An ai-based medical imaging teaching assistance method and system

Abstract

The application discloses an AI-based medical imaging teaching auxiliary method and system, which comprises the following steps: collecting learning background information of all students and evaluating the knowledge level of the students; intelligently planning course content according to the evaluation result of the knowledge level of the students; evaluating the mastering degree of the students on the course knowledge points to realize dynamic adjustment of the teaching content; inputting the student data into an AI-based learning condition evaluation model; and optimizing the teaching content and method according to the feedback of the students on the course learning condition; and establishing a teaching resource sharing platform to share teaching resources and information among different teachers, realize the penetration of the teaching content, and understand the real-time learning degree of the students on the course based on the AI-based learning condition evaluation model, so that the teachers can dynamically adjust the teaching content, improve the flexibility and adaptability of the teaching, and improve the learning effect of the students.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical education, and discloses an AI-based medical imaging teaching auxiliary method and system. BACKGROUND

[0002] The current medical imaging course teaching content includes bone, muscle joint system, respiratory system, circulatory system, digestive system, central nervous system, and five organ system imaging, which are respectively taught by teachers of different professional groups, and the teaching task of the same system is jointly undertaken by teachers of sub-professional groups. Different specialist teachers teaching the same course often leads to poor continuity before and after the course, and the teaching content may be repeated or omitted. In addition, the teaching objects of this course have large differences in background, including five-year clinical, forensic medicine, preventive medicine, imaging professionals, eight-year undergraduate-master-doctor integrated clinical medicine professionals, and medical students of different professional backgrounds such as Zhi Ming undergraduate-master-doctor experimental class, whose pre-requisite course arrangement, basic knowledge level, teaching hours and course tasks are different, and the teaching content needs to be dynamically adjusted. SUMMARY

[0003] The present application provides an AI-based medical imaging teaching auxiliary method and system, which evaluates the learning background of the teaching object to develop a suitable teaching plan, intelligently plans the course content, dynamically adjusts the teaching content according to the students' mastery of knowledge points, accurately feeds back the students' learning situation to the teacher by introducing a learning situation evaluation model based on neural network, and shares the information on the teaching resource sharing platform to realize the flexibility and adaptability of the teaching process.

[0004] To achieve the above object, the present application provides the following technical scheme:

[0005] An AI-based medical imaging teaching auxiliary method, the method comprising the following steps:

[0006] S1. Collecting the learning background information of students and evaluating their knowledge level, wherein the learning background information includes the school system, major, pre-requisite course hours and content forgetting situation, and the evaluation result is used for developing a teaching plan;

[0007] S2. Intelligently planning the course content according to the student knowledge level evaluation result to ensure that the teaching content matches the students' knowledge level;

[0008] S3. Evaluating the students' mastery of course knowledge points to realize dynamic adjustment of the teaching content;

[0009] S4. Inputting the student data into an AI-based learning situation evaluation model, and the teacher optimizes the teaching content and method according to the student learning situation feedback from the model;

[0010] S5. Establish a teaching resource sharing platform, different teachers can share teaching resources and information, and realize the penetration of teaching content.

[0011] Preferably, the assessment of the knowledge level of the students in S1 can be through pre-class examination or questionnaire survey, etc., which is not limited here.

[0012] Preferably, the specific way of developing the teaching plan in S1 is as follows:

[0013] The teaching content of medical imaging includes: course A, course B, course C,..., course N;

[0014] The prerequisite courses include basic courses A1, A2,..., A m1 , which are related to course A; there are m1 courses related to course A; the corresponding class hours are T A1 ,T A2 ,...,T Am1 , and the interval time for learning medical imaging courses is t A1 ,t A2 ,...,t Am1 , and the relevance a1, a2,..., a m1 to medical imaging courses;

[0015] The prerequisite courses also include basic courses B1, B2,..., B m2 , which are related to course B; there are m2 courses related to course B; the corresponding class hours are T B1 ,T B2 ,...,T Bm2 , and the interval time for learning medical imaging courses is t B1 ,t B2 ,...,t Bm2 , and the relevance b1, b2,..., b m2 to medical imaging courses;

[0016] The prerequisite courses also include basic courses C1, C2,..., C m3 , which are related to course C; there are m3 courses related to course C; the corresponding class hours are T C1 ,T C2 ,...,T Cm3 , and the interval time for learning medical imaging courses is t C1 ,t C2 ,...,t Cm3 , and the relevance c1, c2,..., c m3 to medical imaging courses; ...

