Image detection method and system for alimentary tract hemorrhage

Abstract

The invention discloses an image detection method and system for alimentary tract hemorrhage, and relates to the technical field of image detection.Based on source data of a to-be-detected user, the use data of a capsule detection terminal in a data bin are combined; the overall matching value of the to-be-detected user and each user is evaluated from the two aspects of physical fitness and gastrointestinal peristalsis, so that the detected body position posture change set of the to-be-detected user is determined, the accuracy is high, the posture correctness of the to-be-detected user when the to-be-detected user uses the capsule detection terminal to perform digestive tract hemorrhage examination is guaranteed, and the accuracy of the detection result is improved. It is ensured that the capsule detection terminal smoothly moves in the digestive tract, and the detection effect of the capsule detection terminal is improved; according to the invention, based on the operation data of the capsule detection terminal of the to-be-detected user, whether the current capsule detection terminal has an image defect or has an over-high speed problem is judged, the influence on subsequent recognition and judgment of alimentary canal hemorrhage of the to-be-detected user is avoided, and the detection effect of the to-be-detected user is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of image detection, and in particular to an image detection method and system for gastrointestinal bleeding. Background Art

[0002] Gastrointestinal bleeding is a common and potentially life-threatening disease, and its diagnosis and treatment need to be rapid and accurate. Image detection methods play a vital role in the diagnosis of gastrointestinal bleeding. They can not only provide intuitive visual information, but also help doctors accurately determine the location and cause of bleeding, thereby formulating effective treatment plans. Capsule detection terminals are increasingly widely used in the detection of gastrointestinal bleeding due to their small size and non-invasive characteristics. However, if the capsule detection terminal is used improperly, it will not only affect the patient's detection efficiency, but also cause a waste of medical resources. Therefore, it is extremely necessary to analyze the use of capsule detection terminals.

[0003] The prior art, such as the invention application patent with announcement number: CN106373137B, discloses a method for detecting gastrointestinal bleeding images for capsule endoscopes, which includes the following steps: Step 1: After the patient swallows the capsule endoscope, the capsule endoscope collects images in the digestive tract and sends the images to the computer through wireless communication, and the computer performs data preprocessing on the digestive tract images. Step 2: Perform spot bleeding detection and surface bleeding detection on the preprocessed digestive tract images to determine whether there is bleeding in the digestive tract, and mark the spot bleeding area and surface bleeding area. The invention can effectively identify image bleeding and improve the work efficiency of doctors.

[0004] Prior art, such as the invention application patent with announcement number CN115251806B, discloses a medical equipment monitoring, analysis and control system based on magnetic field principle technology, including: a module for analyzing the rationality of the target person's examination suit wearing, a module for the autonomous operation of the capsule detection terminal, a module for analyzing the rationality of the capsule residence time, a module for analyzing the power safety of the capsule detection terminal, a database and an early warning terminal. The invention classifies the collected stomach images according to their structures, and then analyzes the set of stomach images of each component structure of the stomach, thereby effectively ensuring the accuracy of the gastric injury analysis of the medical personnel. The invention performs secondary image acquisition on the damaged part of the stomach, reduces the deviation rate of the judgment result, and thus ensures the accuracy of the judgment of the gastric condition of the medical personnel. The invention not only analyzes the time the capsule detection terminal stays in the body of the medical personnel, but also analyzes the power safety of the capsule detection terminal. The analysis result is relatively comprehensive, which is conducive to ensuring the safety of the medical personnel.

[0005] Based on the above technical solution, it is found that there are still certain defects in the use analysis of capsule detection terminals in the prior art, which are specifically reflected in the following aspects: on the one hand, the prior art does not pay much attention to the changes in the detection posture of the user to be detected. Due to the differences in physical fitness and intestines, the users to be detected have certain differences in their acceptance of the detection posture. The prior art lacks analysis at this level, and it is difficult to ensure the correctness of the posture of the user to be detected when using the capsule detection terminal for gastrointestinal bleeding examination, which in turn affects the movement of the capsule detection terminal in the gastrointestinal tract and reduces the detection effect of the capsule detection terminal. On the other hand, there are few warnings for abnormalities during the use of the capsule detection terminal. When using the capsule detection terminal, it is easy to have problems with defects in the transmitted image or too fast speed. If it is not warned in time and the responsible doctor intervenes, it will affect the subsequent identification and determination of gastrointestinal bleeding of the user to be detected, and it is difficult to ensure the detection effect of the user to be detected. Summary of the invention

[0006] The purpose of the present invention is to provide an image detection method and system for gastrointestinal bleeding, which solves the problems existing in the background technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The first aspect of the present invention provides an image detection method for gastrointestinal bleeding, including ST1. Obtaining the vital sign data of the user to be detected from the hospital operation platform, determining whether the user to be detected needs to undergo target gastrointestinal bleeding detection, and if necessary, executing ST2.

