CN113066562A - Medical image transmission method and system based on 5g - Google Patents
Medical image transmission method and system based on 5g Download PDFInfo
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- G16H30/00—ICT specially adapted for the handling or processing of medical images
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Abstract
The invention discloses a medical image transmission method and a medical image transmission system based on 5g, which comprise a patient application module, a doctor application module, a medical image storage module, a medical image classification module, a lesion position confirmation module, a lesion position comparison module, a re-diagnosis stage analysis module, a patient diagnosis stage matching module, a patient diagnosis experience exchange module, a medical image transmission module, an expert information integration module and a doctor-patient information matching module, and have the advantages that: the medical images are classified, the times of the patient's return visit and the time of the next return visit are counted, the lesion positions of different patients in the medical images and the lesion degrees of the lesion positions are judged, the stage of the patient in the visit is analyzed, the return visit time of the patient is intelligently adjusted, communication channels are provided for the patients, the return visit rate is improved, the comprehensive ability of a doctor is further analyzed, the doctor is matched with the patient, and a consultation channel is provided for the patient.
Description
Technical Field
The invention relates to the technical field of image transmission, in particular to a medical image transmission method and system based on 5 g.
Background
The medical image transmission system is applied to a hospital image department, has the main task of storing various daily medical images including images generated by equipment such as nuclear magnetism, CT, ultrasound, various X-ray machines, various infrared instruments, microscopy and the like in a digital mode in a large quantity through an analog interface, a DICOM interface or a network interface, can be quickly called and used under certain authorization when needed, and simultaneously increases some management functions for auxiliary diagnosis, has important functions in transmitting data and organizing and storing data among various image equipment, has the advantages of reducing material cost and management cost, improving working efficiency and hospital medical level, greatly simplifying the working process of doctors, putting more time and energy on diagnosis, and being beneficial to improving the diagnosis efficiency of hospitals, meanwhile, due to the introduction of various image processing technologies, the lesions which are difficult to perceive in the past become clear and visible, reference can be provided for doctors, the doctors can make more accurate diagnoses according to past experiences, remote medical treatment is possible due to the digitalized resource storage, and besides, the system also provides support for the accumulation of hospital resources and technologies, and technical communication among hospitals can be promoted through the remote medical treatment.
However, the prior art still has many disadvantages, how to grasp gold time for treatment for diseases, regular re-diagnosis is critical, from the perspective of patients, the treatment effect needs to be ensured through re-diagnosis, the purpose of re-diagnosis is to know the recovery condition of the patient, but the re-diagnosis rate is low because of too long time span or forgetting of the patient, some patients do not go through re-diagnosis after remembering the re-diagnosis time, and other patients urgently need surgery because of the disease condition, but for some patients, the surgery is not performed, the surgery itself has a natural fear, and no channel is available to know the relevant information about the surgery, the success rate of the surgery is not clear, which are the reasons for fear, and in the conditioning stage after diagnosis and treatment, the patient is not clear how to condition, the recovery period is long or the disease recurs due to improper conditioning, and the problems are not well solved for the existing medical image transmission system.
Based on the above problems, it is highly desirable to provide a medical image transmission method and system based on 5g, which classify medical images and count the number of times of patient review and the time of next review, determine the lesion positions and the lesion degrees of the lesion positions of different patients in the medical images, analyze the stage of the patients in which the patients are in review, intelligently adjust the review time of the patients, and provide an exchange channel between the patients, so as to improve the review rate, further analyze the comprehensive ability of doctors, match the doctors with the patients, and provide a consultation channel for the patients.
Disclosure of Invention
The invention aims to provide a medical image transmission method and system based on 5g, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
a medical image transmission system based on 5g comprises a patient application module, a doctor application module, a medical image storage module, a medical image classification module, a lesion position confirmation module, a lesion position comparison module, a re-diagnosis stage analysis module, a patient diagnosis stage matching module, a patient diagnosis experience communication module, a medical image transmission module, an expert information integration module and a doctor-patient information matching module,
the patient application module is used for logging in or registering a patient, the doctor application module is used for logging in or registering a doctor, the medical image storage module is used for storing medical images in a classified manner, the medical image classification module is used for classifying the medical images according to patient information, an examination part, an execution department and an application department, the lesion position confirmation module is used for confirming the position of a human lesion in the medical images, the lesion position comparison module is used for comparing whether the lesion positions are the same and the range size of the lesion positions, the review stage analysis module is used for counting the review times of the patient and the next review time and sending a review prompt to the patient application module and the doctor application module according to the next review time, and the review stage analysis module also obtains corresponding patient information according to the counting and analysis results of the next review time, the patient treatment stage matching module is used for matching treatment stages of different patients according to the comparison result of the lesion position comparison module and the statistical result of the re-diagnosis stage analysis module, the patient visit experience communication module is used for recommending other patients in the same visit stage for the patient to carry out experience communication according to the matching result of the patient visit stage matching module, the medical image transmission module is used for transmitting the acquired medical image to the medical image storage module for storage, the expert information integration module is used for analyzing the doctor information according to the working age, the field of excellence, the department of any post, the operation times, the operation success rate and the belonging hospital of the doctor, in the transmission process of the medical image, the doctor-patient information matching module is used for matching the doctor and the patient according to the doctor information obtained by the analysis of the expert information integration module and the patient information.
Further, the medical image classification module is connected with the medical image transmission module, the medical image transmission module is connected with the medical image storage module, the medical image classification module classifies the acquired medical images according to the information of an executive department, an examination part, an application department and a patient, classifies the medical images belonging to the same patient according to the information of the patient, further classifies the medical images belonging to the same patient according to the executive department, the examination part and the application department, classifies the executive department, the examination part and the application department into one class again in the medical image classification of the same patient, the medical image transmission module transmits the classified medical images to the medical image storage module, and the medical image storage module stores the acquired medical images in a classified manner according to the classification result of the medical image classification module, medical images are classified and stored, so that later retrieval is facilitated.
