Abnormity recognition and calibration processing method and system based on digestive tract endoscopic image

Abstract

The invention discloses an exception identification and calibration processing method and system based on a digestive tract endoscope image, and belongs to the technical field of exception identification and calibration processing. Extracting a focus area in the endoscopic image; endoscopic images and gastric acid value data are synchronously acquired in the same data acquisition period and are respectively recorded as periodic focus area and periodic gastric acid value data; calculating the change rate of the periodic focus area; calculating a fluctuation value of the periodic gastric acid value data; calculating comprehensive abnormal values in all data acquisition periods; calculating an abnormal difference value between different patients; presetting a threshold value, analyzing and performing alimentary canal abnormity identification calibration; according to the method, the change rate of the focus area and the fluctuation value of the gastric acid value are calculated, and comprehensive abnormal value analysis is combined, so that the structural and functional abnormalities of the digestive tract can be comprehensively evaluated, and a more accurate diagnosis basis is provided; by calculating the abnormal difference value between the patients, the group associated diseases can be effectively identified, automatic judgment and calibration are performed, and the accuracy and efficiency of diagnosis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of service management, and in particular to a method and system for abnormality identification and calibration processing based on gastrointestinal endoscopic images. Background Art

[0002] With the rapid development of medical imaging technology, endoscopic technology is increasingly used in the diagnosis of gastrointestinal diseases. Gastrointestinal endoscopy can directly observe the internal structure of the digestive tract, such as the esophagus, stomach, duodenum and other areas, through visual images to detect abnormal lesions, such as ulcers, polyps or tumors. Traditional endoscopic examinations usually rely on the doctor's experience to judge gastrointestinal lesions through naked eye observation and manual analysis of images. However, due to the subjectivity of manual analysis and the difficulty in processing image data from a large number of patients, automated image processing technology has gradually become a research focus. In recent years, with the continuous advancement of artificial intelligence technology, especially image recognition algorithms, the automated analysis capabilities of endoscopic images have been significantly improved, enabling more accurate lesion identification and analysis.

[0003] Although the existing automated gastrointestinal endoscopic image analysis technology can assist doctors in disease diagnosis to a certain extent, there are still several problems. First, the existing technology focuses on the identification of the lesion area in the image itself, and lacks comprehensive analysis with other physiological data of the patient, such as gastric acid secretion status, which may lead to incomplete judgment of the lesion. In addition, the existing image processing technology is often difficult to effectively capture the changing trend of the lesion area over time when processing multi-cycle and multi-collected image data, resulting in inaccurate judgment of disease progression. On the other hand, the lesion characteristics between different patients are rarely correlated and analyzed in the existing system, and it is impossible to discover group lesion patterns through horizontal comparison. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for abnormality identification and calibration based on gastrointestinal endoscopic images to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for abnormality identification and calibration based on digestive tract endoscopic images, the method comprising the following steps: based on the number of patients, dividing storage disks in a memory, and storing the patient's diagnosis and treatment information in the storage disks; synchronously collecting endoscopic images and gastric acid value data in the same data acquisition cycle; extracting lesion areas in endoscopic images; recording the lesion areas as periodic lesion areas, and recording the gastric acid value data as periodic gastric acid value data; calculating the change rate of the periodic lesion area of ​​the ith patient at the tth data acquisition cycle node; calculating the fluctuation value of the periodic gastric acid value data of the ith patient at the tth data acquisition cycle node; calculating the comprehensive abnormality value of the ith patient in all data acquisition cycles based on the change rate and the fluctuation value; calculating the abnormal difference value between the ith patient and the jth patient based on the comprehensive abnormality value CO(i) of the ith patient in all data acquisition cycles; presetting a threshold, analyzing and performing digestive tract abnormality identification and calibration.

[0007] As a preferred solution of the abnormality identification, calibration and processing method based on gastrointestinal endoscopic images described in the present invention, storage disks are divided in the memory based on the number of patients, wherein one storage disk is allocated to one patient, and the patient's diagnosis and treatment information is stored in the storage disk after the patient's authorization; the diagnosis and treatment information includes endoscopic images and gastric acid value data; the storage disk includes an endoscopic image database and a gastric acid value database, and is used to store endoscopic images and gastric acid value data, respectively.

[0008] The storage disk corresponding to the i-th patient is recorded as G i , store disk G i The endoscopic image database in Set the storage disk G i The gastric acid value database in

[0009] As a preferred solution of the abnormality identification and calibration processing method based on digestive tract endoscopy images described in the present invention, the data collection cycle is unified, and the endoscopic images and gastric acid value data are synchronously collected in the same data collection cycle and stored in the endoscopic image database respectively. and gastric acid value database In the embodiment of the present invention, the image analysis technology is used to extract the lesion area in the endoscopic image, which is denoted as Fa. The lesion area refers to the abnormal area in shape and color displayed on the endoscopic image.

