Cardiovascular 3D printing data processing method

Abstract

The invention discloses a cardiovascular 3D printing data processing method, and relates to the technical field of 3D printing data processing.The cardiovascular 3D printing data processing method comprises the steps that patients with cardiovascular diseases are classified, then a 3D operation group and a non-3D operation group are obtained, then basic information of the 3D operation group and the non-3D operation group is analyzed, and then the prognosis influence condition of a 3D printing technology on the patients is obtained; according to the method, the necessity of applying the 3D printing technology to cardiovascular disease treatment is determined, then the significant characteristics of the crowd suitable for the 3D printing technology are obtained through comprehensive analysis, then the crowd suitable for the 3D printing technology is obtained, accurate application of the 3D printing technology is ensured, and then the success rate of an operation and the postoperative effect of a patient are improved; whether the target patient belongs to the 3D technology applicable crowd is analyzed, the diagnosis and treatment scheme of the target patient is determined by integrating the historical diagnosis and treatment records of the patients, and the treatment cost input of the target patient is taken into account while the treatment effect is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of 3D printing data processing, and in particular to a cardiovascular 3D printing data processing method. Background Art

[0002] With the development of society and technology, 3D printing technology has been widely used in various fields. Cardiovascular 3D printing technology has gradually become a hot spot and difficulty in the medical field. Therefore, this application proposes a cardiovascular 3D printing data processing method.

[0003] Prior art, such as a method for processing cardiovascular 3D printing data disclosed in the invention application patent with announcement number: CN117094917A. By acquiring each layer of cardiovascular CT images as the image to be processed, and selecting any pixel point as the point to be detected, the initial target degree is calculated according to the grayscale distribution characteristics of the point and its neighborhood; the similarity between all pixel points in the neighborhood to be matched of the pixel point at the corresponding position of the adjacent layer image of the point to be detected is calculated, and the matching point of the point to be detected is screened, and the distance between the point to be detected and the matching point and the direction angle of the connecting vector are calculated, so as to obtain the confidence of the point to be detected; the target degree is obtained according to the confidence; the weight coefficient of the point to be detected is obtained by using the target degree and the grayscale information in the neighborhood, and all pixel points are filtered by using the weight coefficient to obtain a denoising result map, so as to complete the fitting of the 3D printing data. The present invention makes the filtering result more accurate and can better fit the 3D printing data.

[0004] With respect to the above scheme, there are the following technical problems: 1. The current technology mainly processes CT images to complete the fitting of 3D printing data. There is a lack of analysis on whether 3D printing technology can bring beneficial effects to patients. When the use of 3D technology for surgery cannot make a significant difference to the patient's recovery, fitting the patient's 3D printing data loses its meaning. The current technology neglects this aspect, resulting in an imperfect method for processing cardiovascular 3D printing data.

[0005] 2. The current technology lacks targeted analysis of the patient's basic condition, and ignores the situation where the patient's age or lesions in other parts of the body make them unsuitable for surgery using 3D printing technology. Ignoring this aspect will lead to 3D printing data processing for all patients, which will lead to a waste of resources while failing to improve the success rate of patient surgery. Summary of the invention

[0006] The purpose of this application is to provide a cardiovascular 3D printing data processing method to solve the problems existing in the background technology.

[0007] To solve the above technical problems, the present application adopts the following technical solutions: The present application provides a cardiovascular 3D printing data processing method, step one, data acquisition: obtain each 3D surgery patient and each non-3D surgery patient from the medical center, record them as 3D surgery group and non-3D surgery group respectively, and obtain basic information of the 3D surgery group and non-3D surgery group.

[0008] Step 2: Data analysis: Analyze the impact of 3D printing technology on patient recovery based on the basic information of each patient, and analyze the factors that affect patient recovery in the 3D surgery group.

[0009] Step 3. Comprehensive evaluation: Based on the analysis results of the 3D surgery group and the non-3D surgery group, obtain the applicable population for 3D printing technology.

[0010] Step 4: Program analysis: Obtain the basic information of the target patient, analyze whether the target patient is suitable for 3D printing technology, and then analyze the cardiovascular disease treatment plan suitable for the target patient.

[0011] Preferably, the 3D surgery patients and non-3D surgery patients are obtained and recorded as 3D surgery group and non-3D surgery group, respectively. The specific process is as follows: all patients with cardiovascular diseases in the current city are obtained from the medical center, recorded as each patient, and patients who have undergone cardiovascular surgery among each patient are obtained, recorded as each surgery patient, and each patient who has undergone surgery using 3D printing technology and each patient who has not undergone surgery using 3D printing technology are extracted based on the basic information of each surgery patient, and are recorded as each 3D surgery patient and each non-3D surgery patient, respectively, and each 3D surgery group and each non-3D surgery printing group are obtained accordingly.

[0012] Preferably, the basic information includes whether 3D technology is used for surgery, the number of patients in the 3D surgery group and the non-3D surgery group, the number of deaths within 3 years after surgery, the type and number of related complications, the number of secondary surgeries, the age of each patient, and whether there are lesions in other parts of the body before surgery.