Further, the lesion position confirming module is connected with the medical image storage module to further acquire lesion information in the medical image, the lesion information includes specific position information and specific range information, the lesion position comparing module acquires the specific position information and the specific range information of any two medical images belonging to the same category to confirm whether the lesion positions and the lesion degrees of any two medical images are consistent,
determining the geometric central points A and B of the lesion ranges of the any two medical images, further connecting the geometric central points A and B, and acquiring the distance L between the A and the B, wherein when the L is less than or equal to a distance threshold value L0, the lesion positions of the any two medical images are consistent;
and further acquiring lesion ranges Sa and Sb of any two medical images, and when the (Sa-Sb)/S0 value is less than or equal to a preset value, namely the lesion degrees of any two medical images are consistent, wherein S0 is a range difference threshold value, analyzing the lesion position and the lesion range in the medical images, and screening to obtain the patient information in the same period according to the lesion position and the lesion range.
Further, the review stage analysis module acquires the next review time of the patient, and sends a review message prompt to the corresponding patient application module and the doctor application module at the next review time, if the current patient does not need the review, the patient application module displays the current no review appointment, the review stage analysis module also makes statistics on the information of the patient going to the same application department for the review on the same day, and the patient review stage matching module acquires the statistical result of the review stage analysis module; the review stage analysis module is connected with the medical image storage module, acquires medical images of different patients, further acquires lesion positions and lesion ranges in the medical images of the same examination part, an execution department and an application department, acquires patient information corresponding to the medical images when the lesion positions and the lesion ranges are consistent by comparing the lesion positions and the lesion ranges, further acquires the review times and next review time of the different patients according to the patient information, and compares the review times and next review time of the different patients.
Further, the review stage analysis module obtains review times and next review time between different patients, if the review times of any two patients are the same, the number of days between the next review times is n, and n is less than or equal to the threshold value of the number of days between the next review times, the next review time of any two patients is adjusted, if the next review time of one patient is T0, the next review time of the other patient is T1, if T0 is prior to T1, the next review time of one patient is delayed, and the next review time of the other patient is advanced, wherein the delay time and the advance time are determined according to the number of days n between T0 and T1, if n is singular and is not equal to 0, the adjusted next review times are T0+ (n +1)/2 and T1- (n +1)/2 respectively, if n is a double number and is not equal to 0, the adjusted next double-consultation time is respectively T0+ (n +1)/2 and T1- (n +2)/2, the double-consultation time of the patient is intelligently adjusted according to the time interval between the next double-consultation times, because in real life, people often feel that a registration is not needed when the people perform the double-consultation, and the people can directly find the doctor to perform the inquiry, so that the consultation sequence can be disturbed, through adjusting the double-consultation time of the patient, people with the similar next double-consultation time can go to the hospital at the same time to perform the double-consultation, the doctor can reasonably arrange the self-time according to the number of the double-consultation people and the double-consultation time, and because the illness states of some patients are at the same stage or similar, the doctor can perform the double-consultation on the patients at the same time, so that the inquiry time of the doctor can be greatly saved, and the operation efficiency;
if the number of the follow-up visits of any two patients is the same, the interval days between the next follow-up visits is n, and n is larger than the interval days threshold, the next follow-up visits of any two patients do not need to be adjusted.
Further, the review stage analysis module is connected with the patient review stage matching module, the patient review stage matching module obtains the analysis result and the statistical result of the review stage analysis module, the statistical result is the first patient information going to the same application department for review on the same day,
the analysis result is second patient information with the same number of times of the follow-up visits, and the interval days between the next follow-up visits do not exceed an interval days threshold, the patient visit stage matching module obtains the next follow-up visit time corresponding to the first patient information and the adjusted next follow-up visit time corresponding to the second patient information, the follow-up visit stage analysis module further performs a follow-up visit appointment prompt on the corresponding patient according to the adjusted next follow-up visit time, so as to prevent the patient from making a detailed visit when the patient goes to the follow-up visit due to the change of the follow-up visit time, or to prevent the patient from assuming the original follow-up visit time, in addition, for the disease, how to grasp the gold time of the treatment, the regular follow-up visit is critical, from the perspective of the patient, the effect of the treatment needs to be ensured through the follow-up visit, the purpose of the follow-up visit is to understand the recovery condition of the patient, but the follow-up visit rate is low, moreover, even some patients remember the time of the re-diagnosis, but do not go to the re-diagnosis by borrowing the rest of the mouth, the re-diagnosis appointment prompting is carried out on the patients, the re-diagnosis rate can be improved, the re-diagnosis time of the patients meeting the conditions is adjusted, the re-diagnosis time of the patients in the same clinic stage is the same day, a communication channel is provided for the patients in the same clinic stage, the communication of the illness state among the patients can be facilitated,
the patient treatment stage matching module pushes other patients who are treated again in the same day to any patient according to the next treatment time, and the patients can communicate with each other through the patient treatment experience communication module.
Further, the expert information integration module acquires the working age, the field of excellence, the department of any post, the operation times, the operation success rate and the doctor information of the hospital,
the expert information integration module is prestored with comprehensive strength evaluation values of all hospitals, further obtains the working years, the areas of excellence, the departments of any employment, the operation times N and the operation success rate P of doctors of all hospitals, can calculate the average operation times N1 of each doctor every year according to the operation times N and the working years of the doctors, obtains the information of any doctor when the average operation times N1 of any doctor is more than or equal to the average operation times threshold value,
further comparing the number of operations N with a preset value, and when the number of operations N is larger than or equal to the preset value, acquiring doctor information of which the operation success rate P is larger than or equal to a first success rate threshold value; and when the operation times N are smaller than the preset value, acquiring doctor information with the operation success rate P larger than or equal to the second power threshold value, wherein the sample base numbers are the operation times of doctors for different sample base numbers, and the operation success rates of different times cannot be directly compared with each other due to the number of the operation times, so that the operation time threshold value is set, namely the size of the sample base number is limited, different operation success rate threshold values are preset, and the doctor information is screened by comparing the operation success rate with the operation success rate threshold value.