[0008] ST2. Obtain the original data of the user to be tested from the hospital operation platform, and combine it with the capsule detection terminal usage data in the data warehouse to determine the detection posture transformation set and key detection time point set of the user to be tested.

[0009] ST3. After the user to be detected takes the capsule detection terminal, the posture of the user to be detected is adjusted by voice broadcast, and the operation data of the capsule detection terminal of the user to be detected is obtained.

[0010] ST4. Based on the operation data of the capsule detection terminal of the user to be detected, the responsible doctor of the user to be detected is warned.

[0011] ST5. Extract the image set of the capsule detection terminal of the user to be detected, evaluate and obtain the bleeding areas and risk values ​​of the bleeding areas in the area to be detected, and display them.

[0012] The second aspect of the present invention provides a system for executing the image detection method as described in the present invention, including: a target gastrointestinal bleeding detection judgment module, which is used to obtain the vital sign data of the user to be detected from the hospital operation platform, determine whether the user to be detected needs to undergo target gastrointestinal bleeding detection, and if necessary, execute the detection parameter determination module of the user to be detected.

[0013] The detection parameter determination module for the user to be detected is used to obtain the original data of the user to be detected from the hospital operation platform, and combine it with the capsule detection terminal usage data in the data warehouse to determine the detection posture transformation set and key detection time point set of the user to be detected.

[0014] The detection module is used to adjust the body posture of the user to be detected and make a voice announcement after the user to be detected wears the capsule detection terminal, and obtain the operation data of the capsule detection terminal of the user to be detected.

[0015] The early warning terminal is used to warn the doctor responsible for the user to be tested based on the operating data of the capsule detection terminal of the user to be tested.

[0016] The display terminal is used to extract an image set of a capsule detection terminal of a user to be detected, evaluate and obtain each bleeding area and the risk value of each bleeding area in the area to be detected, and display them.

[0017] The beneficial effect of the present invention is that the present invention determines whether the user to be detected needs to use a capsule detection terminal for gastrointestinal bleeding detection based on the vital sign data of the user to be detected in ST1, laying a foundation for the subsequent analysis of the detection posture of the user to be detected.

[0018] In ST2, the present invention evaluates the overall matching value between the user to be detected and each user from the two levels of physical fitness and gastrointestinal motility based on the original data of the user to be detected and combined with the capsule detection terminal usage data in the data warehouse, thereby determining the detection posture transformation set of the user to be detected with high accuracy, which makes up for the defect of neglecting this level in the prior art, ensures the correctness of the posture of the user to be detected when using the capsule detection terminal for gastrointestinal bleeding examination, ensures the smooth movement of the capsule detection terminal in the digestive tract, and improves the detection effect of the capsule detection terminal.

[0019] In ST5, the present invention determines whether the current capsule detection terminal has image defects or too fast speed problems based on the operating data of the capsule detection terminal of the user to be detected, and warns the responsible doctor of the user to be detected, so as to avoid affecting the subsequent identification and judgment of gastrointestinal bleeding of the user to be detected, and ensure the detection effect of the user to be detected. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method.

[0022] Figure 2 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Reference Figure 1 As shown, the first aspect of the present invention provides an image detection method for gastrointestinal bleeding, comprising: ST1. Acquire the vital sign data of the user to be detected from the hospital operation platform, determine whether the user to be detected needs to undergo target gastrointestinal bleeding detection, and if so, execute ST2.

[0025] It should be noted that the target gastrointestinal bleeding detection is to use a capsule detection terminal to perform gastrointestinal bleeding detection.

[0026] As a preferred solution, the physical sign data includes a symptom description text set, blood pressure, heart rate, respiratory rate, facial image set and characteristic values ​​of various examination indicators.

[0027] It should be noted that the characteristic values ​​of the above-mentioned examination indicators include hemoglobin concentration, red blood cell count, hematocrit of routine blood examination, prothrombin time, activated partial thromboplastin time, fibrinogen, etc. of coagulation function examination, and blood urea nitrogen concentration of blood urea nitrogen examination.

[0028] It should also be noted that the facial set is specifically extracted from the monitoring of the hospital operation platform.