[0013] Preferably, the factors affecting patient recovery in the 3D surgery group are analyzed, and the specific analysis process is as follows: The factors affecting patient recovery in the 3D surgery group are analyzed, and the specific analysis process is as follows: S1. Based on the basic information of each patient, the age of each patient, the presence or absence of lesions in other parts of the body before surgery, the mortality rate within 3 years after surgery, the incidence rate of postoperative complications and the incidence rate of secondary surgery are obtained.

[0014] S2. Obtain patients aged 25 to 35 years, 35 to 55 years, and over 55 years old in the 3D surgery group who had no lesions in other parts of the body before surgery, and record them as the young group, middle-aged group, and elderly group, respectively. Obtain the mortality rate within three years after surgery, the incidence of postoperative complications, and the incidence of secondary surgery in the young group, middle-aged group, and elderly group, and analyze to obtain the postoperative recovery assessment coefficients of the young group, middle-aged group, and elderly group. Similarly, analyze to obtain the postoperative recovery assessment coefficients of the young group, middle-aged group, and elderly group in the non-3D surgery group.

[0015] S3. Obtain patients in the 3D surgery group who had lesions in other parts of the body before surgery and were aged between 25 and 35 years old, between 35 and 55 years old, and over 55 years old, and record them as the young lesion group, middle-aged lesion group, and elderly lesion group, respectively. Obtain the mortality rate within three years after surgery, the incidence of postoperative complications, and the incidence of secondary surgery in the young lesion group, middle-aged lesion group, and elderly lesion group, and analyze to obtain the postoperative recovery assessment coefficients of the young lesion group, elderly lesion group, and middle-aged lesion group. Similarly, analyze to obtain the postoperative assessment coefficients of the young lesion group, middle-aged lesion group, and elderly lesion group in the non-3D surgery group.

[0016] S4. Compare the postoperative recovery evaluation coefficients of the young, middle-aged and elderly groups in the 3D surgery group with those of the young, middle-aged and elderly groups in the non-3D surgery group. If the ratios of the postoperative recovery evaluation coefficients are all within the range If the value is within the range, the patient's age has no effect on the postoperative recovery of the patients in the 3D surgery group. Otherwise, it indicates that the patient's age has an effect on the postoperative recovery of the patients in the 3D surgery group.

[0017] S5. Compare the postoperative recovery evaluation coefficients of the young lesion group, middle-aged lesion group and elderly lesion group in the 3D surgery group with the postoperative evaluation coefficients of the young lesion group, middle-aged lesion group and elderly lesion group in the non-3D surgery group. If the ratios of the postoperative recovery evaluation coefficients of the young lesion group, middle-aged lesion group and elderly lesion group are all within the interval If the value is within the range, then preoperative lesions in other parts have no effect on the postoperative recovery of patients in the 3D surgery group. Otherwise, it indicates that preoperative lesions in other parts have an effect on the postoperative recovery of patients in the 3D surgery group.

[0018] Preferably, the applicable population of 3D printing technology is obtained, and the specific process is as follows: A1. When the patient's age has an impact on the postoperative recovery of patients in the 3D surgery group, the postoperative recovery assessment coefficients of the young, middle-aged and elderly groups in the 3D surgery group are compared with the postoperative recovery assessment coefficients of the corresponding age groups in the non-3D surgery group. When the postoperative recovery assessment coefficient of a certain age group in the 3D surgery group is greater than the postoperative recovery assessment coefficient of the corresponding age group in the non-3D surgery group, the age group is recorded as the applicable age group for 3D printing technology, and the applicable age groups for each 3D printing technology are obtained accordingly.

[0019] A2. When preoperative lesions in other parts of the body have an impact on the postoperative recovery of each patient in the 3D surgery group, the postoperative recovery assessment coefficients of the young, middle-aged and elderly lesion groups in the 3D surgery group are compared with those of the corresponding lesion groups in the non-3D surgery group. When the postoperative recovery assessment coefficient of a lesion group in the 3D surgery group is greater than that of the corresponding lesion group in the non-3D surgery group, the lesion group is recorded as the lesion group applicable to 3D printing technology, and the lesion groups applicable to each 3D printing technology are obtained accordingly.

[0020] A3. Based on this, we can identify the applicable population for 3D printing technology.

[0021] Preferably, the analysis of whether the target patient belongs to the applicable population of 3D printing technology is specifically performed as follows: the impact of 3D printing technology on the patient's rehabilitation is obtained. When 3D printing technology has a positive impact on the patient's rehabilitation, the age of the target patient and the presence or absence of lesions in other parts of the body before surgery are obtained, and compared with the age and the presence or absence of lesions in other parts of the body before surgery of the applicable population of 3D printing technology. When the age of the target patient or the presence or absence of lesions in other parts of the body before surgery are consistent with the age or the presence or absence of lesions in other parts of the body before surgery of the applicable population of 3D printing technology, it indicates that the target patient belongs to the applicable population of 3D printing technology; otherwise, it indicates that the target patient does not belong to the applicable population of 3D printing technology.