[0010] At the tth data collection cycle node, obtain the endoscopic image database Database of lesion area Fa and gastric acid value in endoscopic images The gastric acid value data in the image is recorded as the periodic lesion area Fa. i,t (b) Record the gastric acid value data as periodic gastric acid value data WSi,t (h), where Fa i,t (b) represents the b-th cycle lesion area of ​​the ith patient at the t-th data collection cycle node, WS i,t (h) represents the h-th cycle gastric acid value data of the ith patient at the t-th data collection cycle node.

[0011] As a preferred solution of the abnormality identification and calibration processing method based on digestive tract endoscopy images described in the present invention, the periodic lesion area Fa of the i-th patient at the t-th data acquisition cycle node is calculated. i,t (b) The rate of change is calculated as follows:

[0012]

[0013] Where, ΔFa i,t (b) represents the periodic lesion area Fa of the i-th patient at the t-th data acquisition cycle node i,t (b) The rate of change, B represents the total number of periodic lesion areas, Fa i,1 (b) shows the lesion area in the initial cycle.

[0014] In the present invention, the formula calculates the rate of change of the lesion area relative to the initial lesion area in each cycle to obtain an average rate of change. This rate of change can reflect the development of the lesion over time, whether it is expanding or shrinking; the function of this formula is to identify the change trend of the lesion area. By calculating the rate of change of each lesion area, it is possible to determine whether the disease is worsening or improving; if the rate of change of a lesion area ΔFa i,t (b) A significant increase, which may indicate that the disease in that area is getting worse and requires further attention from the doctor.

[0015] Calculate the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t The fluctuation value of (h) is calculated as follows:

[0016]

[0017] Among them, ΔWS i,t (h) represents the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) is the fluctuation value, where H represents the total number of gastric acid value data in the period.

[0018] In the present invention, the formula takes into account all the gastric acid values ​​collected in each cycle, so that it can reflect the fluctuations in the entire cycle, rather than just the deviation of a single data point; this calculation method is more comprehensive and accurate. Compared with only calculating the difference between a single gastric acid value and the average value, this formula calculates the deviation of all data points, thereby avoiding misjudgment due to a single abnormal value or an isolated event; for example, if there is an obviously abnormally high value of the gastric acid value in a certain cycle, but other data are relatively stable, then this formula can reduce the impact of a single abnormal value on the fluctuation result; the result of the formula can be interpreted as the overall fluctuation range of the gastric acid value in a cycle. If the standard deviation (i.e., the fluctuation value) is large, it means that the gastric acid value of the patient in this cycle fluctuates greatly; if the fluctuation value is small, it means that the gastric acid value is relatively stable; under normal circumstances, the gastric acid value should be kept within a certain range, and abnormal fluctuations may mean unstable gastric acid secretion, or the presence of symptoms such as acid reflux.

[0019] Based on the periodic lesion area Fa of the i-th patient at the t-th data acquisition period node i,t (b) The rate of change ΔFa i,t (b) and the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) Fluctuation value ΔWS i,t (h), calculate the comprehensive abnormal value of the i-th patient in the entire data collection cycle, and the calculation formula is as follows:

[0020]

[0021] Among them, CO(i) represents the comprehensive abnormal value of the i-th patient in all data collection cycles, T represents the total number of data collection cycles, and α and β represent the weight coefficients of the preset periodic endoscopic image data and periodic gastric acid value data, respectively.

[0022] In the present invention, the lesion area in the endoscopic image provides direct visualization information, which can help doctors observe pathological changes inside the gastrointestinal tract, such as ulcers, polyps, inflammation, etc., and can show abnormal morphology or damage of the digestive tract, which is the key basis for diagnosing digestive tract diseases; gastric acid value data reflects the functional state of the digestive tract, especially whether the gastric acid secretion is normal. Excessive or insufficient secretion of gastric acid will affect the digestive function, and then cause diseases such as gastroesophageal reflux, gastritis or ulcers; the lesion area may show that there are lesions in the digestive tract, but after combining the gastric acid value data, the doctor can further understand whether the lesion is related to abnormal gastric acid secretion, for example, excessive gastric acid may aggravate ulcers, while insufficient gastric acid may cause indigestion; long-term abnormal gastric acid secretion may affect the physical structure of the digestive tract, for example, long-term excessive gastric acid may corrode the stomach wall, causing gastric ulcers or esophageal damage, and the lesion area can capture these changes, and the causes of these structural changes can be traced through the fluctuations of gastric acid values. In addition, some lesions may not be obvious in the image, but functional abnormalities will be displayed through fluctuations in gastric acid values. On the other hand, if the imaging shows that the digestive tract structure is normal but the gastric acid value is obviously abnormal, the doctor can speculate on the underlying functional digestive tract disease, such as functional dyspepsia or gastroesophageal reflux disease.