Further, expert information integration module transmits doctor information to doctor-patient information matching module, doctor-patient information matching module still connects the stage of the consultation analysis module, doctor-patient information matching module further acquires the patient information after the stage of the consultation analysis module carries out analysis and statistics, in the transmission process of medical images, doctor-patient information matching module matches the doctor information and the patient information who acquire according to the expert field and the examination position, the application department and the doctor and patient information who are unanimous with the arbitrary department, and doctor and patient can communicate through doctor application module and patient application module.
Further, a medical image transmission method based on 5g, the medical image transmission method comprises the following steps: s1: the medical image classification module is connected with the medical image transmission module, the medical image transmission module is connected with the medical image storage module, the medical image classification module classifies the acquired medical images according to information of an execution department, an examination part, an application department and a patient, classifies the medical images belonging to the same patient according to the information of the patient, further classifies the medical images belonging to the same patient according to the execution department, the examination part and the application department, the medical images of the same patient are classified into one class again when the execution department, the examination part and the application department are consistent in the medical image classification of the same patient, the medical image transmission module transmits the classified medical images to the medical image storage module, and the medical image storage module stores the acquired medical images in a classified mode according to the classification result of the medical image classification module;
s2: the lesion position confirming module is connected with the medical image storage module and further obtains lesion information in the medical image, the lesion information comprises specific position information and specific range information, the lesion position comparing module obtains the specific position information and the specific range information of any two medical images belonging to the same classification so as to confirm whether the lesion positions and the lesion degrees of any two medical images are consistent or not,
determining geometric center points A and B of lesion ranges of any two medical images, further connecting the geometric center points A and B, obtaining a distance L between the A and the B, and when the L is smaller than or equal to a distance threshold value L0, enabling lesion positions of any two medical images to be consistent;
further acquiring lesion ranges Sa and Sb of any two medical images, and when the value of (Sa-Sb)/S0 is less than or equal to a preset value, namely the lesion degrees of any two medical images are consistent, wherein S0 is a range difference threshold value;
s3: the method comprises the following steps that a re-diagnosis stage analysis module acquires the next re-diagnosis time of a patient and sends a re-diagnosis message prompt to a corresponding patient application module and a corresponding doctor application module at the next re-diagnosis time, if the current patient does not need re-diagnosis, the patient application module displays the current re-diagnosis reservation, the re-diagnosis stage analysis module also carries out statistics on the information of the patient going to the same application department for re-diagnosis on the same day, and a patient seeing-doctor stage matching module acquires the statistical result of the re-diagnosis stage analysis module;
the review stage analysis module is connected with the medical image storage module, acquires medical images of different patients, further acquires lesion positions and lesion ranges in the medical images of the same examination part, an execution department and an application department, acquires patient information corresponding to the medical images when the lesion positions and the lesion ranges are consistent by comparing the lesion positions and the lesion ranges, further acquires the review times and next review time of different patients according to the patient information, and compares the review times and next review time of different patients;
s4: the method comprises the steps that a re-diagnosis stage analysis module obtains the number of re-diagnoses of different patients and the time of the next re-diagnosis, if the number of re-diagnoses of any two patients is the same, the number of days between the next re-diagnosis is n, and n is smaller than or equal to an interval number threshold, the time of the next re-diagnosis of any two patients is adjusted, if the time of the next re-diagnosis of one patient is T0, the time of the next re-diagnosis of the other patient is T1, if T0 is prior to T1, the time of the next re-diagnosis of one patient is delayed, and the time of the next re-diagnosis of the other patient is advanced, wherein the delay time and the advance time are determined according to the interval number n between T0 and T1, if n is singular and is not equal to 0, the adjusted time of the next re-diagnosis is respectively T0+ (n +1)/2 and T1- (n +1)/2, and if n is even, the adjusted time of the next re-diagnosis is T0 (n + 1)/n +2) and T1 + (n +2) 2) 2;
if the number of the follow-up visits of any two patients is the same, the interval days between the next follow-up visits is n, and n is greater than the interval days threshold, the next follow-up visits of any two patients do not need to be adjusted;
s5: the re-diagnosis stage analysis module is connected with the patient re-diagnosis stage matching module, the patient re-diagnosis stage matching module acquires the analysis result and the statistical result of the re-diagnosis stage analysis module, the statistical result is the first patient information going to the same application department for re-diagnosis on the same day,
the analysis result is second patient information with the same number of times of the follow-up visits and interval days between the next follow-up visits not exceeding an interval day threshold, the patient visit stage matching module acquires the next follow-up visit time corresponding to the first patient information and the adjusted next follow-up visit time corresponding to the second patient information, the follow-up visit stage analysis module also carries out follow-up visit appointment prompting on the corresponding patient according to the adjusted next follow-up visit time, the patient visit stage matching module pushes other patients for the same day to any patient according to the next follow-up visit time, and the patients can communicate with each other through the patient visit experience communication module;
s6: the expert information integration module acquires the working age, the adequacy field, the department of any post, the operation times, the operation success rate of the doctor and the comprehensive analysis of the doctor information in the belonging hospital,
the expert information integration module is prestored with comprehensive strength evaluation values of all hospitals, further obtains the working years, the areas of excellence, the departments of any employment, the operation times N and the operation success rate P of doctors of all hospitals, can calculate the average operation times N1 of each doctor every year according to the operation times N and the working years of the doctors, obtains the information of any doctor when the average operation times N1 of any doctor is more than or equal to the average operation times threshold value,
further comparing the number of operations N with a preset value, and when the number of operations N is larger than or equal to the preset value, acquiring doctor information of which the operation success rate P is larger than or equal to a first success rate threshold value; when the operation times N are smaller than a preset value, acquiring doctor information of which the operation success rate P is larger than or equal to a second power threshold;
s7: expert information integration module transmits doctor information to doctor and patient information matching module, doctor and patient information matching module still connects the stage analysis module of diagnosing again, doctor and patient information matching module further acquires the patient information after stage analysis module of diagnosing again carries out analysis and statistics, in the transmission process of medical image, doctor and patient information matching module will acquire doctor information and patient information according to the doctor and the patient information phase-match that excel in the field and inspection position, application department and arbitrary position are unanimous, and doctor and patient can communicate through doctor application module and patient application module.