[0022] When 3D printing technology has a negative impact or no impact on the patient's recovery, it indicates that the target patient is not a suitable population for 3D printing technology.

[0023] Preferably, the analysis is applicable to the cardiovascular disease treatment plan for the target patient, and the specific analysis process is as follows: when the target patient does not belong to the population applicable to the 3D printing technology, the target patient is operated on using a traditional surgical plan.

[0024] When the target patient belongs to the population applicable for 3D printing technology, the cardiovascular lesion area of ​​the target patient is preliminarily obtained based on the imaging data, and then the interactive multi-planar reconstruction imaging function is used to obtain a clear image of the cardiovascular lesion area. The obtained cardiovascular lesion area image is compared with the cardiovascular lesion area image of each patient in the 3D surgery group, and patients with the same cardiovascular lesion area as the target patient are obtained and recorded as reference patients. The target patient is surgically treated based on the diagnosis and treatment plan of the reference patient and the basic information of the target patient.

[0025] The beneficial effects of the present application are as follows: 1. A cardiovascular 3D printing data processing method provided by the present application classifies each cardiovascular disease patient to obtain a 3D surgery group and a non-3D surgery group, and then analyzes the basic information of the 3D surgery group and the non-3D surgery group to obtain the impact of 3D printing technology on the prognosis of each patient, determine the necessity of applying 3D printing technology to the treatment of cardiovascular diseases, and then comprehensively analyze to obtain the significant characteristics of the population applicable to 3D printing technology, and then obtain the applicable population of 3D printing technology, ensure the accurate application of 3D printing technology, and then improve the success rate of surgery and the postoperative effect of the patient. When the target patient needs treatment, analyze whether the target patient belongs to the population applicable to 3D technology, and determine the diagnosis and treatment plan for the target patient based on the historical diagnosis and treatment records of each patient, ensuring the treatment effect while also taking into account the treatment cost investment of the target patient.

[0026] 2. This application classifies each patient based on whether 3D printing technology was used for surgery, which lays the foundation for subsequent data analysis, and then analyzes the postoperative conditions of patients in the 3D surgery group and the non-3D surgery group to determine whether 3D printing technology has a significant beneficial effect on the treatment of patients, thereby ensuring the necessity of applying 3D printing technology in the medical field. At the same time, the patients in the 3D surgery group and the non-3D surgery group are reclassified based on age and the presence or absence of lesions in other parts of the body before surgery, thereby obtaining the significant characteristics of the population suitable for 3D printing technology, ensuring the accurate application of 3D printing technology, and thereby improving the success rate of surgery and the prognosis of patients.

[0027] 3. This application analyzes the applicable population of 3D printing technology to ensure the maximum treatment effect for each patient. 3D printing technology is used for treatment of the applicable population, and traditional methods are used for treatment of the non-applicable population. While ensuring the treatment effect, it also takes into account the patient's treatment cost investment, improves the hospital's resource utilization rate, and greatly improves the perfection and rationality of the method.

[0028] 4. This application determines the treatment plan for the target patient by comprehensively analyzing the basic information of the target patient and the historical diagnosis and treatment information of each patient, and uses the historical diagnosis and treatment plan as a reference to analyze the diagnosis and treatment plan for the target patient, which greatly reduces the surgical risk and uncertainty of the target patient and ensures the success rate of the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 The figure is a flowchart of the implementation steps of the present application method. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] Reference Figure 1 As shown, the present application provides a cardiovascular 3D printing data processing method, comprising the following steps: Step 1, data acquisition: obtain each 3D surgery patient and each non-3D surgery patient from the medical center, record them as a 3D surgery group and a non-3D surgery group respectively, and obtain basic information of the 3D surgery group and the non-3D surgery group.

[0033] The method of obtaining each 3D surgery patient and each non-3D surgery patient, and recording them as a 3D surgery group and a non-3D surgery group, is specifically as follows: all patients with cardiovascular diseases in the current city are obtained from the medical center, recorded as each patient, and patients who have undergone cardiovascular surgery among each patient are obtained, recorded as each surgery patient, and each patient who has undergone surgery using 3D printing technology and each patient who has not undergone surgery using 3D printing technology are extracted based on the basic information of each surgery patient, and are recorded as each 3D surgery patient and each non-3D surgery patient, respectively, thereby obtaining each 3D surgery group and each non-3D surgery printing group.

[0034] It should be noted that the use of 3D printing technology for surgery includes using 3D printing technology to provide patients with customized cardiac catheters and stents, using 3D printing technology to print customized medical devices and models for patients, etc.

[0035] In a specific example, the basic information includes whether 3D technology is used for surgery, the number of patients in the 3D surgery group and the non-3D surgery group, the number of deaths within 3 years after surgery, the type and number of related complications, the number of secondary surgeries, the age of each patient, and whether there are lesions in other parts of the body before surgery.