[0029] The original data include characteristic values ​​of various basic data, gastrointestinal activity fundamental frequency, esophageal distribution pressure map, anorectal distribution pressure map, fecal samples, small intestinal transit rate, activity level characteristic values, and historical pathological data.

[0030] It should be noted that the characteristic values ​​of the basic data include height, age, body fat percentage, etc. The activity fundamental frequency of the gastrointestinal tract specifically refers to the frequency of bowel sounds listened to by palpation of the abdomen. The esophageal distribution pressure map and the anorectal distribution pressure map are specifically obtained by measuring the pressure of each section of the digestive tract through esophageal manometry and anorectal manometry, and drawing the esophageal distribution pressure map and the anorectal distribution pressure map. The small intestinal transit rate is specifically inferred by monitoring the changing trend of the hydrogen level in the user's breath over time. The activity level characteristic value specifically refers to the user's daily exercise volume. The activity level characteristic value is a value between 0 and 1. The larger the activity level characteristic value, the greater the user's daily exercise volume. The user's responsible doctor will make a judgment and upload it.

[0031] It should also be noted that the small intestinal transport rate is inferred based on the changing trend of the hydrogen level in the user's breath over time. The small intestinal transmission rate can be inferred based on the time point and peak value of the increase in hydrogen concentration. Usually, the time point when the hydrogen concentration increases significantly reflects the time when the marker reaches the colon, thereby indirectly reflecting the small intestinal transmission rate. This method is a common means in the prior art and will not be elaborated here.

[0032] In ST1, the present invention determines whether the user to be detected needs to use a capsule detection terminal for gastrointestinal bleeding detection based on the vital sign data of the user to be detected, which lays a foundation for the subsequent analysis of the detection posture of the user to be detected.

[0033] ST2. Obtain the original data of the user to be tested from the hospital operation platform, and combine it with the capsule detection terminal usage data in the data warehouse to determine the detection posture transformation set and key detection time point set of the user to be tested.

[0034] As a preferred solution, the method for determining whether the user to be detected needs to undergo target gastrointestinal bleeding detection is as follows: inputting the symptom description text set A, blood pressure X, heart rate P and respiratory rate H in the physical sign data of the user to be detected into the first abnormality assessment model , output the first abnormal value of the user to be detected, where A' is a text set of descriptions of gastrointestinal bleeding symptoms preset in the data warehouse, , , XI', PI', HI' respectively represent the required blood pressure interval, required heart rate interval and required respiratory rate interval corresponding to the target gastrointestinal bleeding detection preset in the data warehouse.

[0035] Based on the facial image set of the user to be detected, the second outlier value of the user to be detected is evaluated .

[0036] The characteristic values ​​of each inspection index of the vital sign data of the user to be detected Input to the third anomaly assessment model , output the third abnormal value of the user to be detected, where is the required characteristic value interval of the pth examination index corresponding to the target gastrointestinal bleeding detection preset in the data warehouse, is the number of each inspection indicator, , is any integer greater than 2.

[0037] Import the first abnormal value, the second abnormal value and the third abnormal value of the user to be detected into the target gastrointestinal bleeding detection and judgment model ,in , , are represented by the preset first abnormal convergence value, the second abnormal convergence value and the third abnormal convergence value, respectively. , They are respectively represented by the logical symbols or and, and output the target gastrointestinal bleeding detection judgment value of the user to be detected. The target gastrointestinal bleeding detection judgment value includes the values ​​of 1 and -1. When the target gastrointestinal bleeding detection judgment value is 1, it indicates that the user to be detected needs to perform target gastrointestinal bleeding detection. When the target gastrointestinal bleeding detection judgment value is -1, it indicates that the user to be detected does not need to perform target gastrointestinal bleeding detection.

[0038] It should be noted that the present invention determines whether the user to be tested is suitable for gastrointestinal bleeding detection in the form of a capsule detection terminal. On the one hand, it avoids the use of a capsule detection terminal for detection when the risk of gastrointestinal bleeding is high, which may affect the timely treatment of the user to be tested. On the other hand, it alleviates the pressure of insufficient medical resources in hospitals.

[0039] As a preferred solution, the second abnormal value of the user to be detected is evaluated by a specific evaluation method: based on the facial image set of the user to be detected, the chromaticity values ​​of key parts of several facial images are identified by image recognition technology. , and identify the temperature values ​​of several facial images , is the number of each facial image, , is any integer greater than 2, is the number of each key part, , is any integer greater than 2.

