A thyroid calcification detection system

Abstract

The present invention relates to the technical field of thyroid calcification, and discloses a thyroid calcification detection system, comprising a data acquisition module, an analysis and prediction module and a risk warning module. The data acquisition module is used to collect patient age information, family-related medical history information, related diagnostic information and thyroid imaging data, and compose a personal information data set and a patient thyroid imaging data set to send to the analysis and prediction module, and then analyze the personal information characteristics and personal imaging information characteristics according to the personal information data set and the patient thyroid imaging data set, calculate the calcification characteristic GHtz and the calcification risk index GHfx, and then compare them with the corresponding threshold value to determine whether deterioration will occur, and generate a deterioration report accordingly and send it to the risk warning module, so as to help doctors evaluate the later calcification deterioration of patients, thereby reducing the pressure on doctors and helping doctors to better evaluate patients.

Description

Technical Field

[0001] The present invention relates to the technical field of thyroid calcification, and in particular to a thyroid calcification detection system. Background Art

[0002] The thyroid gland is a butterfly-shaped gland located on the front of our neck. It plays a vital role in our life activities. It is located on both sides of the trachea, below the Adam's apple, covered by skin, muscles and other tissues. It consists of two lobes with a structure called the isthmus connecting the two sides. Each lobe is about 5 cm long, 3 cm wide and about 1.5 cm thick. The outside of the thyroid gland is surrounded by a thin membrane called the "true capsule" and the inside is surrounded by a thin membrane called the "false capsule". The main function of the thyroid gland is to produce, store and release thyroid hormones, which are essential for regulating the body's metabolism, growth and development, the function of the nervous system, and the regulation of body temperature.

[0003] Thyroid calcification refers to the deposition of calcium salts in the thyroid tissue, which can usually be observed in medical imaging through X-rays, ultrasound or CT scans. Thyroid calcification may be caused by a variety of reasons, including inflammation, tumors, malnutrition, etc.

[0004] Existing thyroid calcification detection usually combines multiple imaging technologies to obtain a comprehensive and accurate assessment of the type, location, distribution, morphology of calcification and its relationship with thyroid nodules. After evaluating the type, location, distribution, morphology of calcification and its relationship with thyroid nodules, doctors need to independently analyze the development trend of calcification based on their own medical level, and are unable to conduct risk assessments on patients through existing thyroid calcification detection systems, which can easily lead to errors and increase pressure on doctors and patients. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a thyroid calcification detection system, which has the capability of comprehensively analyzing the patient's thyroid calcification condition by combining a variety of data, and generating a deterioration report after determining the risk of deterioration, which helps to assist doctors in evaluating the patient's later calcification deterioration, thereby reducing the doctor's pressure and assisting the doctor in better evaluating the patient, etc., and solves the problem that existing doctors need to independently analyze the development trend of calcification based on their own medical level.

[0007] (II) Technical solution

[0008] To achieve the above-mentioned object, the present invention provides the following technical solutions: a thyroid calcification detection system, comprising a data acquisition module, an analysis and prediction module and a risk warning module;

[0009] The data acquisition module includes a patient age information acquisition unit, a patient family medical history information acquisition unit, a thyroid-related diagnosis and treatment information acquisition unit, and an image data acquisition unit. The patient age information acquisition unit obtains the patient age information through a network connection database. The patient family medical history information acquisition unit obtains whether the patient's direct relatives within three generations have family-related medical history information through a network connection database. The thyroid-related diagnosis and treatment information acquisition unit is used to obtain relevant diagnostic information whether the patient has long-term chronic inflammation or has received radiotherapy through a network connection database. The image data acquisition unit is used to obtain the patient's thyroid imaging data through a network connection database.

[0010] The data acquisition module integrates the patient's age information, family-related medical history information, and related diagnosis information to form a personal information data set. The data acquisition module forms a patient's thyroid image data set based on multiple thyroid image data. The data acquisition module sends the collected data set to the analysis and prediction module.

[0011] The analysis and prediction module analyzes the personal information feature XXtz based on the personal information data set, and the analysis and prediction module analyzes the personal image information feature YXtz based on the patient's thyroid image data set;

[0012] The analysis and prediction module calculates the calcification feature GHtz based on the personal information feature XXtz and the personal image information feature YXtz. A growth feature threshold SZyz is set inside the analysis and prediction module. The calcification feature GHtz is compared with the growth feature threshold SZyz to determine the probability of growth deterioration, and a deterioration report is generated accordingly and sent to the risk warning module;

[0013] The analysis and prediction module calculates the calcification risk index GHfx based on the calcification feature GHtz and other risk influencing factors of the patient. The analysis and prediction module is internally provided with a calcification development risk threshold GHyz. By comparing the calcification risk index GHfx with the calcification development risk threshold GHyz, it is determined whether a deterioration will occur, and a corresponding deterioration report is generated and sent to the risk warning module.