[0036] Step 2: Data analysis: Analyze the impact of 3D printing technology on patient recovery based on the basic information of each patient, and analyze the factors that affect patient recovery in the 3D surgery group.

[0037] In a specific example, the influence of 3D printing technology on the patient's recovery is analyzed based on the basic information of each patient. The specific process is as follows: based on the basic information of each 3D printing group, the mortality rate, the incidence rate of related complications and the incidence rate of secondary surgery within 3 years after surgery of the group of patients are obtained, which are recorded as , and , according to the calculation formula: The postoperative health impact coefficient of the 3D surgery group was obtained by analysis ,in , and They are respectively expressed as the weight factor corresponding to the patient's mortality rate within 3 years after surgery, the weight factor corresponding to the incidence of related complications, and the weight factor corresponding to the incidence of secondary surgery in the impact of cardiovascular 3D technology on the patient's postoperative health; the postoperative health impact coefficient of the non-3D surgery group was obtained by similar analysis .

[0038] It should be noted that , , , .

[0039] It should be noted that the weight factors corresponding to the mortality rate within 3 years after surgery, the weight factors corresponding to the incidence of related complications, and the weight factors corresponding to the incidence of secondary surgery in the impact of cardiovascular 3D technology on the postoperative health of patients were obtained through factor analysis. First, the information of spatial path attenuation of the mortality rate within 3 years after surgery, the incidence of related complications, and the incidence of secondary surgery of each patient was condensed, and then the variance explanation rate after rotation was obtained, and the weight was obtained by dividing the cumulative variance explanation rate.

[0040] It should be noted that factor analysis is a well-known technology. It is a multivariate statistical analysis method that starts from studying the internal dependencies of variables and reduces some variables with intricate relationships to a few comprehensive factors; information concentration is expressed as calculating the median; the variance explanation rate is the amount of information extracted by the factor, and the variance explanation rate = characteristic root / total number of analysis items; the variance explanation rate after rotation is expressed as the variance explanation rate of the factor after maximum variance rotation.

[0041] The postoperative impact coefficient of the 3D surgery group was compared with that of the non-3D surgery group. When the ratio of the postoperative health impact coefficient of the 3D surgery group to the postoperative impact coefficient of the non-3D surgery group was in the interval When the ratio of the postoperative health impact coefficient of the 3D surgery group to the postoperative impact coefficient of the non-3D surgery group is within the interval When the ratio of the postoperative health impact coefficient of the 3D surgery group to the postoperative impact coefficient of the non-3D surgery group is within the interval This suggests that cardiovascular 3D technology has a positive impact on patients' surgery.

[0042] In a specific example, the mortality rate within 3 years after surgery, the incidence of related complications and the incidence of secondary surgery are obtained as follows: the total number of patients in the 3D printing group and the number of deaths within 3 years after surgery are obtained from the historical medical records, and the calculation formula is used to calculate the mortality rate within 3 years after surgery. The 3-year mortality rate of the 3D printing group was Similarly, the mortality rate within 3 years after surgery in the non-3D printing group was obtained. .

[0043] The number and occurrence frequency of relevant complication types of each patient in the 3D printing group were obtained from the historical medical records and recorded as and ,in is the number corresponding to each patient in each 3D printing group, , is any integer greater than 2, is the number corresponding to the number of each type of relevant complications, , is any integer greater than 2, according to the calculation formula The incidence of related complications in the 3D printing group was analyzed ,in represents the total number of patients in the 3D printing group, Represents the total number of related complication types. Similarly, the incidence of related complications in the non-3D printing group was obtained by analysis .

[0044] The total number of patients in the 3D printing group and the number of patients who underwent secondary surgery were obtained from the historical medical records, and the calculation formula was used The incidence of secondary surgery in the 3D printing group was analyzed Similarly, the incidence of secondary surgery in the non-3D printing group was obtained. .

[0045] In a specific example, the factors affecting patient recovery in the 3D surgery group are analyzed, and the specific analysis process is as follows: S1. Based on the basic information of each patient, the age of each patient, the presence of lesions in other parts of the body before surgery, the mortality rate within 3 years after surgery, the incidence of postoperative complications and the incidence of secondary surgery are obtained.

[0046] S2. Obtain patients aged 25 to 35 years, 35 to 55 years, and over 55 years old in the 3D surgery group who had no lesions in other parts of the body before surgery, and record them as the young group, middle-aged group, and elderly group, respectively. Obtain the mortality rate within three years after surgery, the incidence of postoperative complications, and the incidence of secondary surgery in the young group, middle-aged group, and elderly group, and analyze to obtain the postoperative recovery assessment coefficients of the young group, middle-aged group, and elderly group. Similarly, analyze to obtain the postoperative recovery assessment coefficients of the young group, middle-aged group, and elderly group in the non-3D surgery group.