[0018] The prerequisite courses also include basic courses N1, N2,..., N mn , a total of mn courses are related to the N course; the corresponding teaching hours are T N1 , T N2 ,..., T Nmn , and the interval time from learning the medical imaging course is t N1 , t N2 ,..., t Nmn , and the relevance to the medical imaging course is n1, n2,..., n mn ;

[0019] The priority of N courses is calculated according to the following formula:

[0020]

[0021] Where P A is the priority of the A course corresponding to the teaching, P B is the priority of the B course corresponding to the teaching, P C is the priority of the C course corresponding to the teaching,..., P N is the priority of the N course corresponding to the course, and the calculated priority is compared, and the course arrangement is determined according to the size of the priority. In the teaching process, the course with high priority is taught first, and the course with low priority is strengthened to consolidate the foundation.

[0022] Preferably, the evaluation method of the students' mastery of the course knowledge points in S3 is: the teaching content is divided into multiple knowledge points d1, d2,..., d N , and the mastery of the knowledge points is further calculated,

[0023]

[0024] Where, represents the mastery of the i-th knowledge point; Ti represents the teaching hours of the prerequisite course related to the i-th knowledge point; ti represents the interval time from learning the prerequisite course related to the i-th knowledge point to the medical imaging course knowledge point; represents the memory of the i-th related prerequisite course; k i is the number of check-ins when learning the i-th knowledge point; e i is the learning time of the i-th knowledge point; y i is the student's extracurricular learning time of the i-th knowledge point; z i is the difficulty of the i-th knowledge point, which is related to the score rate in the history test.

[0025] Preferably, the establishment of the AI-based learning condition evaluation model in S4 includes the following steps:

[0026] S41. Collect historical student data as a dataset, including students' prerequisite course grades, hours of medical imaging courses taken, time spent on corresponding practical courses, number of questions asked after class, time spent answering questions by teachers after class, student ratings of teachers, course attendance data, and level of knowledge mastery, as well as corresponding historical student exam scores, and normalize them:

[0027]

[0028] Where X is the original data, X min and X max These are the minimum and maximum values ​​of the data, respectively.

[0029] S42. Input the normalized data into the input layer, and divide the data into features X=[x1,x2,x3,x4,x5,x6,x7,x8] and corresponding labels Y=[y];

[0030] Where x1 represents the student's prerequisite course grade, x2 represents the hours of medical imaging course taken, x3 represents the time of the accompanying practical course, x4 represents the number of questions asked after class, x5 represents the time the teacher spends answering questions after class, x6 represents the student's rating of the teacher, x7 represents the course attendance data, x8 represents the degree of mastery of knowledge points, and y represents the student's exam score.

[0031] S43. Using multiple hidden layers to extract features from the input data, the formula is as follows: Where H is the output of the hidden layer, and W and b are the weights and biases, respectively. It is the ReLU function;

[0032] S44. Output the student's learning progress for the course. The output layer maps the features of the hidden layer to the student's rating of their learning progress. The formula is as follows: ,in These are student ratings of their learning performance in the course; W' and b' are the weights and biases of the output layer.

[0033] S45. Use the cross-entropy loss function to optimize the model so that its output value closely approximates the actual learning performance of students in the course.

[0034]

[0035] in It's a real label. It is the student's rating of their learning performance in the course, output by the model;

[0036] S46. Use the Adam optimizer to update the model's weights and biases to minimize the loss function:

[0037]

[0038] Among them W new For the updated weights, W old The weights before the update. It's the learning rate. It is the gradient of the loss function with respect to the weights.