[0040] It should be noted that the specific identification method for identifying the temperature values ​​of several facial images is: identifying several chromaticity values ​​of several facial images through image recognition technology, obtaining the average chromaticity value of the several facial images, and comparing it with the chromaticity value intervals corresponding to each temperature value preset in the data warehouse; if the average chromaticity value of a facial image is within the chromaticity value interval corresponding to a certain temperature value, then the temperature value is used as the temperature value of the facial image to obtain the temperature values ​​of the several facial images.

[0041] Evaluate the second outlier value of the user to be detected , ,in , They are respectively represented by the preset facial temperature value of gastrointestinal bleeding and the reference chromaticity value of the jth key part, and k is the number of key parts.

[0042] It should be noted that when gastrointestinal bleeding occurs, the temperature of the patient's facial skin usually drops, and the skin in key areas usually becomes pale. Therefore, it is necessary to analyze the patient's facial skin temperature and the chromaticity values ​​of key areas, and to analyze the patient's risk change rate in order to reduce the misjudgment rate.

[0043] As a preferred solution, the detection posture transformation set and the key detection time point set of the user to be detected are determined by the following specific determination method: extracting the characteristic values ​​of each basic data, the characteristic values ​​of the activity level and the historical pathological data from the original data of the user to be detected, obtaining the capsule detection terminal usage data from the data warehouse, wherein the capsule detection terminal usage data includes the original data, the detection posture transformation set and the key detection time point set of each user, and extracting the characteristic values ​​of each basic data, the characteristic values ​​of the activity level and the historical pathological data of each user, and evaluating the physical fitness matching value between the user to be detected and each user.

[0044] The gastrointestinal activity fundamental frequency, esophageal distribution pressure map, anorectal distribution pressure map, fecal samples and small intestinal transit rate are extracted from the original data of the user to be tested, and the gastrointestinal activity fundamental frequency, esophageal distribution pressure map, anorectal distribution pressure map, fecal samples and small intestinal transit rate are extracted from the original data of each user to evaluate the gastrointestinal motility matching value of the user to be tested and each user.

[0045] Import the physical fitness matching value and gastrointestinal motility matching value of the user to be tested and each user into the overall matching value evaluation model middle, is the ID of each user, , is any integer greater than 2, , They are respectively represented as the weight influence factors corresponding to the preset physical fitness matching and gastrointestinal motility matching, and the overall matching value between the user to be detected and each user is output. If the overall matching value between the user to be detected and a certain user is the largest, the detection posture transformation set and key detection time point set of the user are respectively used as the detection posture transformation set and key detection time point set of the user to be detected.

[0046] It should be noted that the preset weight influence factors corresponding to the physical fitness matching and gastrointestinal motility matching are set by medical experts according to specific circumstances. For example, if clinical findings show that the gastrointestinal motility matching has a greater impact on the detected body posture, the weight influence factor corresponding to the gastrointestinal motility matching is set to be greater than the weight influence factor of the physical fitness matching.

[0047] As a preferred solution, the physical fitness matching value of the user to be tested and each user is evaluated by: extracting the emotion set of each disease type and each examination based on the historical pathological data of the user to be tested , the characteristic values ​​of each physiological data , construct a historical disease type set of the user to be tested, and evaluate the risk feature value of the user's examination emotion ,in , They are respectively represented as the preset risk emotion set and the safety characteristic value interval of the xth physiological data. is the number of each inspection, , is any integer greater than 2, is the number of each physiological data, , is any integer greater than 2, is the total number of examinations. Similarly, a set of historical disease types of the users to be tested is constructed, and the examination risk characteristic value of each user is evaluated. .

[0048] It should be noted that the risk emotion set includes panic, anxiety, depression, etc.

[0049] Specifically, the emotion set is constructed by collecting the user's facial video and then identifying various emotions of the user, thereby obtaining the emotion set of each inspection of the user to be inspected.

[0050] The characteristic values ​​of each basic data of the user to be detected , activity level characteristic value , Historical disease type collection , Check emotional risk feature value and the characteristic values ​​of each user's basic data , activity level characteristic value , Historical disease type collection , Check emotional risk feature value Import into the physical literacy matching value assessment model In the output, the physical fitness matching value of the user to be detected and each user is output. is the number of each basic data, , is any integer greater than 2.

[0051] As a preferred solution, the gastrointestinal motility matching value of the user to be detected and each user is evaluated by a specific calculation method: based on the esophageal distribution pressure map, anorectal distribution pressure map, excrement sample of the user to be detected and the esophageal distribution pressure map, anorectal distribution pressure map, excrement sample of each user, the internal distribution pressure matching degree of the user to be detected and each user is evaluated. , intestinal flora matching .

