and labeling. And the cutting irrelevant part is that black frames are cut off manually by software, and the cut data set is divided into two folders. This format was renamed to CD0, cd1. this format was read by a function, and the pictures were changed to 224 x 224 size for input into the model, all in a folder, with only a primary directory. Images with names containing "CD" are labeled with [1,0] and images with names containing "UC" are labeled with [0,1 ].
Further, the model building module is used for identifying the image features by using medical Cnn2 deep learning of the images, classifying the images, wherein the CD images are identified as 1, the UC images are identified as 0, all data sets are divided, 70% of the images are used for training, and the lesion images obtained by the enteroscopy image lesion classification module are segmented and identified and classified.
Further, the test model module is used for testing the accuracy of the training module, and 30% of images are used for testing the accuracy of the medical Cnn2 model.
Further, the identification and classification module is used for acquiring an image of the endoscope to be diagnosed, preprocessing the image into a standard image to be diagnosed, and then classifying and identifying the disease type.
Further, the user exchange module is used for converting the diagnosis result obtained by classifying and identifying the lesion area into characters for outputting by the image to be diagnosed to form a diagnosis result; and the manual input module is used for determining the auxiliary diagnosis result by the doctor.
The invention has the beneficial effects that: the invention automatically classifies and identifies the lesion area by using an artificial intelligence AI technology, realizes an auxiliary intestinal inflammatory lesion diagnosis system, and is efficient and accurate.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a frame diagram of an artificial intelligence auxiliary system for digestive endoscopy imaging of inflammatory bowel disease;
FIG. 2 is enteroscopy image pre-processing;
FIG. 3 is a classification, random numbering of enteroscopy images after processing;
fig. 4 is an analysis of endoscopic images of inflammatory bowel disease using the medically cnnv2 model neural network;
FIG. 5 is the accuracy of the classification model;
FIG. 6 is a ROC curve for a classification model;
fig. 7-8 are diagnostic result outputs.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
The invention provides an artificial intelligence auxiliary system for digestive endoscopy image of inflammatory bowel disease, which has the following general research thought:
the method comprises the following steps of establishing several main parts of an artificial intelligence auxiliary diagnosis system in medical imaging:
firstly, performing advanced processing on an acquired image of the case characteristics of the reactive inflammatory bowel disease, and cutting the size of the image and randomly numbering the image;
secondly, extracting the characteristics of the colon inflammatory lesion image, wherein the screening of the colon inflammatory lesion signs obtained in the previous part is mainly completed in the part, and the lesion signs mainly refer to medical characteristics that a lesion part can be distinguished and distinguished, such as the shape, distribution, inflammation expression characteristics and the like of the lesion;
thirdly, classification and identification, which mainly completes the step of sending the lesion signs obtained in the previous part to a medical Cnn2 neural network classifier to form a decision-making system,
and fourthly, diagnosis application, namely inputting the processed enteroscopy images to be identified into a system, and giving a diagnosis result according to classification by the system.
As shown in fig. 1, in combination with the present framework, in the first step, the digestive endoscopy pictures of inflammatory bowel disease were pre-processed and randomly ordered; the second part inputs 70% of processed images as data into a medical Cnn2 model for training and tests 30% of images to finish the establishment of the medical Cnn2 model; and the third part is used for identifying and classifying the image input system to be diagnosed, giving a diagnosis result and outputting a diagnosis report.
In the image preprocessing process of the present study, the black border is manually cut off by software, and the cut data set is divided into two folders, as shown in fig. 2.
Fig. 3 shows that all pictures under a root directory are read by a function, which is renamed to CD0, cd1. Images with names containing "CD" are labeled with [1,0] and images with names containing "UC" are labeled with [0,1 ].
As shown in fig. 4, all the images described above are divided into data sets:
#split into 70％for train and 30％for test
x _ train, x _ test, y _ train, y _ test _ train _ test _ split (train x _ rgb, train y _ rgb, test _ size 0.3, random _ state) and a medical cnn2 model building process is performed. Digestive endoscopy images of inflammatory bowel disease were analyzed using a MedicalCnV 2 neural network. 70% of the data in the collected images were randomly selected for model training and model building, and the remaining 30% were used for model testing.