[0023] As a preferred solution of the abnormality identification and calibration processing method based on digestive tract endoscopy images described in the present invention, based on the comprehensive abnormality value CO(i) of the i-th patient in the entire data collection cycle, the abnormal difference value between the i-th patient and the j-th patient is calculated, and the calculation formula is as follows:

[0024] CY(i,j)=|CO(i)-CO(j)|;

[0025] Among them, CY(i,j) represents the abnormal difference value between the i-th patient and the j-th patient, and CO(j) represents the comprehensive abnormal value of the j-th patient in the entire data collection cycle.

[0026] A comprehensive abnormal value threshold τ and an abnormal difference value threshold θ are preset. If the comprehensive abnormal value CO(i)>τ of the ith patient in the entire data collection cycle, and the abnormal difference value CY9i,j)<θ between the ith patient and the jth patient, it is determined that the ith patient has a gastrointestinal abnormality, and there is a correlation between the ith patient and the jth patient. Then, all patients who have a correlation with the ith patient are calibrated for gastrointestinal abnormality identification.

[0027] A digestive tract endoscope image-based abnormality identification and calibration processing system comprises: a data storage module, a data acquisition and synchronization module, a data analysis and abnormality detection module and a patient abnormality association module.

[0028] The data storage module divides the storage disk in the memory based on the number of patients, and stores the patient's diagnosis and treatment information in the storage disk.

[0029] The data acquisition and synchronization module: synchronously acquires endoscopic images and gastric acid value data within the same data acquisition cycle; extracts the lesion area in the endoscopic image; and records the lesion area as a periodic lesion area and the gastric acid value data as a periodic gastric acid value data.

[0030] The data analysis and anomaly detection module: calculates the change rate of the periodic lesion area of ​​the i-th patient at the t-th data acquisition cycle node; calculates the fluctuation value of the periodic gastric acid value data of the i-th patient at the t-th data acquisition cycle node; based on the change rate and the fluctuation value, calculates the comprehensive abnormal value of the i-th patient in the entire data acquisition cycle.

[0031] The patient abnormality association module: calculates the abnormal difference value between the ith patient and the jth patient based on the comprehensive abnormality value CO(i) of the ith patient in the entire data collection cycle; presets a threshold, analyzes and performs digestive tract abnormality identification and calibration.

[0032] Furthermore, the data storage module includes an endoscopic image and gastric acid value storage unit and a patient storage allocation unit.

[0033] The endoscopic image and gastric acid value storage unit: based on the number of patients, the storage disk is divided in the memory, wherein one storage disk is allocated to one patient, and the patient's diagnosis and treatment information is stored in the storage disk after the patient's authorization; the diagnosis and treatment information includes endoscopic images and gastric acid value data; the storage disk includes an endoscopic image database and a gastric acid value database, and is used to store endoscopic images and gastric acid value data respectively.

[0034] The patient storage allocation unit: the storage disk corresponding to the i-th patient is recorded as G i , store disk G i The endoscopic image database in Set the storage disk G i The gastric acid value database in

[0035] Furthermore, the data acquisition and synchronization module includes an image acquisition unit and a periodic data setting unit.

[0036] The image acquisition unit: unifies the data acquisition cycle, synchronously acquires endoscopic images and gastric acid value data within the same data acquisition cycle, and stores them in the endoscopic image database respectively and gastric acid value database In the embodiment of the present invention, the image analysis technology is used to extract the lesion area in the endoscopic image, which is denoted as Fa. The lesion area refers to the abnormal area in shape and color displayed on the endoscopic image.

[0037] The periodic data setting unit: at the tth data collection period node, obtains the endoscopic image database Database of lesion area Fa and gastric acid value in endoscopic images The gastric acid value data in the image is recorded as the periodic lesion area Fa. i,t (b) Record the gastric acid value data as periodic gastric acid value data WS i,t (h), where Fa i,t (b) represents the b-th cycle lesion area of ​​the ith patient at the t-th data collection cycle node, WS i,t (h) represents the h-th cycle gastric acid value data of the ith patient at the t-th data collection cycle node.

[0038] Furthermore, the data analysis and anomaly detection module includes a change rate and fluctuation value calculation unit and a comprehensive anomaly value calculation unit.

[0039] The change rate and fluctuation value calculation unit calculates the periodic lesion area Fa of the i-th patient at the t-th data acquisition period node. i,t (b) The rate of change ΔFa i,t (b); Calculate the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) Fluctuation value ΔWS i,t (h).