[0052] It should be noted that the specific evaluation method for evaluating the internal distribution pressure matching degree between the user to be detected and each user is as follows: the esophageal distribution pressure map of the user to be detected and the esophageal distribution pressure map of each user are output through image processing software to obtain the similarity between the esophageal distribution pressure map of the user to be detected and each user; similarly, the similarity between the anorectal distribution pressure map of the user to be detected and each user is output, and the similarity between the esophageal distribution pressure map of the user to be detected and each user and the similarity between the anorectal distribution pressure map are averaged to obtain the internal distribution pressure matching degree between the user to be detected and each user.

[0053] It should also be noted that the specific evaluation method for evaluating the intestinal flora matching degree between the user to be detected and each user is as follows: based on the excrement sample of the user to be detected, various characteristic parameters of the excrement of the user to be detected are obtained, and based on the excrement sample of each user, various characteristic parameters of the excrement of each user are obtained, and the various characteristic parameters of the excrement of each user are specifically recorded successfully during historical use and uploaded to the data warehouse, and the intestinal flora matching degree between the user to be detected and each user is obtained by evaluating the similarity of the various characteristic parameters of the excrement of the user to be detected and each user, and performing mean processing on them. The similarity of evaluating the various characteristic parameters of the excrement of the user to be detected and each user can be obtained by numerical similarity evaluation in the prior art, which will not be elaborated here.

[0054] It should be noted again that the characteristic parameters include chromaticity value, hardness, pH value, etc.

[0055] Evaluate the gastrointestinal motility matching value between the user to be tested and each user ,in , are the activity base frequency of the gastrointestinal tract and the small intestinal transit rate of the user to be tested, , are the gastrointestinal activity fundamental frequency and small intestinal transit rate of the f-th user respectively.

[0056] In ST2, the present invention evaluates the overall matching value between the user to be detected and each user from the two levels of physical fitness and gastrointestinal motility based on the original data of the user to be detected and combined with the capsule detection terminal usage data in the data warehouse, thereby determining the detection posture transformation set of the user to be detected with high accuracy, which makes up for the defect of neglecting this level in the prior art, ensures the correctness of the posture of the user to be detected when using the capsule detection terminal for gastrointestinal bleeding examination, ensures the smooth movement of the capsule detection terminal in the digestive tract, and improves the detection effect of the capsule detection terminal.

[0057] ST3. After the user to be detected takes the capsule detection terminal, the posture of the user to be detected is adjusted by voice broadcast, and the operation data of the capsule detection terminal of the user to be detected is obtained.

[0058] The operation data includes the current output image and the current shooting duration.

[0059] ST4. Based on the operation data of the capsule detection terminal of the user to be detected, the responsible doctor of the user to be detected is warned.

[0060] As a preferred solution, the responsible doctor of the user to be tested is warned, and the specific method is: obtaining the current output image and current shooting duration of the capsule detection terminal of the user to be tested from the operation data of the capsule detection terminal of the user to be tested, and identifying the defect existence status of the current output image of the capsule detection terminal of the user to be tested, and the defect existence status includes existence and non-existence.

[0061] It should be noted that the current shooting duration of the capsule detection terminal of the user to be detected is specifically the interval between the time point of the current output image and the time point of the previous output image.

[0062] It should also be noted that the identification of the defect existence status of the current output image of the capsule detection terminal of the user to be detected is specifically to identify the defects of the current output image of the capsule detection terminal of the user to be detected through image recognition technology. If a defect exists, the defect existence status is recorded as existing, otherwise, the defect existence status is recorded as not existing, and the defects include blur, noise and low contrast.

[0063] If the defect status of the current output image of the capsule detection terminal of the user to be detected is present, the responsible doctor of the user to be detected will be warned of abnormal image quality; if the current shooting time of the capsule detection terminal of the user to be detected is less than the shooting interval preset in the data warehouse, the responsible doctor of the user to be detected will be warned of abnormal shooting speed.

[0064] In ST5, the present invention determines whether the current capsule detection terminal has image defects or too fast speed problems based on the operating data of the capsule detection terminal of the user to be detected, and warns the responsible doctor of the user to be detected, so as to avoid affecting the subsequent identification and judgment of gastrointestinal bleeding of the user to be detected, and ensure the detection effect of the user to be detected.