Compared with the prior art, the invention has the following beneficial effects: the invention classifies the medical images, counts the times of the patient's return visit and the time of the next return visit, judges the lesion positions of different patients in the medical images and the lesion degrees at the lesion positions, analyzes the stage of the patient in the visit, intelligently adjusts the return visit time of the patient, and provides communication channels for the patients, so as to improve the return visit rate, further analyze the comprehensive ability of the doctor, match the doctor with the patient, and provide consultation channels for the patient.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block schematic diagram of a 5 g-based medical image transmission system of the present invention;
fig. 2 is a step diagram of the medical image transmission method based on 5g according to the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
a medical image transmission system based on 5g comprises a patient application module, a doctor application module, a medical image storage module, a medical image classification module, a lesion position confirmation module, a lesion position comparison module, a re-diagnosis stage analysis module, a patient diagnosis stage matching module, a patient diagnosis experience communication module, a medical image transmission module, an expert information integration module and a doctor-patient information matching module,
the system comprises a patient application module, a doctor application module, a medical image storage module, a medical image classification module, a lesion position confirmation module, a lesion position comparison module, a review stage analysis module, a patient review stage matching module and a review stage analysis module, wherein the patient application module is used for logging in or registering patients, the doctor application module is used for logging in or registering doctors, the medical image storage module is used for storing medical images in a classified manner, the medical image classification module is used for classifying the medical images according to patient information, examination parts, execution departments and application departments, the lesion position confirmation module is used for confirming human lesion positions in the medical images, the lesion position comparison module is used for comparing whether the lesion positions are the same and the range size of the lesion positions, the review stage analysis module is used for counting the review times of the patients and the next review time, and sending review prompts to the patient application module and the doctor application module according to the next review time, the review stage analysis module also obtains corresponding patient information according to the counting and analysis The doctor information matching module is used for matching doctors and patients according to doctor information obtained by analysis of the expert information integration module and the doctor information of the patients in the transmission process of the medical images.
The medical image classification module is connected with the medical image transmission module, the medical image transmission module is connected with the medical image storage module, the medical image classification module classifies the acquired medical images according to the information of an execution department, an examination part, an application department and a patient, the medical images belonging to the same patient are classified according to the information of the patient, the medical images belonging to the same patient are further classified according to the execution department, the examination part and the application department, the medical images in the same patient are classified into one class again when the execution department, the examination part and the application department are consistent, the medical image transmission module transmits the classified medical images to the medical image storage module, and the medical image storage module stores the acquired medical images in a classified mode according to the classification result of the medical image classification module.
The lesion position confirming module is connected with the medical image storage module and further obtains lesion information in the medical image, the lesion information comprises specific position information and specific range information, the lesion position comparing module obtains the specific position information and the specific range information of any two medical images belonging to the same classification so as to confirm whether the lesion positions and the lesion degrees of any two medical images are consistent or not,
determining geometric center points A and B of lesion ranges of any two medical images, further connecting the geometric center points A and B, obtaining a distance L between the A and the B, and when the L is smaller than or equal to a distance threshold value L0, enabling lesion positions of any two medical images to be consistent;
further acquiring lesion ranges Sa and Sb of any two medical images, and when the value of (Sa-Sb)/S0 is less than or equal to a preset value, namely the lesion degrees of any two medical images are consistent, wherein S0 is a range difference threshold value.
The method comprises the following steps that a re-diagnosis stage analysis module acquires the next re-diagnosis time of a patient and sends a re-diagnosis message prompt to a corresponding patient application module and a corresponding doctor application module at the next re-diagnosis time, if the current patient does not need re-diagnosis, the patient application module displays the current re-diagnosis reservation, the re-diagnosis stage analysis module also carries out statistics on the information of the patient going to the same application department for re-diagnosis on the same day, and a patient seeing-doctor stage matching module acquires the statistical result of the re-diagnosis stage analysis module;
the review stage analysis module is connected with the medical image storage module, acquires medical images of different patients, further acquires lesion positions and lesion ranges in the medical images of the same examination part, the execution department and the application department, acquires patient information corresponding to the medical images when the lesion positions and the lesion ranges are consistent by comparing the lesion positions and the lesion ranges, further acquires the review times and next review time of different patients according to the patient information, and compares the review times and next review time of different patients.