As shown in fig. 5, the test results show that the recognition rate of UC and CD reaches 90.98%. Among accuracy indexes of the classification model, sensitivity/recall ratio/true positive ratio: 81.20%, misdiagnosis rate/false positive rate: 5.11%, leak diagnosis rate/false negative rate: 18.8%, specificity/true negative rate: 94.89 percent.
FIG. 6 shows the ROC curve of the classification model in 1347 samples of the validation set in this study, wherein the AUC value reached 0.94.
As shown in fig. 7 to 8, the diagnosis result shows that a diagnosis report is outputted after the enteroscope image to be diagnosed is inputted.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will 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, and all of them should be covered by the claims of the present invention.
Claims (6)
1. The utility model provides an artificial intelligence auxiliary system of inflammatory bowel disease digestive endoscopy image which characterized in that:
the image input module is used for inputting an intestinal tract digestive endoscopy image, preprocessing and randomly sequencing the image and generating a training image set;
the model construction module is used for constructing a neural network model and training the model based on a training image set;
the test model module is used for testing the accuracy of the model;
the identification and classification module is used for acquiring identification and classification of a lesion of an enteroscope image to be diagnosed;
and the user exchange module is used for forming an auxiliary diagnosis result according to the analysis result of the enteroscope image lesion information, and forming a diagnosis report after confirming, modifying or inputting a medical order.
2. The digestive endoscopy image artificial intelligence auxiliary system for inflammatory bowel disease according to claim 1, characterized in that: the image input module is used for preprocessing and randomly sequencing the images, and specifically comprises cutting irrelevant parts, changing the size of the images and labeling;
the cutting irrelevant part is to cut off a black frame by software, and the cut data set is divided into two folders;
reading all pictures under a root directory by using a function, and renaming the pictures to be CD0 and CD1, modifying the pictures to be 224 × 224 in size so as to be convenient for inputting the pictures into the model, wherein all the pictures are placed in a folder and only have a primary directory;
and finally, marking a [1,0] label on the image with the name containing the CD, and marking a [0,1] label on the image with the name containing the UC.
3. The digestive endoscopy image artificial intelligence auxiliary system for inflammatory bowel disease according to claim 1, characterized in that: the model building module is used for carrying out deep learning and identification on image characteristics of the images by using medical Cnn2, classifying the images, wherein the CD image is identified as 1, the UC image is identified as 0, all data sets are divided, 70% of images are used for training, and the lesion images obtained by the enteroscope image lesion classification module are segmented and classified.
4. The digestive endoscopy image artificial intelligence auxiliary system for inflammatory bowel disease according to claim 1, characterized in that: the test model module is used for testing the accuracy of the training module, and 30% of images are used for testing the accuracy of the medical Cnn2 model.
5. The digestive endoscopy image artificial intelligence auxiliary system for inflammatory bowel disease according to claim 1, characterized in that: and the identification and classification module is used for acquiring the images of the colonoscopy to be diagnosed, preprocessing the images into standard images to be diagnosed and then classifying and identifying the types of diseases.
6. The digestive endoscopy image artificial intelligence auxiliary system for inflammatory bowel disease according to claim 1, characterized in that: the user exchange module is used for converting the diagnosis result obtained by classifying and identifying the lesion area into characters for outputting by the image to be diagnosed, forming a diagnosis result and determining an auxiliary diagnosis result.
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| CN113793666B (en) * | 2021-09-16 | 2023-10-27 | 中国人民解放军空军军医大学 | Method and system for processing compound mode neuron information |
| CN116153147A (en) * | 2023-02-28 | 2023-05-23 | 中国人民解放军陆军特色医学中心 | 3D-VR binocular stereo vision image construction method and endoscopic operation teaching device |
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