[0039] This invention provides an AI-based medical imaging teaching assistance system, characterized in that it includes:

[0040] Student knowledge level assessment module: Collects learning information from all students and assesses their basic knowledge level. The learning information includes the length of study, major, prerequisite course hours, and the extent of content forgetting. The assessment results are used to develop the teaching plan.

[0041] Intelligent Course Content Planning Module: Based on the assessment results of students' knowledge levels, the module intelligently plans course content to ensure that the teaching content matches the students' knowledge levels.

[0042] Dynamic adjustment module for teaching content: assesses students' mastery of course knowledge points and enables dynamic adjustment of teaching content;

[0043] Learning assessment module: Input student data into an AI-based learning assessment model, and teachers can further optimize teaching content and methods based on the model's feedback on students' learning progress.

[0044] Teaching Resource Sharing Platform: Establish a teaching resource sharing platform where teachers from different majors can share teaching resources and information, thereby achieving seamless integration of teaching content;

[0045] Intelligent Assisted Teaching System: Develop an intelligent assisted teaching system that integrates the above modules to provide teachers and students with one-stop teaching assistance services.

[0046] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0047] This invention formulates teaching plans by comprehensively considering multiple dimensions such as the length of study, major, prerequisite course hours, and content forgetting, so that the teaching plans can more accurately match the students' actual level; and based on a neural network-based learning assessment model, it understands the students' real-time learning progress, dynamically adjusts the teaching content, improves learning effectiveness, and makes the teaching content more flexible. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0049] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0051] It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the invention.

[0052] This invention provides an AI-based teaching aid method for medical imaging, such as... Figure 1 As shown, the method includes the following steps:

[0053] S1. Collect all students' learning background information and assess their basic knowledge level. The learning information includes the length of study, major, prerequisite course hours, and the extent of content forgetting. The assessment results are used to develop the teaching plan.

[0054] Preferably, the assessment of students' knowledge level in S1 can be carried out through pre-class exams or questionnaires, etc., without further restrictions.

[0055] The course content for Medical Imaging includes: Course A, Course B, Course C, ..., Course N;

[0056] The prerequisite courses include foundational courses related to Course A: A1, A2, ..., A m1 There are m1 courses related to course A; their corresponding class hours are T. A1 ,T A2 ,...,T Am1 And the interval between studying medical imaging courses is t A1 ,t A2 ,...,t Am1 And the relevance to the medical imaging course a1, a2, ..., a m1 ;

[0057] The prerequisite courses also include foundational courses related to Course B: B1, B2, ..., B m2 There are m2 courses related to course B; their corresponding class hours are T. B1 ,T B2 ,...,T Bm2 And the interval between studying medical imaging courses is t B1 ,t B2 ,...,t Bm2 And the relevance to the medical imaging course b1, b2, ..., b m2 ;

[0058] The prerequisite courses also include foundational courses related to course C: C1, C2, ..., C m3 There are m3 courses related to course C; their corresponding class hours are T. C1 ,T C2 ,...,T Cm3 And the interval between studying medical imaging courses is t C1 ,t C2 ,...,t Cm3 And the relevance to the medical imaging course c1, c2, ..., c m3 ; ...

[0060] The prerequisite courses also include foundational courses N1, N2, ..., N related to course N. mn There are mn courses related to N courses; their corresponding class hours are T. N1 ,T N2 ,...,T Nmn And the interval between studying medical imaging courses is t N1 ,t N2 ,...,t Nmn And the relevance to the medical imaging course n1, n2, ..., n mn ;

[0061] The priority of N courses is calculated using the following formula:

[0062]

[0063] Where, P A P represents the priority of teaching course A. B P represents the teaching priority corresponding to course B. C For course C, the teaching priority is..., P N Let N be the course priority. The calculated priorities are compared, and the course arrangement is determined according to the priority. In the teaching process, courses with higher priority are taught first, and basic content is reinforced for courses with lower priority.