[0065] ST5. Extract the image set of the capsule detection terminal of the user to be detected, evaluate and obtain the bleeding areas and risk values ​​of the bleeding areas in the area to be detected, and display them.

[0066] As a preferred solution, the evaluation obtains each bleeding area of ​​the user to be detected and the risk value of each bleeding area. The specific evaluation method is: extract a number of images from the image set of the capsule detection terminal of the user to be detected, and divide them into grids to obtain each sub-image of each image, obtain the chromaticity value of each sub-image of each image, and then compare it with the bleeding chromaticity value interval preset in the data warehouse. If the chromaticity value of a sub-image is within the bleeding chromaticity value interval, then the sub-image is recorded as a risk sub-image, and each risk sub-image of each image is obtained by screening, and the number of risk sub-images and the number of sub-images of each image of the capsule detection terminal of the user to be detected are counted.

[0067] If the number of risk sub-images of each image of the capsule detection terminal of the user to be detected is 0, the image is recorded as a normal image. Otherwise, the image is recorded as a bleeding image, and the detection area corresponding to the image is recorded as a bleeding area. The number of risk sub-images of the image is divided by the number of sub-images to obtain the risk value of the bleeding area, thereby obtaining each bleeding area of ​​the user to be detected and the risk value of each bleeding area.

[0068] It should be noted that the detection area corresponding to the image is specifically: during the operation of the capsule detection terminal, the locator of the capsule detection terminal can locate the actual detection area of ​​the output image.

[0069] Reference Figure 2 As shown, the second aspect of the present invention provides a system for executing the image detection method according to the present invention, comprising:

[0070] The target gastrointestinal bleeding detection judgment module is used to obtain the vital sign data of the user to be detected from the hospital operation platform, determine whether the user to be detected needs to undergo target gastrointestinal bleeding detection, and if necessary, execute the detection parameter determination module of the user to be detected.

[0071] The detection parameter determination module for the user to be detected is used to obtain the original data of the user to be detected from the hospital operation platform, and combine it with the capsule detection terminal usage data in the data warehouse to determine the detection posture transformation set and key detection time point set of the user to be detected.

[0072] The detection module is used to adjust the body posture of the user to be detected and make a voice announcement after the user to be detected wears the capsule detection terminal, and obtain the operation data of the capsule detection terminal of the user to be detected.

[0073] The early warning terminal is used to warn the doctor responsible for the user to be tested based on the operating data of the capsule detection terminal of the user to be tested.

[0074] The display terminal is used to extract an image set of a capsule detection terminal of a user to be detected, evaluate and obtain each bleeding area and the risk value of each bleeding area in the area to be detected, and display them.

[0075] It should be noted that the present invention also includes a data warehouse for storing a text set describing gastrointestinal bleeding symptoms, the required blood pressure range, required heart rate range and required respiratory rate range corresponding to the target gastrointestinal bleeding detection, the required characteristic value range of each inspection indicator corresponding to the target gastrointestinal bleeding detection, the capsule detection terminal usage data, the shooting interval duration, and the bleeding chromaticity value range.

[0076] It should also be noted that the target gastrointestinal bleeding detection and judgment module is connected to the detection parameter determination module of the user to be detected, the detection parameter determination module of the user to be detected is connected to the detection module, the detection module is respectively connected to the early warning terminal and the display terminal, and the data warehouse is respectively connected to the target gastrointestinal bleeding detection and judgment module, the detection parameter determination module of the user to be detected and the early warning terminal.

[0077] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An image detection method for gastrointestinal bleeding, characterized in that: include: ST1. Obtain the vital sign data of the user to be tested from the hospital operation platform, determine whether the user to be tested needs to undergo target gastrointestinal bleeding testing, and if necessary, execute ST2; ST2. Obtain the original data of the user to be tested from the hospital operation platform, combine the capsule detection terminal usage data in the data warehouse, and determine the detection posture transformation set and key detection time point set of the user to be tested; ST3. After the user to be tested is served into the capsule detection terminal, the posture of the user to be tested is adjusted by voice broadcast, and the operation data of the capsule detection terminal of the user to be tested is obtained; ST4. Based on the operation data of the capsule detection terminal of the user to be detected, the responsible doctor of the user to be detected is warned; ST5. Extract the image set of the capsule detection terminal of the user to be detected, evaluate and obtain the bleeding areas and risk values ​​of the bleeding areas in the area to be detected, and display them.