[0047] S3. Obtain patients in the 3D surgery group who had lesions in other parts of the body before surgery and were aged between 25 and 35 years old, between 35 and 55 years old, and over 55 years old, and record them as the young lesion group, middle-aged lesion group, and elderly lesion group, respectively. Obtain the mortality rate within three years after surgery, the incidence of postoperative complications, and the incidence of secondary surgery in the young lesion group, middle-aged lesion group, and elderly lesion group, and analyze to obtain the postoperative recovery assessment coefficients of the young lesion group, elderly lesion group, and middle-aged lesion group. Similarly, analyze to obtain the postoperative assessment coefficients of the young lesion group, middle-aged lesion group, and elderly lesion group in the non-3D surgery group.

[0048] S4. Compare the postoperative recovery evaluation coefficients of the young, middle-aged and elderly groups in the 3D surgery group with those of the young, middle-aged and elderly groups in the non-3D surgery group. If the ratios of the postoperative recovery evaluation coefficients of the young, middle-aged and elderly groups are all within the range If the value is within the range, the patient's age has no effect on the postoperative recovery of the patients in the 3D surgery group. Otherwise, it indicates that the patient's age has an effect on the postoperative recovery of the patients in the 3D surgery group.

[0049] S5. Compare the postoperative recovery evaluation coefficients of the young lesion group, middle-aged lesion group and elderly lesion group in the 3D surgery group with the postoperative evaluation coefficients of the young lesion group, middle-aged lesion group and elderly lesion group in the non-3D surgery group. If the ratios of the postoperative recovery evaluation coefficients of the young lesion group, middle-aged lesion group and elderly lesion group are all within the interval If the value is within the range, then preoperative lesions in other parts have no effect on the postoperative recovery of patients in the 3D surgery group. Otherwise, it indicates that preoperative lesions in other parts have an effect on the postoperative recovery of patients in the 3D surgery group.

[0050] In a specific example, the analysis obtains the postoperative recovery assessment coefficients of the young group, the elderly group and the middle-aged group, and the postoperative recovery assessment coefficients of the young lesion group, the elderly lesion group and the middle-aged lesion group. The specific analysis process is as follows: the mortality rate within 3 years after surgery, the incidence rate of postoperative complications and the incidence rate of secondary surgery in the young group, the middle-aged group and the elderly group are recorded as , and ,in The numbers corresponding to the youth group, middle-aged group and elderly group, , where numbers 1, 2 and 3 represent the youth group, middle-aged group and elderly group respectively. According to the calculation formula: The postoperative recovery assessment coefficients of the young group, middle-aged group and elderly group were obtained by analysis. ,in , and They are respectively expressed as the standard values ​​of mortality rate within 3 years after surgery, standard values ​​of incidence of postoperative complications and standard values ​​of incidence of secondary surgery. , and They are respectively expressed as the weight factor corresponding to the patient's mortality rate within 3 years after surgery, the weight factor corresponding to the incidence of postoperative complications, and the weight factor corresponding to the incidence of secondary surgery; similarly, the postoperative recovery assessment coefficients of the young lesion group, middle-aged lesion group, and elderly lesion group can be analyzed to obtain.

[0051] It should be noted that the analysis methods of the 3-year postoperative mortality rate, postoperative complication rate and secondary surgery rate of the young group, middle-aged group and elderly group and the young lesion group, middle-aged lesion group and elderly lesion group were the same as those of the 3D surgery group and non-3D surgery group, so they will not be repeated here.

[0052] It should be noted that the 3-year postoperative mortality rate of each patient in the non-3D surgery group was obtained, and the middle value was taken as the standard value of the patient's 3-year postoperative mortality rate after normal distribution. Similarly, the incidence of related complications and secondary surgery of the patients can be analyzed.

[0053] It should be noted that , , , ,in , and The setting method and , and The same, so no further description.

[0054] Step 3. Comprehensive evaluation: Based on the analysis results of the 3D surgery group and the non-3D surgery group, obtain the applicable population for 3D printing technology.

[0055] In a specific example, the specific process of obtaining the applicable population of 3D printing technology is as follows: A1. When the patient age has an impact on the postoperative recovery of patients in the 3D surgery group, the postoperative recovery assessment coefficients of the young, middle-aged and elderly groups in the 3D surgery group are compared with the postoperative recovery assessment coefficients of the corresponding age groups in the non-3D surgery group. When the postoperative recovery assessment coefficient of a certain age group in the 3D surgery group is greater than the postoperative recovery assessment coefficient of the corresponding age group in the non-3D surgery group, the age group is recorded as the applicable age group for 3D printing technology, and the applicable age group for 3D printing technology is obtained accordingly.

[0056] A2. When preoperative lesions in other parts of the body have an impact on the postoperative recovery of each patient in the 3D surgery group, the postoperative recovery assessment coefficients of the young, middle-aged and elderly lesion groups in the 3D surgery group are compared with those of the corresponding lesion groups in the non-3D surgery group. When the postoperative recovery assessment coefficient of a lesion group in the 3D surgery group is greater than that of the corresponding lesion group in the non-3D surgery group, the lesion group is recorded as the lesion group applicable to 3D printing technology, and the lesion groups applicable to each 3D printing technology are obtained accordingly.