The method comprises the steps that a re-diagnosis stage analysis module obtains the number of re-diagnoses of different patients and the time of the next re-diagnosis, if the number of re-diagnoses of any two patients is the same, the number of days between the next re-diagnosis is n, and n is smaller than or equal to an interval number threshold, the time of the next re-diagnosis of any two patients is adjusted, if the time of the next re-diagnosis of one patient is T0, the time of the next re-diagnosis of the other patient is T1, if T0 is prior to T1, the time of the next re-diagnosis of one patient is delayed, and the time of the next re-diagnosis of the other patient is advanced, wherein the delay time and the advance time are determined according to the interval number n between T0 and T1, if n is singular and is not equal to 0, the adjusted time of the next re-diagnosis is respectively T0+ (n +1)/2 and T1- (n +1)/2, and if n is even, the adjusted time of the next re-diagnosis is T0 (n + 1)/n +2) and T1 + (n +2) 2) 2;
if the number of the follow-up visits of any two patients is the same, the interval days between the next follow-up visits is n, and n is larger than the interval days threshold, the next follow-up visits of any two patients do not need to be adjusted.
The re-diagnosis stage analysis module is connected with the patient re-diagnosis stage matching module, the patient re-diagnosis stage matching module acquires the analysis result and the statistical result of the re-diagnosis stage analysis module, the statistical result is the first patient information going to the same application department for re-diagnosis on the same day,
the analysis result is second patient information with the same number of times of the follow-up visits and the interval days between the next follow-up visits not exceeding the interval days threshold, the patient visit stage matching module obtains the next follow-up visit time corresponding to the first patient information and the adjusted next follow-up visit time corresponding to the second patient information, the follow-up visit stage analysis module further carries out follow-up visit appointment prompting on the corresponding patient according to the adjusted next follow-up visit time, the patient visit stage matching module pushes other patients who have been subjected to the follow-up visit on the same day to any patient according to the next follow-up visit time, and the patients can communicate through the patient visit experience communication module.
The expert information integration module acquires the working age, the adequacy field, the department of any post, the operation times, the operation success rate of the doctor and the comprehensive analysis of the doctor information in the belonging hospital,
the expert information integration module is prestored with comprehensive strength evaluation values of all hospitals, further obtains the working years, the areas of excellence, the departments of any employment, the operation times N and the operation success rate P of doctors of all hospitals, can calculate the average operation times N1 of each doctor every year according to the operation times N and the working years of the doctors, obtains the information of any doctor when the average operation times N1 of any doctor is more than or equal to the average operation times threshold value,
further comparing the number of operations N with a preset value, and when the number of operations N is larger than or equal to the preset value, acquiring doctor information of which the operation success rate P is larger than or equal to a first success rate threshold value; and when the operation times N are smaller than a preset value, acquiring the doctor information of which the operation success rate P is larger than or equal to a second power threshold value.
The expert information integration module transmits doctor information to the doctor-patient information matching module, the doctor-patient information matching module is further connected with the review stage analysis module, the doctor-patient information matching module further acquires patient information analyzed and counted by the review stage analysis module, in the transmission process of medical images, the doctor-patient information matching module matches the acquired doctor information and the acquired patient information with the information of doctors and patients according to the adequacy field, the positions of the doctors and the patients are checked, and the application departments are matched with the doctor and the patient information consistent with the arbitrary departments, and the doctors and the patients can communicate through the doctor application module and the patient application module.
A medical image transmission method based on 5g comprises the following steps:
s1: the medical image classification module is connected with the medical image transmission module, the medical image transmission module is connected with the medical image storage module, the medical image classification module classifies the acquired medical images according to information of an execution department, an examination part, an application department and a patient, classifies the medical images belonging to the same patient according to the information of the patient, further classifies the medical images belonging to the same patient according to the execution department, the examination part and the application department, the medical images of the same patient are classified into one class again when the execution department, the examination part and the application department are consistent in the medical image classification of the same patient, the medical image transmission module transmits the classified medical images to the medical image storage module, and the medical image storage module stores the acquired medical images in a classified mode according to the classification result of the medical image classification module;
s2: the lesion position confirming module is connected with the medical image storage module and further obtains lesion information in the medical image, the lesion information comprises specific position information and specific range information, the lesion position comparing module obtains the specific position information and the specific range information of any two medical images belonging to the same classification so as to confirm whether the lesion positions and the lesion degrees of any two medical images are consistent or not,
determining geometric center points A and B of lesion ranges of any two medical images, further connecting the geometric center points A and B, obtaining a distance L between the A and the B, and when the L is smaller than or equal to a distance threshold value L0, enabling lesion positions of any two medical images to be consistent;
further acquiring lesion ranges Sa and Sb of any two medical images, and when the value of (Sa-Sb)/S0 is less than or equal to a preset value, namely the lesion degrees of any two medical images are consistent, wherein S0 is a range difference threshold value;
s3: the method comprises the following steps that a re-diagnosis stage analysis module acquires the next re-diagnosis time of a patient and sends a re-diagnosis message prompt to a corresponding patient application module and a corresponding doctor application module at the next re-diagnosis time, if the current patient does not need re-diagnosis, the patient application module displays the current re-diagnosis reservation, the re-diagnosis stage analysis module also carries out statistics on the information of the patient going to the same application department for re-diagnosis on the same day, and a patient seeing-doctor stage matching module acquires the statistical result of the re-diagnosis stage analysis module;
the review stage analysis module is connected with the medical image storage module, acquires medical images of different patients, further acquires lesion positions and lesion ranges in the medical images of the same examination part, an execution department and an application department, acquires patient information corresponding to the medical images when the lesion positions and the lesion ranges are consistent by comparing the lesion positions and the lesion ranges, further acquires the review times and next review time of different patients according to the patient information, and compares the review times and next review time of different patients;