[0064] Furthermore, to explain in detail how priority is calculated, taking the courses on head and neck, chest, and abdominal imaging as examples, the specific method for developing the teaching plan is as follows:

[0065] The three main areas of medical imaging are head and neck imaging, chest imaging, and abdominal imaging. Therefore, prerequisite courses include foundational courses related to head and neck imaging: A1 = Human Anatomy (General Nervous System, Central Nervous System, Visual Organs, Vestibular Organ), A2 = Regional Anatomy (Head, Neck), ..., AN =Neurology; its corresponding class hours are T A1 ,T A2 ,...,T AN And the interval between studying medical imaging courses is t A1 ,t A2 ,...,t AN And the relevance to the medical imaging course a1, a2, ..., a N ;

[0066] The prerequisite courses also include basic courses related to chest imaging: B1 = Human Anatomy (Respiratory System, Cardiovascular System), B2 = Regional Anatomy (Chest), ..., B N = Surgery (cardiothoracic diseases); the corresponding class hours are T. B1 ,T B2 ,...,T BN And the interval between studying medical imaging courses is t B1 ,t B2 ,...,t BN And the relevance to the medical imaging course b1, b2, ..., b N ;

[0067] The prerequisite courses also include foundational courses related to abdominal imaging: C1 = Human Anatomy (Digestive System, Urinary System, Male and Female Reproductive Systems), C2 = Regional Anatomy (Abdomen), ..., C N =Surgery (Digestive, Urinary, and Reproductive Diseases); the corresponding class hours are T. C1 ,T C2 ,...,T CN And the interval between studying medical imaging courses is t C1 ,t C2 ,...,t CN And the relevance to the medical imaging course c1, c2, ..., c N ;

[0068] The relevance of each course to the medical imaging course is related to the student's historical grades and prerequisite grades in the medical imaging course.

[0069] The priority of the three courses is calculated using the following formula:

[0070]

[0071] Where, P A Prioritizing head and neck imaging, P B Prioritizing chest imaging, P CFor abdominal imaging, the calculated priorities are compared, and the course arrangement is determined according to the priority. In the teaching process, courses with higher priorities are taught first.

[0072] For example, P A >P B >P C The course will be taught in the following order: head and neck imaging, chest imaging, and finally abdominal imaging.

[0073] Prioritizing courses allows for the selection of courses where students have a strong grasp of the fundamental knowledge, thus preventing a decline in learning outcomes due to students forgetting relevant basic concepts.

[0074] Furthermore, this allows teachers to increase the explanation of prerequisite knowledge when teaching courses with lower priority, thereby improving the quality of teaching.

[0075] S2. Based on the results of student knowledge level assessments (combining pre-class exams and questionnaires, etc.), intelligently plan course content to ensure that the teaching content matches the students' knowledge level.

[0076] S3. Assess students' mastery of course knowledge points to enable dynamic adjustment of teaching content.

[0077] The assessment method for students' mastery of course knowledge points is as follows: the teaching content is divided into multiple knowledge points, d1 = imaging of brain tumors, d2 = imaging of traumatic brain injury, ..., d N =Imaging of adrenal adenoma, further calculating the level of mastery of knowledge points.

[0078]

[0079] in, Ti represents the mastery level of the i-th knowledge point; Ti represents the required course hours related to the i-th knowledge point; ti represents the time interval between the required course hours related to the i-th knowledge point and the learning of the medical imaging course knowledge point; This represents the memorization status of the prerequisite course associated with the i-th prerequisite; k i e represents the number of times you check in when learning the i-th knowledge point; i y represents the learning time for the i-th knowledge point; i z represents the extracurricular study time for the student learning the i-th knowledge point; i Let represent the difficulty level of the i-th knowledge point, and let it be represented by the score rate in the history exam.

[0080] S4. Input student data into an AI-based learning assessment model, and teachers can further optimize teaching content and methods based on the model's feedback on students' learning progress.