2. The image detection method for gastrointestinal bleeding according to claim 1, characterized in that: The physical sign data includes a symptom description text set, blood pressure, heart rate, respiratory rate, facial image set and characteristic values ​​of each examination index; The original data include characteristic values ​​of basic data, gastrointestinal activity fundamental frequency, esophageal distribution pressure map, anorectal distribution pressure map, fecal samples, small intestinal transit rate, activity level characteristic values, and historical pathological data.

3. The image detection method for gastrointestinal bleeding according to claim 2, characterized in that: The specific method for determining whether the user to be detected needs to undergo target digestive tract bleeding detection is as follows: The symptom description text set A, blood pressure X, heart rate P and respiratory rate H in the physical sign data of the user to be detected are input into the first abnormality assessment model , output the first abnormal value of the user to be detected, where A' is a text set of descriptions of gastrointestinal bleeding symptoms preset in the data warehouse, , , XI', PI', HI' respectively represent the required blood pressure interval, required heart rate interval and required respiratory rate interval corresponding to the target gastrointestinal bleeding detection preset in the data warehouse; Based on the facial image set of the user to be detected, the second outlier value of the user to be detected is evaluated ; The characteristic values ​​of each inspection index of the vital sign data of the user to be detected Input to the third anomaly assessment model , output the third abnormal value of the user to be detected, where is the required characteristic value interval of the pth examination index corresponding to the target gastrointestinal bleeding detection preset in the data warehouse, is the number of each inspection indicator, , is any integer greater than 2; Import the first abnormal value, the second abnormal value and the third abnormal value of the user to be detected into the target gastrointestinal bleeding detection and judgment model ,in , , are represented by the preset first abnormal convergence value, the second abnormal convergence value and the third abnormal convergence value, respectively. , They are respectively represented by the logical symbols or and, and output the target gastrointestinal bleeding detection judgment value of the user to be detected. The target gastrointestinal bleeding detection judgment value includes the values ​​of 1 and -1. When the target gastrointestinal bleeding detection judgment value is 1, it indicates that the user to be detected needs to perform target gastrointestinal bleeding detection. When the target gastrointestinal bleeding detection judgment value is -1, it indicates that the user to be detected does not need to perform target gastrointestinal bleeding detection.

4. The image detection method for digestive tract bleeding according to claim 3, characterized in that: The specific evaluation method of evaluating the second abnormal value of the user to be detected is: Based on the facial image set of the user to be detected, the chromaticity values ​​of key parts of several facial images are identified through image recognition technology. , and identify the temperature values ​​of several facial images , is the number of each facial image, , is any integer greater than 2, is the number of each key part, , is any integer greater than 2; Evaluate the second outlier value of the user to be detected , ,in , They are respectively represented by the preset facial temperature value of gastrointestinal bleeding and the reference chromaticity value of the jth key part, and k is the number of key parts.

5. The image detection method for gastrointestinal bleeding according to claim 2, characterized in that: The specific determination method of determining the detection posture transformation set and key detection time point set of the user to be detected is: Extract the characteristic values ​​of each basic data, the characteristic values ​​of the activity level and the historical pathological data from the original data of the user to be detected, obtain the capsule detection terminal usage data from the data warehouse, wherein the capsule detection terminal usage data includes the original data of each user, the detection posture change set and the key detection time point set, and extract the characteristic values ​​of each basic data, the characteristic values ​​of the activity level and the historical pathological data of each user, and evaluate the physical fitness matching value between the user to be detected and each user; Extracting the gastrointestinal activity fundamental frequency, esophageal distribution pressure map, anorectal distribution pressure map, fecal sample and small intestinal transit rate from the original data of the user to be tested, and extracting the gastrointestinal activity fundamental frequency, esophageal distribution pressure map, anorectal distribution pressure map, fecal sample and small intestinal transit rate from the original data of each user, and evaluating the gastrointestinal motility matching value of the user to be tested and each user; Import the physical fitness matching value and gastrointestinal motility matching value of the user to be tested and each user into the overall matching value evaluation model middle, is the ID of each user, , is any integer greater than 2, , They are respectively represented as the weight influence factors corresponding to the preset physical fitness matching and gastrointestinal motility matching, and the overall matching value between the user to be detected and each user is output, and the detection posture transformation set and key detection time point set of the user to be detected are screened.