s4: the method comprises the steps that a re-diagnosis stage analysis module obtains the number of re-diagnoses of different patients and the time of the next re-diagnosis, if the number of re-diagnoses of any two patients is the same, the number of days between the next re-diagnosis is n, and n is smaller than or equal to an interval number threshold, the time of the next re-diagnosis of any two patients is adjusted, if the time of the next re-diagnosis of one patient is T0, the time of the next re-diagnosis of the other patient is T1, if T0 is prior to T1, the time of the next re-diagnosis of one patient is delayed, and the time of the next re-diagnosis of the other patient is advanced, wherein the delay time and the advance time are determined according to the interval number n between T0 and T1, if n is singular and is not equal to 0, the adjusted time of the next re-diagnosis is respectively T0+ (n +1)/2 and T1- (n +1)/2, and if n is even, the adjusted time of the next re-diagnosis is T0 (n + 1)/n +2) and T1 + (n +2) 2) 2;
if the number of the follow-up visits of any two patients is the same, the interval days between the next follow-up visits is n, and n is greater than the interval days threshold, the next follow-up visits of any two patients do not need to be adjusted;
s5: the re-diagnosis stage analysis module is connected with the patient re-diagnosis stage matching module, the patient re-diagnosis stage matching module acquires the analysis result and the statistical result of the re-diagnosis stage analysis module, the statistical result is the first patient information going to the same application department for re-diagnosis on the same day,
the analysis result is second patient information with the same number of times of the follow-up visits and interval days between the next follow-up visits not exceeding an interval day threshold, the patient visit stage matching module acquires the next follow-up visit time corresponding to the first patient information and the adjusted next follow-up visit time corresponding to the second patient information, the follow-up visit stage analysis module also carries out follow-up visit appointment prompting on the corresponding patient according to the adjusted next follow-up visit time, the patient visit stage matching module pushes other patients for the same day to any patient according to the next follow-up visit time, and the patients can communicate with each other through the patient visit experience communication module;
s6: the expert information integration module acquires the working age, the adequacy field, the department of any post, the operation times, the operation success rate of the doctor and the comprehensive analysis of the doctor information in the belonging hospital,
the expert information integration module is prestored with comprehensive strength evaluation values of all hospitals, further obtains the working years, the areas of excellence, the departments of any employment, the operation times N and the operation success rate P of doctors of all hospitals, can calculate the average operation times N1 of each doctor every year according to the operation times N and the working years of the doctors, obtains the information of any doctor when the average operation times N1 of any doctor is more than or equal to the average operation times threshold value,
further comparing the number of operations N with a preset value, and when the number of operations N is larger than or equal to the preset value, acquiring doctor information of which the operation success rate P is larger than or equal to a first success rate threshold value; when the operation times N are smaller than a preset value, acquiring doctor information of which the operation success rate P is larger than or equal to a second power threshold;
s7: the expert information integration module transmits doctor information to the doctor-patient information matching module, the doctor-patient information matching module is further connected with the review stage analysis module, the doctor-patient information matching module further acquires patient information analyzed and counted by the review stage analysis module, in the transmission process of medical images, the doctor-patient information matching module matches the acquired doctor information and the acquired patient information with the information of doctors and patients according to the adequacy field, the positions of the doctors and the patients are checked, and the application departments are matched with the doctor and the patient information consistent with the arbitrary departments, and the doctors and the patients can communicate through the doctor application module and the patient application module.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A 5 g-based medical image transmission system, characterized by: the medical image transmission system comprises a patient application module, a doctor application module, a medical image storage module, a medical image classification module, a lesion position confirmation module, a lesion position comparison module, a re-diagnosis stage analysis module, a patient diagnosis stage matching module, a patient diagnosis experience communication module, a medical image transmission module, an expert information integration module and a doctor-patient information matching module,
the patient application module is used for logging in or registering a patient, the doctor application module is used for logging in or registering a doctor, the medical image storage module is used for storing medical images in a classified manner, the medical image classification module is used for classifying the medical images according to patient information, an examination part, an execution department and an application department, the lesion position confirmation module is used for confirming the position of a human lesion in the medical images, the lesion position comparison module is used for comparing whether the lesion positions are the same and the range size of the lesion positions, the review stage analysis module is used for counting the review times of the patient and the next review time and sending a review prompt to the patient application module and the doctor application module according to the next review time, and the review stage analysis module also obtains corresponding patient information according to the counting and analysis results of the next review time, the patient treatment stage matching module is used for matching treatment stages of different patients according to the comparison result of the lesion position comparison module and the statistical result of the re-diagnosis stage analysis module, the patient visit experience communication module is used for recommending other patients in the same visit stage for the patient to carry out experience communication according to the matching result of the patient visit stage matching module, the medical image transmission module is used for transmitting the acquired medical image to the medical image storage module for storage, the expert information integration module is used for analyzing the doctor information according to the working age, the field of excellence, the department of any post, the operation times, the operation success rate and the belonging hospital of the doctor, in the transmission process of the medical image, the doctor-patient information matching module is used for matching the doctor and the patient according to the doctor information obtained by the analysis of the expert information integration module and the patient information.
2. The 5 g-based medical image transmission system according to claim 1, wherein: the medical image classification module is connected with the medical image transmission module, the medical image transmission module is connected with the medical image storage module, the medical image classification module classifies the acquired medical images according to information of an executive department, an examination part, an application department and a patient, classifying the medical images belonging to the same patient according to the patient information, further classifying the medical images belonging to the same patient according to the executive department, the examination part and the application department, the medical image classification of the same patient classifies the department of execution, the part to be checked and the department applying for the same department into one category again, the medical image transmission module transmits the classified medical images to the medical image storage module, the medical image storage module is used for storing the acquired medical images in a classified mode according to the classification result of the medical image classification module.