[0040] The comprehensive abnormal value calculation unit: based on the periodic lesion area Fa of the i-th patient at the t-th data acquisition period node i,t (b) The rate of change ΔFa i,t (b) and the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) Fluctuation value ΔWS i,t (h) Calculate the comprehensive abnormal value CO(i) of the i-th patient during the entire data collection period.

[0041] Furthermore, the patient abnormality association module includes an abnormality difference value calculation unit and a patient abnormality difference analysis unit.

[0042] The abnormal difference value calculation unit calculates the abnormal difference value CY9i,j) between the ith patient and the jth patient based on the comprehensive abnormal value CO(i) of the ith patient in the entire data collection cycle.

[0043] The patient abnormal difference analysis unit: presets a comprehensive abnormal value threshold τ and an abnormal difference value threshold θ. If the comprehensive abnormal value CO(i)>τ of the ith patient in the entire data collection cycle, and the abnormal difference value CY(i,j)<θ between the ith patient and the jth patient, it is determined that the ith patient has a digestive tract abnormality, and there is a correlation between the ith patient and the jth patient, then all patients who have a correlation with the ith patient are calibrated for digestive tract abnormality identification.

[0044] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in the method and system for abnormal identification, calibration and processing based on gastrointestinal endoscopic images provided by the present invention, by dividing independent storage disks, individualized management of patient diagnosis and treatment information is achieved, ensuring data security and privacy protection, while facilitating retrieval and analysis. Within a unified data acquisition cycle, endoscopic images and gastric acid value data are synchronously acquired, the lesion area is extracted through image analysis, and the changes in lesions and gastric acid values ​​are dynamically monitored. By calculating the change rate of the lesion area and the gastric acid value fluctuation value, combined with comprehensive abnormal value analysis, the structural and functional abnormalities of the digestive tract can be comprehensively evaluated, providing a more accurate basis for diagnosis. By calculating the abnormal difference values ​​between patients, the system can effectively identify group-related diseases, and perform automated judgment and calibration, improve the accuracy and efficiency of diagnosis, and ultimately provide more comprehensive support for clinical decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0046] Figure 1 It is a schematic diagram of the steps of a method for abnormality identification and calibration based on digestive tract endoscopy images of the present invention;

[0047] Figure 2 It is a structural schematic diagram of an abnormality recognition and calibration processing system based on gastrointestinal endoscopic images of the present invention. DETAILED DESCRIPTION

[0048] 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.

[0049] See also Figure 1 In the first embodiment, a method for abnormality identification and calibration based on digestive tract endoscopy images is provided, the method comprising the following steps:

[0050] Step S1: Based on the number of patients, the storage disk is divided in the memory, and the diagnosis and treatment information of the patients is stored in the storage disk.

[0051] Specifically, based on the number of patients, storage disks are divided in the memory, wherein one storage disk is allocated to one patient, and the patient's diagnosis and treatment information is stored in the storage disk after the patient's authorization; the diagnosis and treatment information includes endoscopic images and gastric acid value data; the storage disk includes an endoscopic image database and a gastric acid value database, and is used to store endoscopic images and gastric acid value data, respectively.

[0052] Furthermore, the storage disk corresponding to the i-th patient is recorded as G i , store disk G i The endoscopic image database in Set the storage disk G i The gastric acid value database in

[0053] Step S2: synchronously collect endoscopic images and gastric acid value data within the same data collection cycle; extract the lesion area in the endoscopic image; record the lesion area as a periodic lesion area, and record the gastric acid value data as a periodic gastric acid value data.

[0054] Specifically, the data collection cycle is unified. In the same data collection cycle, endoscopic images and gastric acid value data are collected synchronously and stored in the endoscopic image database respectively. and gastric acid value database In the embodiment of the present invention, the image analysis technology is used to extract the lesion area in the endoscopic image, which is denoted as Fa. The lesion area refers to the abnormal area in shape and color displayed on the endoscopic image.

[0055] Furthermore, at the tth data collection cycle node, the endoscopic image database is obtained Database of lesion area Fa and gastric acid value in endoscopic images The gastric acid value data in the image is recorded as the periodic lesion area Fa. i,t (b) Record the gastric acid value data as periodic gastric acid value data WS i,t (h), where Fa i,t (b) represents the b-th cycle lesion area of ​​the ith patient at the t-th data collection cycle node, WS i,t (h) represents the h-th cycle gastric acid value data of the ith patient at the t-th data collection cycle node.

[0056] Step S3: Calculate the change rate of the periodic lesion area of ​​the ith patient at the tth data acquisition cycle node; calculate the fluctuation value of the periodic gastric acid value data of the ith patient at the tth data acquisition cycle node; based on the change rate and the fluctuation value, calculate the comprehensive abnormal value of the ith patient in the entire data acquisition cycle.