[0057] A3. Based on this, we can identify the applicable population for 3D printing technology.

[0058] Step 4: Program analysis: Obtain the basic information of the target patient, analyze whether the target patient is suitable for 3D printing technology, and then analyze the cardiovascular disease treatment plan suitable for the target patient.

[0059] In a specific example, the analysis of whether the target patient belongs to the applicable population of 3D printing technology is as follows: the impact of 3D printing technology on the patient's rehabilitation is obtained. When 3D printing technology has a positive impact on the patient's rehabilitation, the age of the target patient and whether there are lesions in other parts of the body before surgery are obtained, and compared with the age and whether there are lesions in other parts of the body before surgery of the applicable population of 3D printing technology. When the age of the target patient or the presence or absence of lesions in other parts of the body before surgery are consistent with the age or presence or absence of lesions in other parts of the body of the applicable population of 3D printing technology, it indicates that the target patient belongs to the applicable population of 3D printing technology; otherwise, it indicates that the target patient does not belong to the applicable population of 3D printing technology.

[0060] When 3D printing technology has a negative impact or no impact on the patient's recovery, it indicates that the target patient is not a suitable population for 3D printing technology.

[0061] In a specific example, the analysis is applicable to a cardiovascular disease treatment plan for a target patient, and the specific analysis process is as follows: when the target patient does not belong to the population applicable to 3D printing technology, a traditional surgical plan is used to perform surgery on the target patient.

[0062] When the target patient belongs to the population applicable for 3D printing technology, the cardiovascular lesion area of ​​the target patient is preliminarily obtained based on the imaging data, and then the interactive multi-planar reconstruction imaging function is used to obtain a clear image of the cardiovascular lesion area. The obtained cardiovascular lesion area image is compared with the cardiovascular lesion area image of each patient in the 3D surgery group, and patients with the same cardiovascular lesion area as the target patient are obtained and recorded as reference patients. The target patient is surgically treated based on the diagnosis and treatment plan of the reference patient and the basic information of the target patient.

[0063] It should be noted that the imaging data includes cardiac echocardiography and cardiac magnetic resonance imaging.

[0064] The present application provides a cardiovascular 3D printing data processing method, which classifies each cardiovascular disease patient to obtain a 3D surgery group and a non-3D surgery group, and then analyzes the basic information of the 3D surgery group and the non-3D surgery group to obtain the impact of 3D printing technology on the prognosis of each patient, determine the necessity of applying 3D printing technology to the treatment of cardiovascular diseases, and then comprehensively analyze to obtain the significant characteristics of the population applicable to 3D printing technology, and then obtain the applicable population of 3D printing technology, ensure the accurate application of 3D printing technology, and then improve the success rate of surgery and the postoperative effect of the patient. When the target patient needs treatment, it is analyzed whether the target patient belongs to the population applicable to 3D technology, and the diagnosis and treatment plan of the target patient is determined by combining the historical diagnosis and treatment records of each patient, so as to ensure the treatment effect while taking into account the treatment cost investment of the target patient.

[0065] The above contents are merely examples and explanations of the concept of the present application. The technicians in this technical field may make various modifications or additions to the described specific embodiments 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 in the present application, they should all fall within the protection scope of the present application.

Claims

1. A cardiovascular 3D printing data processing method, characterized in that: include: Step 1: Data acquisition: Obtain all 3D surgery patients and all non-3D surgery patients from the medical center, record them as 3D surgery group and non-3D surgery group respectively, and obtain basic information of the 3D surgery group and non-3D surgery group; Step 2: Data analysis: Analyze the impact of 3D printing technology on patient recovery based on the basic information of each patient, and analyze the factors that affect patient recovery in the 3D surgery group; Step 3: Comprehensive evaluation: Based on the analysis results of the 3D surgery group and the non-3D surgery group, the applicable population for 3D printing technology is obtained; Step 4: Program analysis: Obtain the basic information of the target patient, analyze whether the target patient is suitable for 3D printing technology, and then analyze the cardiovascular disease treatment plan suitable for the target patient.

2. A cardiovascular 3D printing data processing method according to claim 1, characterized in that: The specific process of obtaining each 3D surgery patient and each non-3D surgery patient and recording them as a 3D surgery group and a non-3D surgery group is as follows: All patients with cardiovascular diseases in the current city are obtained from the medical center and recorded as each patient. Among each patient, patients who have undergone cardiovascular surgery are obtained and recorded as each surgical patient. Based on the basic information of each surgical patient, each patient who underwent surgery using 3D printing technology and each patient who did not undergo surgery using 3D printing technology are extracted and recorded as each 3D surgical patient and each non-3D surgical patient, respectively. Based on this, each 3D surgical group and each non-3D surgical printing group are obtained.

3. A cardiovascular 3D printing data processing method according to claim 2, characterized in that: The basic information includes whether 3D technology is used for surgery, the number of patients in the 3D surgery group and the non-3D surgery group, the number of deaths within 3 years after surgery, the type and number of related complications, the number of secondary surgeries, the age of each patient, and whether there are lesions in other parts of the body before surgery.