3. The 5 g-based medical image transmission system according to claim 1, wherein: the lesion position confirming module is connected with the medical image storage module and further obtains lesion information in the medical image, the lesion information comprises specific position information and specific range information, the lesion position comparing module obtains the specific position information and the specific range information of any two medical images belonging to the same classification so as to confirm whether the lesion positions and the lesion degrees of any two medical images are consistent or not,
determining the geometric central points A and B of the lesion ranges of the any two medical images, further connecting the geometric central points A and B, and acquiring the distance L between the A and the B, wherein when the L is less than or equal to a distance threshold value L0, the lesion positions of the any two medical images are consistent;
further acquiring lesion ranges Sa and Sb of any two medical images, and when the value of (Sa-Sb)/S0 is less than or equal to a preset value, namely the lesion degrees of any two medical images are consistent, wherein S0 is a range difference threshold value.
4. The 5 g-based medical image transmission system according to claim 1, wherein: the method comprises the steps that a follow-up examination stage analysis module acquires the next follow-up examination time of a patient and sends a follow-up examination message prompt to a corresponding patient application module and a corresponding doctor application module at the next follow-up examination time, if the current patient does not need the follow-up examination, the patient application module displays the current no-follow-up examination appointment, the follow-up examination stage analysis module also carries out statistics on the information of the patient going to the same application department for the follow-up examination on the same day, and a patient follow-up examination stage matching module acquires the statistical result of the follow-up examination stage analysis module;
the review stage analysis module is connected with the medical image storage module, acquires medical images of different patients, further acquires lesion positions and lesion ranges in the medical images of the same examination part, an execution department and an application department, acquires patient information corresponding to the medical images when the lesion positions and the lesion ranges are consistent by comparing the lesion positions and the lesion ranges, further acquires the review times and next review time of the different patients according to the patient information, and compares the review times and next review time of the different patients.
5. The 5 g-based medical image transmission system according to claim 4, wherein: the review stage analysis module acquires the review times and the next review time of different patients, if the review times of any two patients are the same, the interval days between the next review times are n, and n is less than or equal to the interval days threshold, the next review time of any two patients is adjusted, if the next review time of one patient is T0, the next review time of the other patient is T1, if T0 is prior to T1, the next review time of the one patient is delayed, and the next review time of the other patient is advanced, wherein the delay time and the advance time are determined according to the interval days n between T0 and T1, if n is singular and is not equal to 0, the adjusted next review times are respectively T0+ (n +1)/2 and T1- (n +1)/2, if n is dual and is not equal to 0, the adjusted next consultation time is T0+ (n +1)/2 and T1- (n +2)/2 respectively;
if the number of the follow-up visits of any two patients is the same, the interval days between the next follow-up visits is n, and n is larger than the interval days threshold, the next follow-up visits of any two patients do not need to be adjusted.
6. The 5 g-based medical image transmission system according to claim 4 or 5, wherein: the re-diagnosis stage analysis module is connected with the patient re-diagnosis stage matching module, the patient re-diagnosis stage matching module acquires the analysis result and the statistical result of the re-diagnosis stage analysis module, the statistical result is the information of a first patient going to the same application department for re-diagnosis on the same day,
the analysis result is second patient information with the same number of times of the follow-up visits and the interval days between the next follow-up visits not exceeding the interval days threshold, the patient visit stage matching module obtains the next follow-up visit time corresponding to the first patient information and the adjusted next follow-up visit time corresponding to the second patient information, the follow-up visit stage analysis module also carries out follow-up visit appointment prompting on the corresponding patient according to the adjusted next follow-up visit time,
the patient treatment stage matching module pushes other patients who are treated again in the same day to any patient according to the next treatment time, and the patients can communicate with each other through the patient treatment experience communication module.
7. The 5 g-based medical image transmission system according to claim 1, wherein: the expert information integration module acquires the working age, the field of excellence, the department of any post, the operation times, the operation success rate and the doctor information of the belonging hospital for comprehensive analysis,
the expert information integration module is prestored with comprehensive strength evaluation values of all hospitals, further obtains the working years, the areas of excellence, the departments of any employment, the operation times N and the operation success rate P of doctors of all hospitals, can calculate the average operation times N1 of each doctor every year according to the operation times N and the working years of the doctors, obtains the information of any doctor when the average operation times N1 of any doctor is more than or equal to the average operation times threshold value,
further comparing the number of operations N with a preset value, and when the number of operations N is larger than or equal to the preset value, acquiring doctor information of which the operation success rate P is larger than or equal to a first success rate threshold value; and when the operation times N are smaller than a preset value, acquiring the doctor information of which the operation success rate P is larger than or equal to a second power threshold value.
8. The 5 g-based medical image transmission system according to claim 1 or 7, wherein: expert information integration module transmits doctor information to doctor and patient information matching module, doctor and patient information matching module still connects the stage analysis module of diagnosing again, doctor and patient information matching module further acquires the patient information after stage analysis module of diagnosing again carries out analysis and statistics, in the transmission process of medical image, doctor and patient information matching module will acquire doctor information and patient information according to the doctor and the patient information phase-match that excel in the field and inspection position, application department and arbitrary position are unanimous, and doctor and patient can communicate through doctor application module and patient application module.