[0057] Specifically, calculate the periodic lesion area Fa of the i-th patient at the t-th data collection period node i,t (b) The rate of change is calculated as follows:

[0058]

[0059] Where, ΔFa i,t (b) represents the periodic lesion area Fa of the i-th patient at the t-th data acquisition cycle node i,t (b) The rate of change, B represents the total number of periodic lesion areas, Fa i,1 (b) shows the lesion area in the initial cycle.

[0060] For example, assuming that the total number of periodic lesion regions B is 3, the initial periodic lesion region Fa of the first patient is 1,1 (1) = 0.6, Fa 1,1 (2) = 0.6, Fa 1,1 (3) = 06, the periodic lesion area Fa of the first patient at the third data collection period node 1,3 (1) = 0.7, Fa 1,3 (2) = 0.8, Fa 1,3 (1) = 0.9, and the periodic lesion area Fa of the first patient at the third data acquisition period node is calculated. i,t (b) The rate of change ΔFa 1,3 (b)=0.17+0.29+0.375=0.835.

[0061] Further, calculate the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t The fluctuation value of (h) is calculated as follows:

[0062]

[0063] Among them, ΔWS i,t (h) represents the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) is the fluctuation value, where H represents the total number of gastric acid value data in the period.

[0064] For example, assuming that the total number of periodic gastric acid value data H is 4, the periodic gastric acid value data WS of the first patient at the third data collection period node is1,3 (1) = 5, WS 1,3 (2) = 6, WS 1,3 (3) = 4, WS 1,3 (4) = 7, substitute into the formula to calculate the fluctuation value ΔWS of the periodic gastric acid value data of the first patient at the third data collection period node 1,3 (h) = 0.12.

[0065] Further, based on the periodic lesion area Fa of the i-th patient at the t-th data acquisition period node i,t (b) The rate of change ΔFa i,t (b) and the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) Fluctuation value ΔWS i,t (h), calculate the comprehensive abnormal value of the i-th patient in the entire data collection cycle, and the calculation formula is as follows:

[0066]

[0067] Among them, CO(i) represents the comprehensive abnormal value of the i-th patient in all data collection cycles, T represents the total number of data collection cycles, and α and β represent the weight coefficients of the preset periodic endoscopic image data and periodic gastric acid value data, respectively.

[0068] For example, assuming that the total number of data acquisition cycles T is 3, α and β are 0.7 and 0.8 respectively, ΔFa 1,1 (b) = 0.7, ΔWS 1,1 (h) = 0.1, ΔFa 1,2 (b) = 0.8, ΔWS 1,2 (h) = 0.11, ΔFa is known 1,3 (b) = 0.835, ΔWS 1,3 (h) = 0.12, and substituting it into the formula, we get the comprehensive abnormal value CO(1) = 0.633.

[0069] Step S4: Based on the comprehensive abnormal value CO(i) of the ith patient in the entire data collection cycle, calculate the abnormal difference value between the ith patient and the jth patient; preset the threshold, analyze and perform gastrointestinal abnormality identification and calibration.

[0070] Specifically, based on the comprehensive abnormal value CO(i) of the i-th patient in all data collection cycles, the abnormal difference value between the i-th patient and the j-th patient is calculated, and the calculation formula is as follows:

[0071] CY(i,j)=|CO(i)-CO(j)|;

[0072] Among them, CY(i,j) represents the abnormal difference value between the i-th patient and the j-th patient, and CO(j) represents the comprehensive abnormal value of the j-th patient in the entire data collection cycle.

[0073] Furthermore, a comprehensive abnormal value threshold τ and an abnormal difference value threshold θ are preset. If the comprehensive abnormal value CO(i)>τ of the ith patient in the entire data collection cycle, and the abnormal difference value CY(i,j)<θ between the ith patient and the jth patient, it is determined that the ith patient has a gastrointestinal abnormality, and there is a correlation between the ith patient and the jth patient. Then, all patients who have a correlation with the ith patient are calibrated for gastrointestinal abnormality identification.

[0074] For example, assuming that j is 2, CO(2) is 0.62, the comprehensive abnormal value threshold τ and the abnormal difference value threshold θ are 0.5 and 0.1 respectively, and the abnormal difference value CY(1,2)=0.013 is calculated by substituting into the formula. It is known that CO(1)=0.633, then the comprehensive abnormal value CO(1)>τ, and the abnormal difference value CY(1,2) between the first patient and the second patient<θ, then it is determined that the first patient has a gastrointestinal abnormality, and there is a correlation between the first patient and the second patient, then the second patient is calibrated for gastrointestinal abnormality identification.

[0075] See also Figure 2 In the second embodiment of the present invention, a system for identifying, calibrating and processing abnormalities based on gastrointestinal endoscopy images is provided. The system includes a data storage module, a data acquisition and synchronization module, a data analysis and abnormality detection module and a patient abnormality association module.

