Malignant tumor radiotherapy clinical scene simulation teaching system based on deep learning

Abstract

The invention relates to the technical field of medical education, and discloses a malignant tumor radiotherapy clinical scene simulation teaching system based on deep learning, and the system comprises a case generation module which is used for generating diversified malignant tumor virtual cases covering different cancer species, stages and patient characteristics, and providing medical history, examination data and imaging data; the diagnosis decision-making module is used for simulating inquiry, physical examination and auxiliary examination in the diagnosis and treatment process and generating diagnosis stages and clinical decision-making bases of the patients; and the treatment scheme design module is used for formulating a comprehensive treatment scheme according to the diagnosis stages and the illness states of the patients and providing rationality analysis of the treatment scheme. Through a virtual case simulation technology, full diagnosis and treatment processes of different cancer species and stages are completely covered, and the technical effects of comprehensive teaching and skill enhancement are achieved, so that the defect that medical students are difficult to contact comprehensive diagnosis and treatment cases is overcome on the premise of not increasing psychological and economic burdens of patients, and the diagnosis and treatment efficiency is improved. Therefore, the system has clinical thinking and humanistic care capabilities earlier.

Description

Technical Field

[0001] The present invention relates to the technical field of medical education, and specifically to a clinical scenario simulation teaching system for radiotherapy of malignant tumors based on deep learning. Background Art

[0002] With the development of medical technology and artificial intelligence technology, virtual case simulation has gradually become an important tool for medical education, especially in the teaching of the diagnosis and treatment of malignant tumors. In the prior art, virtual case systems usually adopt medical data generation technology combined with standardized teaching content to simulate some links of clinical diagnosis and treatment, such as imaging data analysis, diagnostic decision-making, and basic treatment plan planning. The application of these technologies has alleviated to a certain extent the problem of insufficient case resources for medical students in clinical practice, and at the same time provided a simulation environment for some complex diagnosis and treatment processes. For example, virtual case generation methods based on deep learning technologies such as generative adversarial networks (GANs) can generate high-quality medical image data; the interrogation system based on natural language processing (NLP) can also help students simulate some diagnostic interaction scenarios. These technologies provide a relatively efficient and flexible solution for medical education and promote the intelligent development of medical teaching.

[0003] However, with the continuous improvement of clinical teaching requirements, the existing virtual case simulation systems still face many problems and limitations. On the one hand, the types and complexities of cases generated by the existing systems are limited, and it is difficult to fully cover the needs of different cancer types, stages, and treatment paths in malignant tumors. It is difficult for students to deeply learn the whole-process diagnosis and treatment ideas of malignant tumors through a single system, especially the cultivation of clinical thinking ability in links such as interrogation, auxiliary examination, treatment planning, and post-treatment evaluation is still insufficient. On the other hand, most systems only focus on the simulation at the technical level and ignore the teaching value of doctor-patient interaction and humanistic care. At the same time, the existing technologies lack real-time feedback and adaptive teaching mechanisms, and it is difficult for students to discover their own problems in time and obtain targeted guidance during operation. In addition, due to the fragmented teaching process of some systems, the whole process of diagnosis and treatment cannot be effectively simulated, resulting in students being difficult to form a systematic knowledge and skill system. Therefore, there is an urgent need for an intelligent teaching system that can cover the whole diagnosis and treatment process, has both real-time feedback and humanistic care functions, so as to better meet the needs of medical education and make up for the deficiencies of the existing technologies. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a clinical scenario simulation teaching system for radiotherapy of malignant tumors based on deep learning, which solves the problems of the prior art that it is impossible to fully cover the whole-process diagnosis and treatment of different cancer types and stages of malignant tumors, lack of real-time feedback and adaptive teaching mechanisms, and neglect of doctor-patient communication and humanistic care.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A clinical scenario simulation teaching system for radiotherapy of malignant tumors based on deep learning, comprising: A case generation module, configured to generate diverse virtual cases of malignant tumors, covering different cancer types, stages, and patient characteristics, and providing medical history, examination data, and imaging data; A diagnosis and decision-making module, configured to simulate the interrogation, physical examination, and auxiliary examinations during the diagnosis and treatment process, and generate the diagnosis stage and clinical decision-making basis for the patient; A treatment plan design module, configured to formulate a comprehensive treatment plan according to the patient's diagnosis stage and condition, and provide a rationality analysis of the treatment plan; A ray dose optimization module, configured to optimize the ray dose distribution according to the patient's treatment plan, and generate an optimal dose distribution that meets the requirements of tumor dose coverage and critical organ protection; A post-treatment evaluation and follow-up module, configured to simulate the disease changes and efficacy evaluation after the patient's treatment, and optimize the strategy for long-term follow-up management of the patient; A humanistic care and feedback module, configured to simulate the patient's emotional state and evaluate the student's doctor-patient communication ability and humanistic care awareness; A teaching interaction module, configured to guide students to improve their deficiencies through interactive teaching.