4. A cardiovascular 3D printing data processing method according to claim 3, characterized in that: The specific process of analyzing the impact of 3D printing technology on the patient's recovery based on the basic information of each patient is as follows: Based on the basic information analysis of the 3D printing group, the mortality rate, incidence of related complications and incidence of secondary surgery within 3 years after surgery were obtained, which were recorded as , and , according to the calculation formula: The postoperative health impact coefficient of the 3D surgery group was obtained by analysis ,in , and They are respectively expressed as the weight factor corresponding to the patient's mortality rate within 3 years after surgery, the weight factor corresponding to the incidence of related complications, and the weight factor corresponding to the incidence of secondary surgery in the impact of cardiovascular 3D technology on the patient's postoperative health; the postoperative health impact coefficient of the non-3D surgery group was obtained by similar analysis ; The postoperative impact coefficient of the 3D surgery group was compared with that of the non-3D surgery group. When the ratio of the postoperative health impact coefficient of the 3D surgery group to the postoperative impact coefficient of the non-3D surgery group was in the interval When the ratio of the postoperative health impact coefficient of the 3D surgery group to the postoperative impact coefficient of the non-3D surgery group is within the interval When the ratio of the postoperative health impact coefficient of the 3D surgery group to the postoperative impact coefficient of the non-3D surgery group is within the interval This suggests that cardiovascular 3D technology has a positive impact on patients' surgery.

5. A cardiovascular 3D printing data processing method according to claim 4, characterized in that: The specific process of obtaining the mortality rate, incidence of related complications and incidence of secondary surgery within 3 years after surgery is as follows: The total number of patients in the 3D printing group and the number of deaths within 3 years after surgery were obtained from the historical medical records. The 3-year mortality rate of the 3D printing group was Similarly, the mortality rate within 3 years after surgery in the non-3D printing group was obtained. ; The number and occurrence frequency of relevant complication types of each patient in the 3D printing group were obtained from the historical medical records and recorded as and ,in is the number corresponding to each patient in each 3D printing group, , is any integer greater than 2, is the number corresponding to the number of each type of relevant complications, , is any integer greater than 2, according to the calculation formula The incidence of related complications in the 3D printing group was analyzed ,in represents the total number of patients in the 3D printing group, Represents the total number of related complication types. Similarly, the incidence of related complications in the non-3D printing group was obtained by analysis ; The total number of patients in the 3D printing group and the number of patients who underwent secondary surgery were obtained from the historical medical records, and the calculation formula was used The incidence of secondary surgery in the 3D printing group was analyzed Similarly, the incidence of secondary surgery in the non-3D printing group was obtained. .

6. A cardiovascular 3D printing data processing method according to claim 5, characterized in that: The factors affecting patient recovery in the 3D surgery group were analyzed, and the specific analysis process was as follows: S1. Based on the basic information of each patient, obtain the age of each patient, the presence of other lesions before surgery, the mortality rate within 3 years after surgery, the incidence of postoperative complications and the incidence of secondary surgery; S2. Obtain all patients aged 25 to 35 years, 35 to 55 years, and over 55 years old who had no lesions in other parts of the body before surgery in the 3D surgery group, and record them as the young group, middle-aged group, and elderly group, respectively. Obtain the mortality rate within three years after surgery, the incidence rate of postoperative complications, and the incidence rate of secondary surgery in each young group, middle-aged group, and elderly group, and analyze and obtain the postoperative recovery assessment coefficients of the young group, middle-aged group, and elderly group. Similarly, analyze and obtain the postoperative recovery assessment coefficients of the young group, middle-aged group, and elderly group in the non-3D surgery group; S3. Obtain patients aged 25 to 35 years, 35 to 55 years, and over 55 years old who had lesions in other parts of the body before surgery in the 3D surgery group, and record them as the young lesion group, middle-aged lesion group, and elderly lesion group, respectively. Obtain the mortality rate within three years after surgery, the incidence of postoperative complications, and the incidence of secondary surgery in the young lesion group, middle-aged lesion group, and elderly lesion group, and analyze to obtain the postoperative recovery assessment coefficients of the young lesion group, elderly lesion group, and middle-aged lesion group. Similarly, analyze to obtain the postoperative assessment coefficients of the young lesion group, middle-aged lesion group, and elderly lesion group in the non-3D surgery group; S4. Compare the postoperative recovery evaluation coefficients of the young, middle-aged and elderly groups in the 3D surgery group with those of the young, middle-aged and elderly groups in the non-3D surgery group. If the ratios of the postoperative recovery evaluation coefficients of the young, middle-aged and elderly groups are all within the range If the age of the patients is within the range of 0.1, then the age of the patients has no effect on the postoperative recovery of the patients in the 3D surgery group. Otherwise, it indicates that the age of the patients has an effect on the postoperative recovery of the patients in the 3D surgery group. S5. Compare the postoperative recovery evaluation coefficients of the young lesion group, middle-aged lesion group and elderly lesion group in the 3D surgery group with the postoperative evaluation coefficients of the young lesion group, middle-aged lesion group and elderly lesion group in the non-3D surgery group. If the ratios of the postoperative recovery evaluation coefficients of the young lesion group, middle-aged lesion group and elderly lesion group are all within the interval If the value is within the range, then preoperative lesions in other parts have no effect on the postoperative recovery of patients in the 3D surgery group. Otherwise, it indicates that preoperative lesions in other parts have an effect on the postoperative recovery of patients in the 3D surgery group.