[0076] The data storage module divides the storage disk in the memory based on the number of patients, and stores the patient's diagnosis and treatment information in the storage disk.

[0077] The data acquisition and synchronization module: synchronously acquires endoscopic images and gastric acid value data within the same data acquisition cycle; extracts the lesion area in the endoscopic image; and records the lesion area as a periodic lesion area and the gastric acid value data as a periodic gastric acid value data.

[0078] The data analysis and anomaly detection module: calculates the change rate of the periodic lesion area of ​​the i-th patient at the t-th data acquisition cycle node; calculates the fluctuation value of the periodic gastric acid value data of the i-th patient at the t-th data acquisition cycle node; based on the change rate and the fluctuation value, calculates the comprehensive abnormal value of the i-th patient in the entire data acquisition cycle.

[0079] The patient abnormality association module: calculates the abnormal difference value between the ith patient and the jth patient based on the comprehensive abnormality value CO(i) of the ith patient in the entire data collection cycle; presets a threshold, analyzes and performs digestive tract abnormality identification and calibration.

[0080] Furthermore, the data storage module includes an endoscopic image and gastric acid value storage unit and a patient storage allocation unit.

[0081] The endoscopic image and gastric acid value storage unit: based on the number of patients, the storage disk is divided in the memory, wherein one storage disk is allocated to one patient, and the patient's diagnosis and treatment information is stored in the storage disk after the patient's authorization; the diagnosis and treatment information includes endoscopic images and gastric acid value data; the storage disk includes an endoscopic image database and a gastric acid value database, and is used to store endoscopic images and gastric acid value data respectively.

[0082] The patient storage allocation unit: the storage disk corresponding to the i-th patient is recorded as G i , store disk G i The endoscopic image database in Set the storage disk G i The gastric acid value database in

[0083] Furthermore, the data acquisition and synchronization module includes an image acquisition unit and a periodic data setting unit.

[0084] The image acquisition unit: unifies the data acquisition cycle, synchronously acquires endoscopic images and gastric acid value data within the same data acquisition cycle, and stores them in the endoscopic image database respectively and gastric acid value database In the embodiment of the present invention, the image analysis technology is used to extract the lesion area in the endoscopic image, which is denoted as Fa. The lesion area refers to the abnormal area in shape and color displayed on the endoscopic image.

[0085] The periodic data setting unit: at the tth data collection period node, obtains the endoscopic image database Database of lesion area Fa and gastric acid value in endoscopic images The gastric acid value data in the image is recorded as the periodic lesion area Fa. i,t (b) Record the gastric acid value data as periodic gastric acid value data WS i,t (h), where Fa i,t (b) represents the b-th cycle lesion area of ​​the ith patient at the t-th data collection cycle node, WS i,t (h) represents the h-th cycle gastric acid value data of the ith patient at the t-th data collection cycle node.

[0086] Furthermore, the data analysis and anomaly detection module includes a change rate and fluctuation value calculation unit and a comprehensive anomaly value calculation unit.

[0087] The change rate and fluctuation value calculation unit calculates the periodic lesion area Fa of the i-th patient at the t-th data acquisition period node. i,t (b) The rate of change ΔFa i,t (b); Calculate the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) Fluctuation value ΔWS i,t (h).

[0088] The comprehensive abnormal value calculation unit: based on the periodic lesion area Fa of the i-th patient at the t-th data acquisition period node i,t (b) The rate of change ΔFa i,t (b) and the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) Fluctuation value ΔWS i,t (h) Calculate the comprehensive abnormal value CO(i) of the i-th patient during the entire data collection period.

[0089] Furthermore, the patient abnormality association module includes an abnormality difference value calculation unit and a patient abnormality difference analysis unit.

[0090] The abnormal difference value calculation unit calculates the abnormal difference value CY(i,j) between the i-th patient and the j-th patient based on the comprehensive abnormal value CO(i) of the i-th patient in all data collection cycles.

[0091] The patient abnormal difference analysis unit: presets a comprehensive abnormal value threshold τ and an abnormal difference value threshold θ. If the comprehensive abnormal value CO(i)>τ of the ith patient in the entire data collection cycle, and the abnormal difference value CY(i,j)<θ between the ith patient and the jth patient, it is determined that the ith patient has a digestive tract abnormality, and there is a correlation between the ith patient and the jth patient, then all patients who have a correlation with the ith patient are calibrated for digestive tract abnormality identification.