[0006] Preferably, the case generation module includes: A text generation unit, configured to generate the patient background information of the virtual case based on deep learning technology, including age, gender, occupation, past medical history, and family history; An image generation unit, configured to generate the three-dimensional medical imaging data of the patient, including CT, MRI, and pathological section images; A dynamic case configuration unit, configured to dynamically adjust the complexity and characteristics of the case according to teaching needs, such as generating cases with different cancer stages, complications, or special pathological manifestations.

[0007] Preferably, the diagnosis and decision-making module includes: An interrogation unit, configured to simulate the interrogation interaction scenario between the patient and the student, and the student obtains the patient's chief complaint and medical history by asking questions in natural language; A physical examination unit, configured to provide an interactive 3D virtual patient model, and generate corresponding examination results according to the student's examination actions; An auxiliary examination analysis unit, configured to perform tumor segmentation, lesion annotation, and staging judgment on the patient's medical imaging data in combination with a deep learning model.

[0008] Preferably, the treatment plan design module includes: A treatment plan recommendation unit for generating comprehensive treatment suggestions according to the patient's tumor type, stage, and physical condition, including surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy; A student plan evaluation unit for analyzing the treatment plan formulated by students, outputting a rationality evaluation report, and providing optimization suggestions.

[0009] Preferably, the ray dose optimization module includes: A dose initialization unit for calculating the initial value of the ray dose distribution based on the Poisson scattering model, which describes the absorption and scattering laws of the ray dose; A dose optimization unit for adjusting the ray dose distribution through variational mathematical optimization methods, and the optimization objectives include tumor dose coverage, critical organ protection, and dose smoothness; A dose feedback unit for visually displaying the optimized dose distribution to students through 3D visualization and providing evaluation results of the dose coverage rate and organ overdose rate.

[0010] Preferably, the optimization objectives of the dose optimization unit are represented by the following mathematical forms: The first objective is tumor region dose coverage, requiring the actual dose distribution to be as close as possible to the target dose; The second objective is critical organ protection, requiring the cumulative dose in the critical organ region to be as small as possible; The third objective is dose smoothness, restricting the change of the dose gradient to avoid dose concentration or drastic fluctuations.

[0011] Preferably, the variational mathematical optimization is defined by the following objective function:

[0012] Where: represents the current dose distribution of the optimization degree; is defined at the spatial position the ray dose value at; is defined at the spatial position the ray dose value at; is the volume of the tumor region; is the volume of the critical organ region; is the tumor region dose deviation; is the critical organ region dose cumulative value; is the gradient smoothness of the ray dose distribution; is the key organ protection weight parameter; is the dose distribution smoothness weight parameter.

[0013] Preferably, the post-treatment evaluation and follow-up module includes: The efficacy evaluation unit is used to simulate the dynamic changes of patients' conditions after treatment and generate efficacy reports based on the solid tumor efficacy evaluation criteria; The follow-up management unit is used to simulate the long-term follow-up process of patients, including recurrence monitoring, post-treatment complication management and quality of life assessment.

[0014] Preferably, the humanistic care and feedback module includes: The emotional modeling unit is used to simulate the patient's emotional state during the diagnosis and treatment process, including psychological changes such as anxiety, depression, hope and helplessness; The communication assessment unit is used to analyze students' communication language, tone and emotional coping ability during consultation and follow-up, and provide optimization suggestions.

[0015] Preferably, the teaching interaction module is implemented by the following steps: Quantitative evaluation of student performance in diagnosis, treatment planning, and dose optimization; Generate personalized learning reports, including students' strengths, weaknesses, and suggestions for improvement; Dynamically adjust the difficulty of subsequent simulation scenarios to suit students' learning progress.

[0016] The present invention provides a clinical scenario simulation teaching system for malignant tumor radiotherapy based on deep learning. It has the following beneficial effects: 1. The present invention uses virtual case simulation technology to completely cover the entire diagnosis and treatment process of different cancer types and stages, achieving the technical effect of comprehensive teaching and skill enhancement. Compared with the existing technology, which has insufficient teaching due to limited case resources, the present invention solves the problem that medical students are difficult to access comprehensive diagnosis and treatment cases without increasing the psychological and economic burden on patients, so that they can have clinical thinking and humanistic care capabilities earlier.