[0081] Preferably, establishing the AI-based learning assessment model described in S4 includes the following steps:

[0082] S41. Collect historical student data as a dataset, including students' prerequisite course grades, hours of medical imaging courses taken, time spent on corresponding practical courses, number of questions asked after class, time spent answering questions by teachers after class, student ratings of teachers, course attendance data, and level of knowledge mastery, as well as corresponding historical student exam scores, and normalize them:

[0083]

[0084] Where X is the original data, X min and X max These are the minimum and maximum values ​​of the data, respectively.

[0085] S42. Input the normalized data into the input layer, and divide the data into features X=[x1,x2,x3,x4,x5,x6,x7,x8] and corresponding labels Y=[y];

[0086] Where x1 represents the student's prerequisite course grade, x2 represents the hours of medical imaging course taken, x3 represents the time of the accompanying practical course, x4 represents the number of questions asked after class, x5 represents the time the teacher spends answering questions after class, x6 represents the student's rating of the teacher, x7 represents the course attendance data, x8 represents the degree of mastery of knowledge points, and y represents the student's exam score.

[0087] S43. Using multiple hidden layers to extract features from the input data, the formula is as follows: Where H is the output of the hidden layer, and W and b are the weights and biases, respectively. It is the ReLU function;

[0088] S44. Output the student's learning progress for the course. The output layer maps the features of the hidden layer to the student's rating of their learning progress. The formula is as follows: ,in These are student ratings of their learning performance in the course; W' and b' are the weights and biases of the output layer.

[0089] S45. Use the cross-entropy loss function to optimize the model so that its output value closely approximates the actual learning performance of students in the course.

[0090]

[0091] in It's a real label. It is the student's rating of their learning performance in the course, output by the model;

[0092] S46. Use the Adam optimizer to update the model's weights and biases to minimize the loss function:

[0093]

[0094] Among them W new For the updated weights, W old The weights before the update. It's the learning rate. It is the gradient of the loss function with respect to the weights.

[0095] S5. Establish a teaching resource sharing platform so that teachers from different majors can share teaching resources and information, and achieve seamless integration of teaching content.

[0096] This invention also provides an AI-based medical imaging teaching assistance system, such as... Figure 2 As shown, it includes:

[0097] Student knowledge level assessment module: Collects learning information from all students and assesses their basic knowledge level. The learning information includes the length of study, major, prerequisite course hours, and the extent of content forgetting. The assessment results are used to develop the teaching plan.

[0098] Students' knowledge level can be assessed through pre-class exams or questionnaires, and no further restrictions are set here.

[0099] Since medical imaging includes three courses—head and neck imaging, chest imaging, and abdominal imaging—this is just an example, and the course content may be expanded as it is continuously adjusted.

[0100] At this point, prerequisite courses include foundational courses related to head and neck imaging, A1, A2, ..., A N The corresponding class hours are T. A1 ,T A2 ,...,T AN And the interval between studying medical imaging courses is t A1 ,t A2 ,...,t AN And the relevance to the medical imaging course a1, a2, ..., a N ;

[0101] Prerequisite courses also include foundational courses related to chest imaging, B1, B2, ..., B N The corresponding class hours are T. B1 ,T B2,...,T BN And the interval between studying medical imaging courses is t B1 ,t B2 ,...,t BN And the relevance to the medical imaging course b1, b2, ..., b N ;

[0102] The prerequisite courses also include foundational courses related to abdominal imaging, such as C1, C2, ..., C. N The corresponding class hours are T. C1 ,T C2 ,...,T CN And the interval between studying medical imaging courses is t C1 ,t C2 ,...,t CN And the relevance to the medical imaging course c1, c2, ..., c N ;

[0103] The relevance of each course to the medical imaging course is related to the student's historical grades and prerequisite grades in the medical imaging course.

[0104] The priority of the three courses is calculated using the following formula:

[0105]

[0106] Where, P A Prioritizing head and neck imaging, P B Prioritizing chest imaging, P C For abdominal imaging, the calculated priorities are compared, and the course arrangement is determined according to the priority. In the teaching process, courses with higher priorities are taught first.

[0107] For example, P A >P B >P C The course will be taught in the following order: head and neck imaging, chest imaging, and finally abdominal imaging.