7. A cardiovascular 3D printing data processing method according to claim 6, characterized in that: The analysis obtained the postoperative recovery evaluation coefficients of the young group, the elderly group and the middle-aged group, as well as the postoperative recovery evaluation coefficients of the young lesion group, the elderly lesion group and the middle-aged lesion group. The specific analysis process is as follows: The mortality rate, postoperative complication rate and secondary surgery rate within 3 years after surgery in the young, middle-aged and elderly groups were recorded as , and ,in The numbers corresponding to the youth group, middle-aged group and elderly group, , where numbers 1, 2 and 3 represent the youth group, middle-aged group and elderly group respectively. According to the calculation formula: The postoperative recovery assessment coefficients of the young group, middle-aged group and elderly group were obtained by analysis. ,in , and They are respectively expressed as the standard values ​​of mortality rate within 3 years after surgery, standard values ​​of incidence of postoperative complications and standard values ​​of incidence of secondary surgery. , and They are respectively expressed as the weight factor corresponding to the patient's mortality rate within 3 years after surgery, the weight factor corresponding to the incidence of postoperative complications, and the weight factor corresponding to the incidence of secondary surgery; similarly, the postoperative recovery assessment coefficients of the young lesion group, middle-aged lesion group, and elderly lesion group can be analyzed to obtain.

8. A cardiovascular 3D printing data processing method according to claim 7, characterized in that: The specific process of obtaining the applicable population of 3D printing technology is as follows: A1. When the patient's age has an impact on the postoperative recovery of the patients in the 3D surgery group, the postoperative recovery assessment coefficients of the young, middle-aged and elderly groups in the 3D surgery group are compared with those of the corresponding age groups in the non-3D surgery group. When the postoperative recovery assessment coefficient of a certain age group in the 3D surgery group is greater than that of the corresponding age group in the non-3D surgery group, the age group is recorded as the applicable age group for 3D printing technology, and the applicable age groups for each 3D printing technology are obtained accordingly; A2. When preoperative lesions in other parts of the body have an impact on the postoperative recovery of each patient in the 3D surgery group, the postoperative recovery assessment coefficients of the young lesion group, the middle-aged lesion group, and the elderly lesion group in the 3D surgery group are compared with the postoperative recovery assessment coefficients of the corresponding lesion groups in the non-3D surgery group. When the postoperative recovery assessment coefficient of a lesion group in the 3D surgery group is greater than the postoperative recovery assessment coefficient of the corresponding lesion group in the non-3D surgery group, the lesion group is recorded as the applicable age group for 3D printing technology, and the applicable lesion groups for each 3D printing technology are obtained accordingly; A3. Combining the above steps, we can determine the applicable population for 3D printing technology.

9. A cardiovascular 3D printing data processing method according to claim 8, characterized in that: The specific analysis process of analyzing whether the target patient belongs to the applicable population of 3D printing technology is as follows: Obtain the impact of 3D printing technology on the patient's rehabilitation. When 3D printing technology has a positive impact on the patient's rehabilitation, obtain the target patient's age and whether there are lesions in other parts of the body before the operation, and compare them with the age and whether there are lesions in other parts of the body before the operation of the population applicable to 3D printing technology. When the age of the target patient or the presence or absence of lesions in other parts of the body before the operation meets the age or the presence or absence of lesions in other parts of the body before the operation of the population applicable to 3D printing technology, it indicates that the target patient belongs to the population applicable to 3D printing technology. Otherwise, it indicates that the target patient does not belong to the population applicable to 3D printing technology. When 3D printing technology has a negative impact or no impact on the patient's recovery, it indicates that the target patient is not a suitable population for 3D printing technology.

10. A cardiovascular 3D printing data processing method according to claim 9, characterized in that: The analysis is applicable to the treatment plan for cardiovascular diseases of target patients, and the specific analysis process is as follows: When the target patient does not belong to the population suitable for 3D printing technology, the target patient is operated on using the traditional surgical plan; When the target patient belongs to the population applicable for 3D printing technology, the cardiovascular lesion area of ​​the target patient is preliminarily obtained based on the imaging data, and then the interactive multi-planar reconstruction imaging function is used to obtain a clear image of the cardiovascular lesion area. The obtained cardiovascular lesion area image is compared with the cardiovascular lesion area image of each patient in the 3D surgery group, and patients with the same cardiovascular lesion area as the target patient are obtained and recorded as reference patients. The target patient is surgically treated based on the diagnosis and treatment plan of the reference patient and the basic information of the target patient.

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