6. The image detection method for gastrointestinal bleeding according to claim 5, characterized in that: The specific evaluation method of evaluating the physical fitness matching value of the user to be detected and each user is as follows: Extract the emotion set of each disease type and each examination based on the historical pathological data of the user to be tested , the characteristic values ​​of each physiological data , construct a historical disease type set of the user to be tested, and evaluate the risk feature value of the user's examination emotion ,in , They are respectively represented as the preset risk emotion set and the safety characteristic value interval of the xth physiological data. is the number of each inspection, , is any integer greater than 2, is the number of each physiological data, , is any integer greater than 2, is the total number of examinations. Similarly, a set of historical disease types of the users to be tested is constructed, and the examination risk characteristic value of each user is evaluated. ; The characteristic values ​​of each basic data of the user to be detected , activity level characteristic value , Historical disease type collection , Check emotional risk feature value and the characteristic values ​​of each user's basic data , activity level characteristic value , Historical disease type collection , Check emotional risk feature value Import into the physical literacy matching value assessment model In the output, the physical fitness matching value of the user to be detected and each user is output. is the number of each basic data, , is any integer greater than 2.

7. The image detection method for digestive tract bleeding according to claim 5, characterized in that: The specific calculation method of evaluating the gastrointestinal motility matching value between the user to be detected and each user is as follows: Based on the esophageal distribution pressure map, anorectal distribution pressure map, excrement sample of the user to be tested and the esophageal distribution pressure map, anorectal distribution pressure map, excrement sample of each user, the internal distribution pressure matching degree of the user to be tested and each user is evaluated. , intestinal flora matching ; Evaluate the gastrointestinal motility matching value between the user to be tested and each user ,in , are the activity base frequency of the gastrointestinal tract and the small intestinal transit rate of the user to be tested, , are the gastrointestinal activity fundamental frequency and small intestinal transit rate of the f-th user respectively.

8. The image detection method for gastrointestinal bleeding according to claim 1, characterized in that: The specific method of giving an early warning to the responsible doctor of the user to be tested is as follows: Acquire a current output image and a current shooting duration of the capsule detection terminal of the user to be detected from the operation data of the capsule detection terminal of the user to be detected, and identify a defect existence state of the current output image of the capsule detection terminal of the user to be detected, wherein the defect existence state includes existence and non-existence; If the defect status of the current output image of the capsule detection terminal of the user to be detected is present, the responsible doctor of the user to be detected will be warned of abnormal image quality; if the current shooting time of the capsule detection terminal of the user to be detected is less than the shooting interval preset in the data warehouse, the responsible doctor of the user to be detected will be warned of abnormal shooting speed.

9. The image detection method for digestive tract bleeding according to claim 1, characterized in that: The evaluation obtains each bleeding area of ​​the user to be detected and the risk value of each bleeding area, and the specific evaluation method is: Extract several images from the image set of the capsule detection terminal of the user to be detected, divide them into grids, obtain sub-images of each image, obtain the chromaticity value of each sub-image of each image, and then compare it with the bleeding chromaticity value interval preset in the data warehouse. If the chromaticity value of a sub-image is within the bleeding chromaticity value interval, the sub-image is recorded as a risk sub-image, and the risk sub-images of each image are obtained by screening, and the number of risk sub-images and the number of sub-images of each image of the capsule detection terminal of the user to be detected are counted; If the number of risk sub-images of each image of the capsule detection terminal of the user to be detected is 0, the image is recorded as a normal image. Otherwise, the image is recorded as a bleeding image, and the detection area corresponding to the image is recorded as a bleeding area. The number of risk sub-images of the image is divided by the number of sub-images to obtain the risk value of the bleeding area, thereby obtaining each bleeding area of ​​the user to be detected and the risk value of each bleeding area.

10. A system for executing the image detection method according to any one of claims 1 to 9, characterized in that: include: The target gastrointestinal bleeding detection judgment module is used to obtain the vital sign data of the user to be detected from the hospital operation platform, determine whether the user to be detected needs to undergo the target gastrointestinal bleeding detection, and if necessary, execute the detection parameter determination module of the user to be detected; The module for determining the detection parameters of the user to be detected is used to obtain the original data of the user to be detected from the hospital operation platform, and determine the detection posture transformation set and key detection time point set of the user to be detected in combination with the capsule detection terminal usage data in the data warehouse; The detection module is used to adjust the posture of the user to be detected after the user to be detected takes the capsule detection terminal, and to obtain the operation data of the capsule detection terminal of the user to be detected; An early warning terminal is used to warn the doctor responsible for the user to be tested based on the operation data of the capsule detection terminal of the user to be tested; The display terminal is used to extract an image set of a capsule detection terminal of a user to be detected, evaluate and obtain each bleeding area and the risk value of each bleeding area in the area to be detected, and display them.

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