[0108] Prioritizing courses allows for the selection of courses where students have a strong grasp of the fundamental knowledge, thus preventing a decline in learning outcomes due to students forgetting relevant basic concepts.

[0109] Furthermore, this allows teachers to increase the explanation of prerequisite knowledge when teaching courses with lower priority, thereby improving the quality of teaching.

[0110] Intelligent course content planning module: Based on the assessment results of students' knowledge level, the module intelligently plans the course content to ensure that the teaching content matches the students' knowledge level.

[0111] The dynamic adjustment module for teaching content assesses students' mastery of course knowledge points and enables dynamic adjustments to the teaching content.

[0112] Students' knowledge level can be assessed through pre-class exams or questionnaires, and no further restrictions are set here.

[0113] The teaching content is divided into several knowledge points d1, d2, ..., d according to head and neck imaging, chest imaging, and abdominal imaging. N Further calculate the degree of mastery of the knowledge points.

[0114]

[0115] in Ti represents the mastery level of the i-th knowledge point; Ti represents the required course hours related to the i-th knowledge point; ti represents the time interval between the required course hours related to the i-th knowledge point and the learning of the medical imaging course knowledge point; This represents the memorization status of the prerequisite course associated with the i-th prerequisite; k i e represents the number of times you check in when learning the i-th knowledge point; i y represents the learning time for the i-th knowledge point; i z represents the extracurricular study time for the student learning the i-th knowledge point; i Let represent the difficulty level of the i-th knowledge point, which is determined by the score rate in the history exam.

[0116] Learning Assessment Module: Establish an AI-based learning assessment model, allowing teachers to further optimize teaching content and methods based on student feedback on course learning.

[0117] Furthermore, the aforementioned AI-based learning assessment model is constructed to evaluate students' learning performance in the course, which includes the following steps:

[0118] S41. Collect historical student data as a dataset, including students' prerequisite course grades, hours of medical imaging courses taken, time spent on corresponding practical courses, number of questions asked after class, time spent answering questions by teachers after class, student ratings of teachers, course attendance data, and level of knowledge mastery, as well as corresponding historical student exam scores, and normalize them:

[0119]

[0120] Where X is the original data, X min and X max These are the minimum and maximum values ​​of the data, respectively.

[0121] S42. Input the normalized data into the input layer, and divide the data into features X=[x1,x2,x3,x4,x5,x6,x7,x8] and corresponding labels Y=[y];

[0122] Where x1 represents the student's prerequisite course grade, x2 represents the hours of medical imaging course taken, x3 represents the time of the accompanying practical course, x4 represents the number of questions asked after class, x5 represents the time the teacher spends answering questions after class, x6 represents the student's rating of the teacher, x7 represents the course attendance data, x8 represents the degree of mastery of knowledge points, and y represents the student's exam score.

[0123] S43. Using multiple hidden layers to extract features from the input data, the formula is as follows: Where H is the output of the hidden layer, and W and b are the weights and biases, respectively. It is the ReLU function;

[0124] S44. Output the student's learning progress for the course. The output layer maps the features of the hidden layer to the student's rating of their learning progress. The formula is as follows: ,in These are student ratings of their learning performance in the course; W' and b' are the weights and biases of the output layer.

[0125] S45. Use the cross-entropy loss function to optimize the model so that its predictions closely approximate the actual student learning performance.

[0126]

[0127] in It's a real label. It is the student's predicted rating of their learning performance in the course, as determined by the model;

[0128] S46. Use the Adam optimizer to update the model's weights and biases to minimize the loss function:

[0129]

[0130] Among them W new For the updated weights, W old The weights before the update. It's the learning rate. It is the gradient of the loss function with respect to the weights.

[0131] Through the above model and calculation process, we can effectively assess students' learning progress in the course.

[0132] Teaching Resource Sharing Platform: Establish a teaching resource sharing platform where teachers from different majors can share teaching resources and information, thereby achieving seamless integration of teaching content.