[0092] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0093] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for abnormality identification and calibration based on digestive tract endoscopy images, characterized in that: The method comprises the following steps: Step S1: dividing the storage disk in the memory based on the number of patients, and storing the endoscopic images and gastric acid value data of the patients in the storage disk; Step S2: synchronously collecting endoscopic images and gastric acid value data within the same data collection cycle; extracting the lesion area in the endoscopic image; recording the lesion area as a periodic lesion area, and recording the gastric acid value data as a periodic gastric acid value data; Step S3: calculating the change rate of the patient's periodic lesion area at the data collection cycle node; calculating the fluctuation value of the patient's periodic gastric acid value data at the data collection cycle node; based on the change rate and the fluctuation value, calculating the comprehensive abnormal value of the patient in the entire data collection cycle; Step S4: Based on the comprehensive abnormal values ​​of the patients in the entire data collection cycle, the abnormal difference values ​​between different patients are calculated, and the digestive tract abnormality identification and calibration are analyzed and performed.

2. The method for abnormality identification and calibration based on digestive tract endoscopy images according to claim 1, characterized in that: The specific implementation process of step S1 includes: Based on the number of patients, a storage disk is divided in the memory, wherein one storage disk is correspondingly divided for one patient, and after authorization by the patient, the patient's diagnosis and treatment information is stored in the storage disk; the diagnosis and treatment information includes endoscopic images and gastric acid value data; the storage disk includes an endoscopic image database and a gastric acid value database, and is used to store endoscopic images and gastric acid value data respectively; The storage disk corresponding to the i-th patient is denoted as G i , store disk G i The endoscopic image database in Set the storage disk G i The gastric acid value database in 3. The abnormality identification and calibration processing method based on digestive tract endoscopy images according to claim 2 is characterized in that: The specific implementation process of step S2 includes: Unified data collection cycle: within the same data collection cycle, endoscopic images and gastric acid value data are collected synchronously and stored in the endoscopic image database respectively. and gastric acid value database ; using image analysis technology to extract the lesion area in the endoscopic image, denoted as Fa, the lesion area refers to the abnormal area of ​​morphology and color displayed on the endoscopic image; At the tth data collection cycle node, obtain the endoscopic image database Database of lesion area Fa and gastric acid value in endoscopic images The gastric acid value data in the image is recorded as the periodic lesion area Fa. i,t (b) Record the gastric acid value data as periodic gastric acid value data WS i,t (h), where Fa i,t (b) represents the b-th cycle lesion area of ​​the ith patient at the t-th data collection cycle node, WS i,t (h) represents the h-th cycle gastric acid value data of the ith patient at the t-th data collection cycle node.

4. The abnormality identification and calibration processing method based on digestive tract endoscopy images according to claim 3 is characterized in that: The specific implementation process of step S3 includes: Calculate the periodic lesion area Fa of the i-th patient at the t-th data acquisition cycle node i,t (b) The rate of change is calculated as follows: Where, ΔFa i,t (b) represents the periodic lesion area Fa of the i-th patient at the t-th data acquisition cycle node i,t (b) The rate of change, B represents the total number of periodic lesion areas, Fa i,1 (b) indicates the initial cycle lesion area; Calculate the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t The fluctuation value of (h) is calculated as follows: Among them, ΔWS i,t (h) represents the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) is the fluctuation value, where H represents the total number of periodic gastric acid value data; Based on the periodic lesion area Fa of the i-th patient at the t-th data acquisition period node i,t (b) The rate of change ΔFa i,t (b) and the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) Fluctuation value ΔWS i,t (h), calculate the comprehensive abnormal value of the i-th patient in the entire data collection cycle, and the calculation formula is as follows: Among them, CO(i) represents the comprehensive abnormal value of the i-th patient in all data collection cycles, T represents the total number of data collection cycles, and α and β represent the weight coefficients of the preset periodic endoscopic image data and periodic gastric acid value data, respectively.

5. The method for abnormality identification and calibration based on digestive tract endoscopy images according to claim 4, characterized in that: The specific implementation process of step S4 includes: Based on the comprehensive abnormal value CO(i) of the i-th patient in all data collection cycles, the abnormal difference value between the i-th patient and the j-th patient is calculated using the following formula: CY(i,j)=|CO(i)-CO(j)|; Among them, CY(i,j) represents the abnormal difference value between the i-th patient and the j-th patient, and CO(j) represents the comprehensive abnormal value of the j-th patient in the entire data collection cycle; A comprehensive abnormal value threshold τ and an abnormal difference value threshold θ are preset. If the comprehensive abnormal value CO(i) of the ith patient in the entire data collection cycle is greater than τ, and the abnormal difference value CY(i,j) between the ith patient and the jth patient is less than θ, then it is determined that the ith patient has a gastrointestinal abnormality, and there is a correlation between the ith patient and the jth patient. Then, all patients who have a correlation with the ith patient are calibrated for gastrointestinal abnormality identification.