[0017] 2. This invention generates highly realistic case data by introducing multimodal data integration and deep learning algorithms, and combines optimized diagnosis and treatment plan simulation to achieve the effect of improving students' clinical thinking ability. Compared with the problems of single data generation and limited diagnostic support in the prior art, it effectively solves the shortcomings of single teaching content and lack of realism.

[0018] 3. By designing an adaptive feedback mechanism and real-time interaction functions, the present invention enables students to obtain operation suggestions in a timely manner and adjust their learning strategies, enhancing the interactivity and pertinence of the learning process. Different from the traditional technology where one-way teaching feedback is lagged and cannot be improved in real time, it solves the deficiency that students cannot effectively master skills due to feedback delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the system module of the present invention; Figure 2 It is a schematic diagram of the case generation module of the present invention; Figure 3 It is a schematic diagram of the diagnostic decision-making module of the present invention; Figure 4 It is a schematic diagram of the treatment plan design module of the present invention; Figure 5 It is a schematic diagram of the radiation dose optimization module of the present invention; Figure 6 It is a schematic diagram of the post-treatment evaluation and follow-up module of the present invention; Figure 7 It is a schematic diagram of the humanistic care and feedback module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to the attached Figure 1 , the embodiment of the present invention provides a clinical scenario simulation teaching system for radiotherapy of malignant tumors based on deep learning, including: A case generation module for generating diverse virtual cases of malignant tumors, covering different cancer types, stages, and patient characteristics, and providing medical histories, examination data, and imaging data; A diagnostic decision-making module for simulating the interrogation, physical examination, and auxiliary examinations during the diagnosis and treatment process, and generating the diagnostic stage and clinical decision-making basis of the patient; A treatment plan design module for formulating a comprehensive treatment plan according to the patient's diagnostic stage and condition, and providing a rationality analysis of the treatment plan; A radiation dose optimization module for optimizing the radiation dose distribution according to the patient's treatment plan, and generating an optimal dose distribution that meets the requirements of tumor dose coverage and critical organ protection; The post-treatment evaluation and follow-up module is used to simulate the disease changes and efficacy evaluation after the patient's treatment, and optimize the strategies for the long-term follow-up management of the patient; The humanistic care and feedback module is used to simulate the patient's emotional state and evaluate the students' doctor-patient communication ability and humanistic care awareness; The teaching interaction module is used to guide students to improve their deficiencies through interactive teaching.

[0022] Specifically, the system first creates virtual cases that meet the set conditions by the case generation module, including medical history descriptions, imaging data, and pathological information. These data are directly transmitted to the diagnostic decision-making module. Students conduct diagnostic analysis based on the case information to determine the tumor type, stage, and lesion characteristics. The system records the diagnostic path and operation behaviors in real time and generates corresponding diagnostic conclusions. Subsequently, the diagnostic conclusions are transmitted to the treatment plan design module. The module plans the treatment path according to the tumor type and stage, including surgical, chemotherapy, radiotherapy, and targeted therapy plans, and generates preliminary radiation therapy dose distribution parameters at the same time. Then, these parameters are input into the radiation dose optimization module, and a three-dimensional dose distribution that meets the requirements of target area dose coverage and critical organ protection is generated through an optimization algorithm, and a detailed dose report is output at the same time. The optimization results are transmitted to the post-treatment evaluation and follow-up module. The system simulates the treatment effect, predicts the tumor volume change, recurrence risk, and patient quality of life, and generates a personalized follow-up plan. On this basis, the output data of all modules and the students' operation behaviors are summarized into the humanistic care and feedback module. The module provides a comprehensive score and improvement suggestions by analyzing the students' decision-making path, language expression, and patient emotional response, and dynamically adjusts the difficulty and content of subsequent teaching tasks. According to these contents, the teaching interaction module will start to guide students on how to do their homework correctly, and finally realize the cyclic optimization of case simulation and personalized teaching.

[0023] Please refer to the appendix Figure 2 , the case generation module includes: The text generation unit is used to generate the patient background information of the virtual case based on deep learning technology, including age, gender, occupation, past medical history, and family history; The image generation unit is used to generate the three-dimensional medical imaging data of the patient, including CT, MRI, and pathological section images; The dynamic case configuration unit is used to dynamically adjust the complexity and characteristics of the case according to teaching needs, such as generating cases with different cancer stages, complications, or special pathological manifestations.

[0024] Specifically, the case generation module first generates the basic information of the patient. These information include but are not limited to the patient's gender, age, occupation, living habits, medical history, and family genetic history, etc.