[0133] Intelligent Assisted Teaching System: Develop an intelligent assisted teaching system that integrates the above modules to provide teachers and students with one-stop teaching assistance services.

[0134] This invention comprehensively assesses students' basic knowledge levels by taking into account multiple dimensions such as the length of study, major, prerequisite course hours, and content forgetting, enabling the teaching plan to more accurately match students' actual levels. Furthermore, based on a neural network-based learning assessment model, it understands students' real-time learning progress, dynamically adjusts teaching content, enhances the flexibility and adaptability of teaching, and improves students' learning outcomes.

[0135] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0139] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0140] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0141] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0142] Those skilled in the art can implement the present invention in various variations without departing from its scope and spirit. For example, a feature of one embodiment can be used in another embodiment to obtain yet another embodiment. Any modifications, equivalent substitutions, and improvements made within the scope of the present invention's technical concept should be within the scope of the present invention.

Claims

1. An AI-based teaching aid method for medical imaging, characterized in that, Includes the following steps: S1. Collect all students' learning background information and assess their knowledge level. The learning background information includes the length of study, major, prerequisite course hours, and the extent of content forgetting. The assessment results are used to develop the teaching plan. S2. Based on the assessment results of students' knowledge level, intelligently plan the course content to ensure that the teaching content matches the students' knowledge level; S3. Assess students' mastery of course knowledge points to enable dynamic adjustment of teaching content; S4. Input student data into an AI-based learning assessment model, and teachers optimize teaching content and methods based on the model's feedback on students' learning progress; S5. Establish a teaching resource sharing platform so that teachers from different majors can share teaching resources and information to achieve seamless integration of teaching content; The specific methods for developing a teaching plan in S1 are as follows: The course content for Medical Imaging includes: Course A, Course B, Course C, ..., Course N; The prerequisite courses include foundational courses related to Course A: A1, A2, ..., A m1 There are m1 courses related to course A; their corresponding class hours are T. A1 ,T A2 ,...,T Am1 And the interval between studying medical imaging courses is t A1 ,t A2 ,...,t Am1 And the relevance to the medical imaging course a1, a2, ..., a m1 ; The prerequisite courses also include foundational courses related to Course B: B1, B2, ..., B m2 There are m2 courses related to course B; their corresponding class hours are T. B1 ,T B2 ,...,T Bm2 And the interval between studying medical imaging courses is t B1 ,t B2 ,...,t Bm2 And the relevance to the medical imaging course b1, b2, ..., b m2 ; The prerequisite courses also include foundational courses related to course C: C1, C2, ..., C m3 There are m3 courses related to course C; their corresponding class hours are T. C1 ,T C2 ,...,T Cm3 And the interval between studying medical imaging courses is t C1 ,t C2 ,...,t Cm3 And the relevance to the medical imaging course c1, c2, ..., c m3 ; ... The prerequisite courses also include foundational courses N1, N2, ..., N related to course N. mn There are mn courses related to N courses; their corresponding class hours are T. N1 ,T N2 ,...,T Nmn And the interval between studying medical imaging courses is t N1 ,t N2 ,...,t Nmn And the relevance to the medical imaging course n1, n2, ..., n mn ; The priority of N courses is calculated using the following formula: ， Where, P A P represents the priority of teaching course A. B P represents the teaching priority corresponding to course B. C For course C, the teaching priority is..., P N Given the priority of courses N, the calculated priorities are compared, and the course arrangement is determined according to the priority. In the teaching process, courses with higher priority are taught first, and basic content is reinforced for courses with lower priority.

2. The AI-based teaching aid method for medical imaging according to claim 1, characterized in that, The relevance of each course to the medical imaging course is determined by the degree of mastery of the corresponding content as specified in the syllabus, the historical grades in the medical imaging course, and the grades of prerequisite courses.