[0014] Preferably, the patient age information is divided into adolescent, middle-aged and elderly groups according to actual age, wherein 0-18 years old represents the adolescent group, 18-55 years old represents the middle-aged group, and 55 years old and above represents the elderly group.

[0015] Preferably, the expression of the personal information data set is: Among them, GR1~FRn represent the collected data sets corresponding to the first and last data in the personal information data respectively, n represents that there are n groups of collected data, x represents that the patient in each group of personal information data is a young patient, o represents the number of family-related medical history information in each group of personal information data, and p represents the number of related diagnostic information in each group of personal information data.

[0016] Preferably, the expression of the thyroid image dataset is: {YXsj 1 , YXsj 2 , YXsj 3 、···YXsj n}, where YXsj 1 ~YXsj n Represents the first image data and the last image data in the collected data set, and n represents that there are n groups of collected data.

[0017] Preferably, the personal information feature XXtz is calculated as follows:

[0018]

[0019] In the above formula, Represents the sum of all data in the personal information dataset.

[0020] Preferably, the calculation method of the personal image information feature YXtz is as follows:

[0021]

[0022] In the above formula, YXsj 1 +YX 2 +YX 3 +···+YXsj n Represents the sum of all data in the personal information dataset.

[0023] Preferably, the calcification feature GHtz is calculated as follows:

[0024] GHtz=YXtz+XXtz

[0025] Preferably, when the calcification feature GHtz is greater than the growth feature threshold SZyz, it indicates that there is a risk of deterioration, and a risk report is generated for risk warning.

[0026] Preferably, the calcification risk index GHfx is calculated as follows:

[0027] GHfx=YXtz+XXtz+P

[0028] In the above formula, P represents the factors that affect the deterioration of thyroid calcification. If the patient has unbalanced iodine intake, malnutrition, changes in sex hormones, or environmental pollution in the living area, the impact value of P will increase by 1 accordingly.

[0029] Preferably, when the calcification risk index GHfx is greater than the calcification development risk threshold GHyz, it indicates that there is a risk of deterioration, and a risk report is generated for risk warning.

[0030] Compared with the prior art, the present invention provides a thyroid calcification detection system, which has the following beneficial effects:

[0031] 1. The present invention collects information about the patient's age information, family-related medical history information, related diagnostic information, and thyroid imaging data, and then forms a personal information data set based on the patient's age information, family-related medical history information, and related diagnostic information. The patient's age information is then divided into groups of adolescents, middle-aged people, and the elderly. Combined with the thyroid imaging data, it is beneficial to analyze the influence of various factors on the patient's thyroid calcification through the patient's age information, family-related medical history information, related diagnostic information, and thyroid imaging data, providing a favorable reference evaluation basis for the later development trend of the patient's thyroid calcification, facilitating a detailed analysis of the later development trend of the patient's thyroid calcification, and improving the accuracy of the analysis of the later development trend of the patient's thyroid calcification.

[0032] 2. The present invention can effectively analyze the deterioration of the late development trend of thyroid calcification of patients from the patient's age information, family medical history information, relevant diagnostic information and thyroid imaging data information by performing corresponding analysis on the calcification feature GHtz and the calcification risk index GHfx, and compare them with the corresponding thresholds. Then, combined with other risk influencing factors of the patient, the deterioration of the late development trend of thyroid calcification of the patient is further judged. After the judgment is completed, a deterioration risk report is generated, which helps to assist doctors in evaluating the late deterioration of calcification of patients, thereby reducing the pressure on doctors and helping doctors to better evaluate patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the structural system of the present invention; DETAILED DESCRIPTION

[0034] 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.