9. A medical image transmission method based on 5g is characterized in that: the medical image transmission method comprises the following steps:
s1: the medical image classification module is connected with the medical image transmission module, the medical image transmission module is connected with the medical image storage module, the medical image classification module classifies the acquired medical images according to information of an execution department, an examination part, an application department and a patient, classifies the medical images belonging to the same patient according to the information of the patient, further classifies the medical images belonging to the same patient according to the execution department, the examination part and the application department, the medical images of the same patient are classified into one class again when the execution department, the examination part and the application department are consistent in the medical image classification of the same patient, the medical image transmission module transmits the classified medical images to the medical image storage module, and the medical image storage module stores the acquired medical images in a classified mode according to the classification result of the medical image classification module;
s2: the lesion position confirming module is connected with the medical image storage module and further obtains lesion information in the medical image, the lesion information comprises specific position information and specific range information, the lesion position comparing module obtains the specific position information and the specific range information of any two medical images belonging to the same classification so as to confirm whether the lesion positions and the lesion degrees of any two medical images are consistent or not,
determining geometric center points A and B of lesion ranges of any two medical images, further connecting the geometric center points A and B, obtaining a distance L between the A and the B, and when the L is smaller than or equal to a distance threshold value L0, enabling lesion positions of any two medical images to be consistent;
further acquiring lesion ranges Sa and Sb of any two medical images, and when the value of (Sa-Sb)/S0 is less than or equal to a preset value, namely the lesion degrees of any two medical images are consistent, wherein S0 is a range difference threshold value;
s3: the method comprises the following steps that a re-diagnosis stage analysis module acquires the next re-diagnosis time of a patient and sends a re-diagnosis message prompt to a corresponding patient application module and a corresponding doctor application module at the next re-diagnosis time, if the current patient does not need re-diagnosis, the patient application module displays the current re-diagnosis reservation, the re-diagnosis stage analysis module also carries out statistics on the information of the patient going to the same application department for re-diagnosis on the same day, and a patient seeing-doctor stage matching module acquires the statistical result of the re-diagnosis stage analysis module;
the review stage analysis module is connected with the medical image storage module, acquires medical images of different patients, further acquires lesion positions and lesion ranges in the medical images of the same examination part, an execution department and an application department, acquires patient information corresponding to the medical images when the lesion positions and the lesion ranges are consistent by comparing the lesion positions and the lesion ranges, further acquires the review times and next review time of different patients according to the patient information, and compares the review times and next review time of different patients;
s4: the method comprises the steps that a re-diagnosis stage analysis module obtains the number of re-diagnoses of different patients and the time of the next re-diagnosis, if the number of re-diagnoses of any two patients is the same, the number of days between the next re-diagnosis is n, and n is smaller than or equal to an interval number threshold, the time of the next re-diagnosis of any two patients is adjusted, if the time of the next re-diagnosis of one patient is T0, the time of the next re-diagnosis of the other patient is T1, if T0 is prior to T1, the time of the next re-diagnosis of one patient is delayed, and the time of the next re-diagnosis of the other patient is advanced, wherein the delay time and the advance time are determined according to the interval number n between T0 and T1, if n is singular and is not equal to 0, the adjusted time of the next re-diagnosis is respectively T0+ (n +1)/2 and T1- (n +1)/2, and if n is even, the adjusted time of the next re-diagnosis is T0 (n + 1)/n +2) and T1 + (n +2) 2) 2;
if the number of the follow-up visits of any two patients is the same, the interval days between the next follow-up visits is n, and n is greater than the interval days threshold, the next follow-up visits of any two patients do not need to be adjusted;
s5: the re-diagnosis stage analysis module is connected with the patient re-diagnosis stage matching module, the patient re-diagnosis stage matching module acquires the analysis result and the statistical result of the re-diagnosis stage analysis module, the statistical result is the first patient information going to the same application department for re-diagnosis on the same day,
the analysis result is the second patient information with the same number of times of the follow-up visits and the interval days between the next follow-up visits not exceeding the interval days threshold, the patient visit stage matching module obtains the next follow-up visit time corresponding to the first patient information and the adjusted next follow-up visit time corresponding to the second patient information, the follow-up visit stage analysis module also carries out follow-up visit appointment prompting on the corresponding patient according to the adjusted next follow-up visit time,
the patient treatment stage matching module pushes other patients who are subjected to the repeated treatment in the same day to any patient according to the next repeated treatment time, and the patients can communicate with each other through the patient treatment experience communication module;
s6: the expert information integration module acquires the working age, the adequacy field, the department of any post, the operation times, the operation success rate of the doctor and the comprehensive analysis of the doctor information in the belonging hospital,
the expert information integration module is prestored with comprehensive strength evaluation values of all hospitals, further obtains the working years, the areas of excellence, the departments of any employment, the operation times N and the operation success rate P of doctors of all hospitals, can calculate the average operation times N1 of each doctor every year according to the operation times N and the working years of the doctors, obtains the information of any doctor when the average operation times N1 of any doctor is more than or equal to the average operation times threshold value,
further comparing the number of operations N with a preset value, and when the number of operations N is larger than or equal to the preset value, acquiring doctor information of which the operation success rate P is larger than or equal to a first success rate threshold value; when the operation times N are smaller than a preset value, acquiring doctor information of which the operation success rate P is larger than or equal to a second power threshold;
s7: expert information integration module transmits doctor information to doctor and patient information matching module, doctor and patient information matching module still connects the stage analysis module of diagnosing again, doctor and patient information matching module further acquires the patient information after stage analysis module of diagnosing again carries out analysis and statistics, in the transmission process of medical image, doctor and patient information matching module will acquire doctor information and patient information according to the doctor and the patient information phase-match that excel in the field and inspection position, application department and arbitrary position are unanimous, and doctor and patient can communicate through doctor application module and patient application module.
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| CN115511229A (en) * | 2022-11-18 | 2022-12-23 | 曹县人民医院 | Patient hospital arrival time prediction system for hospital management based on disease analysis |
| CN116741350A (en) * | 2023-08-16 | 2023-09-12 | 枣庄市山亭区妇幼保健院 | File management system for hospital X-ray images |
| CN116741350B (en) * | 2023-08-16 | 2023-10-31 | 枣庄市山亭区妇幼保健院 | File management system for hospital X-ray images |
| CN116862906A (en) * | 2023-08-24 | 2023-10-10 | 武汉大学人民医院（湖北省人民医院） | Eye detection device and method |
| CN116862906B (en) * | 2023-08-24 | 2023-12-12 | 武汉大学人民医院（湖北省人民医院） | Eye detection device and method |
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