6. A system for abnormality identification and calibration based on digestive tract endoscopy images, characterized in that: The system includes: a data storage module, a data acquisition and synchronization module, a data analysis and abnormality detection module and a patient abnormality association module; The data storage module: divides the storage disk in the memory based on the number of patients, and stores the diagnosis and treatment information of the patients in the storage disk; The data acquisition and synchronization module: synchronously acquires endoscopic images and gastric acid value data within the same data acquisition cycle; extracts the lesion area in the endoscopic image; respectively records the lesion area as a periodic lesion area, and records the gastric acid value data as a periodic gastric acid value data; The data analysis and anomaly detection module: calculates the change rate of the periodic lesion area of ​​the ith patient at the tth data collection cycle node; calculates the fluctuation value of the periodic gastric acid value data of the ith patient at the tth data collection cycle node; based on the change rate and the fluctuation value, calculates the comprehensive abnormal value of the ith patient in all data collection cycles; The patient abnormality association module: calculates the abnormal difference value between the ith patient and the jth patient based on the comprehensive abnormality value CO(i) of the ith patient in the entire data collection cycle; presets a threshold, analyzes and performs digestive tract abnormality identification and calibration.

7. The abnormality identification and calibration processing system based on digestive tract endoscopy images according to claim 6 is characterized by: The data storage module includes an endoscopic image and gastric acid value storage unit and a patient storage allocation unit; The endoscopic image and gastric acid value storage unit: based on the number of patients, a storage disk is divided in the memory, wherein one storage disk is correspondingly allocated to one patient, and after authorization by the patient, the patient's diagnosis and treatment information is stored in the storage disk; the diagnosis and treatment information includes endoscopic images and gastric acid value data; the storage disk includes an endoscopic image database and a gastric acid value database, and is used to store endoscopic images and gastric acid value data respectively; The patient storage allocation unit: the storage disk corresponding to the i-th patient is recorded as G i , store disk G i The endoscopic image database in Set the storage disk G i The gastric acid value database in 8. The abnormality identification and calibration processing system based on digestive tract endoscopy images according to claim 7 is characterized by: The data acquisition and synchronization module includes an image acquisition unit and a periodic data setting unit; The image acquisition unit: unifies the data acquisition cycle, synchronously acquires endoscopic images and gastric acid value data within the same data acquisition cycle, and stores them in the endoscopic image database respectively and gastric acid value database ; using image analysis technology to extract the lesion area in the endoscopic image, denoted as Fa, the lesion area refers to the abnormal area of ​​morphology and color displayed on the endoscopic image; The periodic data setting unit: at the tth data collection period node, obtains the endoscopic image database Database of lesion area Fa and gastric acid value in endoscopic images The gastric acid value data in the image is recorded as the periodic lesion area Fa. i,t (b) Record the gastric acid value data as periodic gastric acid value data WS i,t (h), where Fa i,t (b) represents the b-th cycle lesion area of ​​the ith patient at the t-th data collection cycle node, WS i,t (h) represents the h-th cycle gastric acid value data of the ith patient at the t-th data collection cycle node.

9. The abnormality identification and calibration processing system based on digestive tract endoscopy images according to claim 8 is characterized by: The data analysis and anomaly detection module includes a change rate and fluctuation value calculation unit and a comprehensive anomaly value calculation unit; The change rate and fluctuation value calculation unit calculates the periodic lesion area Fa of the i-th patient at the t-th data acquisition period node. i,t (b) The rate of change ΔFa i,t (b); Calculate the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) Fluctuation value ΔWS i,t (h); The comprehensive abnormal value calculation unit: based on the periodic lesion area Fa of the i-th patient at the t-th data acquisition period node i,t (b) The rate of change ΔFa i,t (b) and the periodic gastric acid value data WS of the i-th patient at the t-th data collection cycle node i,t (h) Fluctuation value ΔWS i,t (h) Calculate the comprehensive abnormal value CO(i) of the i-th patient during the entire data collection period.

10. The abnormality identification and calibration processing system based on digestive tract endoscopy images according to claim 9, characterized in that: The patient abnormality association module includes an abnormality difference value calculation unit and a patient abnormality difference analysis unit; The abnormal difference value calculation unit calculates the abnormal difference value CY(i,j) between the i-th patient and the j-th patient based on the comprehensive abnormal value CO(i) of the i-th patient in all data collection cycles; The patient abnormal difference analysis unit: presets a comprehensive abnormal value threshold τ and an abnormal difference value threshold θ. If the comprehensive abnormal value CO(i)>τ of the ith patient in the entire data collection cycle, and the abnormal difference value CY(i,j)<θ between the ith patient and the jth patient, it is determined that the ith patient has a digestive tract abnormality, and there is a correlation between the ith patient and the jth patient, then all patients who have a correlation with the ith patient are calibrated for digestive tract abnormality identification.

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