[0035] See also Figure 1, a thyroid calcification detection system, including a data acquisition module, an analysis and prediction module, and a risk warning module;

[0036] The data acquisition module includes a patient age information acquisition unit, a patient family medical history information acquisition unit, a thyroid-related diagnosis and treatment information acquisition unit, and an image data acquisition unit. The patient age information acquisition unit obtains the patient age information through a network connection to a database. The patient age information is divided into groups of adolescents, middle-aged and elderly people according to the actual age, wherein 0-18 years old represents the adolescent group, 18-55 years old represents the middle-aged group, and 55 years old and above represents the elderly group. The patient family medical history information acquisition unit obtains whether the patient's direct relatives within three generations have family-related medical history information through a network connection to a database. The thyroid-related diagnosis and treatment information acquisition unit is used to obtain relevant diagnostic information on whether the patient has long-term chronic inflammation or has received radiotherapy through a network connection to a database. The image data acquisition unit is used to obtain the patient's thyroid imaging data through a network connection to a database;

[0037] The data collection module integrates the patient's age information, family medical history information, and related diagnostic information to form a personal information data set: Where GR1~GRn represent the collected data sets corresponding to the first and last data in the personal information data, respectively, n represents that there are n groups of collected data, x represents that the patient is a young patient in each group of personal information data, o represents the number of family-related medical history information in each group of personal information data, and p represents the number of related diagnostic information in each group of personal information data. The data collection module forms a patient thyroid image data set based on multiple thyroid image data: {YXsj 1 , YXsj 2 , YXsj 3 、···YXsj n , where YXsj 1 ~YXsj n represents the first image data and the last image data in the collected data set, respectively; n represents that there are n groups of collected data, and the data collection module sends the collected data set to the analysis and prediction module;

[0038] The analysis and prediction module analyzes the personal information feature XXtz based on the personal information data set;

[0039] The calculation method of personal information feature XXtz is as follows:

[0040]

[0041] In the above formula, Represents the sum of all data in the personal information dataset;

[0042] The analysis and prediction module analyzes the individual imaging information features YXtz based on the patient's thyroid imaging dataset;

[0043] The calculation method of personal image information feature YXtz is as follows:

[0044]

[0045] In the above formula, YXsj 1 +YX 2 +YX 3 +···+YXsj n Represents the sum of all data in the personal information dataset;

[0046] After collecting information on the patient's age information, family-related medical history information, relevant diagnostic information, and thyroid imaging data, a personal information data set is formed based on the patient's age information, family-related medical history information, and relevant diagnostic information. The patient's age information is then divided into groups of adolescents, middle-aged people, and the elderly. Combined with thyroid imaging data, it is beneficial to analyze the impact of various factors on the patient's thyroid calcification through the patient's age information, family-related medical history information, relevant diagnostic information, and thyroid imaging data, providing a favorable reference and evaluation basis for the patient's thyroid calcification's later development trend, facilitating a detailed analysis of the patient's thyroid calcification's later development trend, and improving the accuracy of the analysis of the patient's thyroid calcification's later development trend.

[0047] The analysis and prediction module calculates the calcification feature GHtz based on the personal information feature XXtz and the personal image information feature YXtz. A growth feature threshold SZyz is set inside the analysis and prediction module. By comparing the calcification feature GHtz with the growth feature threshold SZyz, the probability of growth deterioration is determined, and a deterioration report is generated and sent to the risk warning module accordingly;

[0048] The calculation method of calcification feature GHtz is as follows:

[0049] GHtz=YXtz+XXtz

[0050] In the above formula, when the calcification feature GHyz is greater than the growth feature threshold SZyz, it means there is a risk of deterioration, and a risk report is generated for risk warning;

[0051] The analysis and prediction module calculates the calcification risk index GHfx based on the calcification feature GHtz and other risk influencing factors of the patient. The analysis and prediction module is internally set with a calcification development risk threshold GHyz. By comparing the calcification risk index GHfx with the calcification development risk threshold GHyz, it is determined whether a deterioration will occur, and a deterioration report is generated and sent to the risk warning module accordingly;

[0052] The calcification risk index GHfx is calculated as follows:

[0053] GHfx=YXtz+XXtz+P

[0054] In the above formula, P represents factors that affect the deterioration of thyroid calcification. For example, if the patient has unbalanced iodine intake, malnutrition, changes in sex hormones, or environmental pollution in the living area, the impact value of P will increase by 1 accordingly. When the calcification risk index GHfx is greater than the calcification development risk threshold GHyz, it means that there is a risk of deterioration, and a risk report will be generated for risk warning.