3. The AI-based teaching aid method for medical imaging according to claim 1, characterized in that, The assessment method for S3 students' mastery of course knowledge points is as follows: the teaching content is divided into multiple knowledge points d1, d2, ..., d... N And calculate the level of mastery of the knowledge points according to the following formula. ， in, Ti represents the mastery level of the i-th knowledge point; Ti represents the required course hours related to the i-th knowledge point; ti represents the time interval between the required course hours related to the i-th knowledge point and the learning of the medical imaging course knowledge point; This represents the memorization status of the prerequisite course associated with the i-th prerequisite; k i e represents the number of times you check in when learning the i-th knowledge point; i y represents the learning time for the i-th knowledge point; i z represents the extracurricular study time for the student learning the i-th knowledge point; i Let represent the difficulty level of the i-th knowledge point, which is related to the score rate in the history exam.

4. The AI-based teaching aid method for medical imaging according to claim 3, characterized in that, The student data described in S4 includes students' prerequisite course grades, hours of medical imaging courses already taken, time spent on accompanying practical courses, number of questions asked after class, time spent answering questions by teachers after class, student ratings of teachers, course attendance data, and level of mastery of knowledge points.

5. The AI-based medical imaging teaching aid method according to claim 4, characterized in that, Establishing the AI-based learning assessment model described in S4 includes the following steps: S41. Collect historical student data as a dataset. This dataset includes students' prerequisite course grades, hours of medical imaging classes, corresponding practical course time, number of after-class questions, time spent answering questions by the teacher, student ratings of the teacher, class attendance data, and level of knowledge mastery, as well as corresponding historical student exam scores. Normalization processing is then performed on this dataset. ， Where X is the original data, X min and X max These are the minimum and maximum values ​​of the data, respectively. S42. Input the normalized data into the input layer, and divide the data into features X=[x1,x2,x3,x4,x5,x6,x7,x8] and corresponding labels Y=[y]; Where x1 represents the student's prerequisite course grade, x2 represents the hours of medical imaging course taken, x3 represents the time of the accompanying practical course, x4 represents the number of questions asked after class, x5 represents the time the teacher spends answering questions after class, x6 represents the student's rating of the teacher, x7 represents the course attendance data, x8 represents the degree of mastery of knowledge points, and y represents the student's exam score. S43. Using multiple hidden layers to extract features from the input data, the formula is as follows: Where H is the output of the hidden layer, and W and b are the weights and biases, respectively. It is the ReLU function; S44. Output the student's learning progress for the course. The output layer maps the features of the hidden layer to the student's rating of their learning progress. The formula is as follows: ,in These are student ratings of their learning performance in the course; W' and b' are the weights and biases of the output layer. S45. Use the cross-entropy loss function to optimize the model so that its output value closely approximates the actual learning performance of students in the course. ， in It's a real label. It is the student's rating of their learning performance in the course, output by the model; S46. Use the Adam optimizer to update the model's weights and biases to minimize the loss function: ， Among them W new For the updated weights, W old The weights before the update. It's the learning rate. It is the gradient of the loss function with respect to the weights.

6. An AI-based medical imaging teaching aid system comprising the method of any one of claims 1-5, characterized in that, include: Student knowledge level assessment module: Collects learning information from all students and assesses their basic knowledge level. The learning information includes the length of study, major, prerequisite course hours, and the extent of content forgetting. The assessment results are used to develop the teaching plan. Intelligent Course Content Planning Module: Based on the assessment results of students' knowledge levels, the module intelligently plans course content to ensure that the teaching content matches the students' knowledge levels. Dynamic adjustment module for teaching content: assesses students' mastery of course knowledge points and enables dynamic adjustment of teaching content; Learning assessment module: Input student data into an AI-based learning assessment model, and teachers can further optimize teaching content and methods based on the model's feedback on students' learning progress. Teaching Resource Sharing Platform: Establish a teaching resource sharing platform where teachers from different majors can share teaching resources and information to achieve seamless integration of teaching content; Intelligent Assisted Teaching System: Develop an intelligent assisted teaching system that integrates the above modules to provide teachers and students with one-stop teaching assistance services.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the AI-based medical imaging teaching aid method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores computer instructions, which, when executed, perform the steps of any one of the AI-based medical imaging teaching aids methods according to claims 1-5.

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