[0055] By performing corresponding analysis on the calcification feature GHtz and the calcification risk index GHfx and comparing them with the corresponding thresholds, the deterioration of the later development trend of the patient's thyroid calcification can be effectively analyzed from the patient's age information, family medical history information, relevant diagnostic information and thyroid imaging data information. Combined with other risk influencing factors of the patient, the deterioration of the later development trend of the patient's thyroid calcification can be further judged. After the judgment is completed, a deterioration risk report is generated, which helps to assist doctors in evaluating the patient's later calcification deterioration, thereby reducing the doctor's pressure and helping doctors to better evaluate patients.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A thyroid calcification detection system, characterized in that: It includes data collection module, analysis and prediction module and risk warning module; The data acquisition module includes a patient age information acquisition unit, a patient family medical history information acquisition unit, a thyroid-related diagnosis and treatment information acquisition unit, and an image data acquisition unit. The patient age information acquisition unit obtains the patient age information through a network connection database. The patient family medical history information acquisition unit obtains whether the patient's direct relatives within three generations have family-related medical history information through a network connection database. The thyroid-related diagnosis and treatment information acquisition unit is used to obtain relevant diagnostic information whether the patient has long-term chronic inflammation or has received radiotherapy through a network connection database. The image data acquisition unit is used to obtain the patient's thyroid imaging data through a network connection database. The data acquisition module integrates the patient's age information, family-related medical history information, and related diagnosis information to form a personal information data set. The data acquisition module forms a patient's thyroid image data set based on multiple thyroid image data. The data acquisition module sends the collected data set to the analysis and prediction module. The analysis and prediction module analyzes the personal information feature XXtz based on the personal information data set, and the analysis and prediction module analyzes the personal image information feature YXtz based on the patient's thyroid image data set; The analysis and prediction module calculates the calcification feature GHtz based on the personal information feature XXtz and the personal image information feature YXtz. A growth feature threshold SZyz is set inside the analysis and prediction module. The calcification feature GHtz is compared with the growth feature threshold SZyz to determine the probability of growth deterioration, and a deterioration report is generated accordingly and sent to the risk warning module; The analysis and prediction module calculates the calcification risk index GHfx based on the calcification feature GHtz and other risk influencing factors of the patient. The analysis and prediction module is internally provided with a calcification development risk threshold GHyz. By comparing the calcification risk index GHfx with the calcification development risk threshold GHyz, it is determined whether a deterioration will occur, and a corresponding deterioration report is generated and sent to the risk warning module.

2. A thyroid calcification detection system according to claim 1, characterized in that: The patient age information is divided into adolescent, middle-aged and elderly groups according to actual age, where 0-18 years old represents the adolescent group, 18-55 years old represents the middle-aged group, and 55 years old and above represents the elderly group.

3. A thyroid calcification detection system according to claim 2, characterized in that: The expression of the personal information dataset is: Among them, GR1~GRn represent the collected data sets corresponding to the first and last data in the personal information data respectively, n represents that there are n groups of collected data, x represents that the patient in each group of personal information data is a young patient, o represents the number of family-related medical history information in each group of personal information data, and p represents the number of related diagnostic information in each group of personal information data.

4. A thyroid calcification detection system according to claim 1, characterized in that: The expression of the thyroid image dataset is: {YXsj1, YXsj2, YXsj3, ···YXsj n }, where YXsj1~YXsj n Represents the first image data and the last image data in the collected data set, and n represents that there are n groups of collected data.

5. A thyroid calcification detection system according to claim 3, characterized in that: The calculation method of the personal information feature XXtz is as follows: In the above formula, Represents the sum of all data in the personal information dataset.

6. A thyroid calcification detection system according to claim 4, characterized in that: The calculation method of the personal image information feature YXtz is as follows: In the above formula, YXsj1+YXsj2+YXsj3+···+YXsj n Represents the sum of all data in the personal information dataset.

7. A thyroid calcification detection system according to claim 6, characterized in that: The calculation method of the calcification feature GHtz is as follows: GHtz=YXtz+XXtz.

8. A thyroid calcification detection system according to claim 7, characterized in that: When the calcification feature GHtz is greater than the growth feature threshold SZyz, it indicates that there is a risk of deterioration, and a risk report is generated for risk warning.

9. A thyroid calcification detection system according to claim 6, characterized in that: The calcification risk index GHfx is calculated as follows: GHfx=YXtz+XXtz+P In the above formula, P represents the factors that affect the deterioration of thyroid calcification. If the patient has unbalanced iodine intake, malnutrition, changes in sex hormones, or environmental pollution in the living area, the impact value of P will increase by 1 accordingly.

10. A thyroid calcification detection system according to claim 9, characterized in that: When the calcification risk index GHfx is greater than the calcification development risk threshold GHyz, it indicates that there is a risk of deterioration, and a risk report is generated for risk warning.

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