[0025] As an option, a method based on a natural language generation model (such as a GPT-like model) is adopted to generate the patient's medical history information. Specifically, the model inputs include the type and stage labels of malignant tumors, and combined with the training dataset to generate a medical history description. The medical history information includes the chief complaint, current medical history, past medical history, and family history.

[0026] In some embodiments, to enhance the diversity of cases, randomization parameters are used to control the complexity of the medical history. For example: The patient's chief complaint may involve a single symptom (such as "right lung pain") or multiple complications (such as "chest tightness accompanied by a significant weight loss").

[0027] In the past medical history, it is possible to simulate that the patient has other chronic diseases (such as diabetes, hypertension) or a family history of malignant tumors.

[0028] Generally, the content of the medical history needs to be consistent with the subsequent generated imaging data and pathological examination data. For example, if a late-stage lung cancer case is generated, the medical history may include features such as hemoptysis and progressive dyspnea.

[0029] In one possible implementation, the input of the generator network of the GAN is the tumor type, stage, anatomical location, and basic patient information, and the output is the corresponding three-dimensional imaging data. Generally, the discriminator network of the GAN is trained based on a real imaging dataset to ensure that the anatomical and pathological features of the generated images conform to clinical reality.

[0030] Specifically, the generated CT images include the following features: The density distribution of the tumor area, usually represented using Hounsfield units (HU); The distribution of normal tissues and key organs, including lung tissue, blood vessels, and lymph nodes, etc.; The location and size of metastatic tumor lesions.

[0031] MRI data focuses on the characteristics of soft tissue contrast. As an option, T1-weighted images and T2-weighted images can be generated to simulate the boundary features and infiltration of tumors.

[0032] In some embodiments, PET imaging data is generated in combination with tumor metabolic characteristics. Specifically, the generated tumor area shows high metabolic characteristics, forming a significant contrast with normal tissues.

[0033] In one possible implementation, the case generation module provides a dynamic case customization function for generating cases in specific scenarios according to teaching needs. For example, the user can select the case type (such as lung cancer, gastric cancer, or breast cancer), stage (such as stage I, stage II, or advanced metastatic), and whether to add specific complications (such as malignant pleural effusion, local vascular infiltration).

[0034] Specifically, dynamic case customization is achieved through the following steps: After selecting the tumor type and stage, the system automatically retrieves the corresponding training model parameters.

[0035] Use a randomization factor to control the differentiation of imaging features. For example, when generating a CT image of lung cancer, the size, shape, and density distribution of the tumor can all be randomized to simulate different pathological types (such as adenocarcinoma, squamous cell carcinoma).

[0036] Generate comorbidities according to the teaching purpose. For example, the large blood vessels adjacent to the tumor may show infiltration features or hilar lymph node enlargement, indicating local advanced stage.

[0037] In some embodiments, the randomization parameters for dynamic case generation can be saved as a log file to facilitate the repeated generation and adjustment of teaching content.

[0038] The pathological data generation function is implemented through a deep learning model. Generally, pathological data includes slice images of tumor tissue, immunohistochemical staining results, and molecular marker detection results.

[0039] Specifically, the generation of pathological slice images can be based on the style transfer technology of the sample slice dataset. The generated slices show pathological features such as the density distribution of tumor cells, nuclear atypia, and mitotic figures. For example, in an adenocarcinoma case, the pathological slice shows hyperplasia of glandular structures, accompanied by obvious large and deeply stained nuclei.

[0040] The generation of immunohistochemical results can automatically output positive markers according to the tumor type and molecular subtype. For example: In a case of lung adenocarcinoma, PD-L1 expression positive can be generated for subsequent targeted therapy or immunotherapy recommendation.

[0041] In a case of breast cancer, ER / PR positive and HER2 negative can be generated for hormone therapy guidance.

[0042] In the generative adversarial network, the generator network receives the input noise and the conditional variable :

[0043] where: is a random noise vector used to control the generated imaging features; is the conditional variable, including the tumor type, stage, and patient background; is the generated three-dimensional imaging data with a resolution of ; , , : respectively represent , , 's data space.

[0044] Discriminator network is used to distinguish real images from generated images, and its goal is to minimize the following cross-entropy loss:

[0045] where: : real image data distribution; : noise distribution; : conditional distribution; : loss function of the discriminator; : output of the discriminator network; : image generated by the generator network; : log probability that the real image XXX is judged as real; : generated image is judged as false log probability.

[0046] The generator is optimized by minimizing the following loss:

[0047] where: : loss function of the generator, the goal is to minimize the probability that the generated image is classified as false by the discriminator, that is, to maximize the probability that the generated image is judged as real.

[0048] : image generated by the generator network.

[0049] : output of the discriminator network for the generated image indicating the probability that the image is classified as real.

[0050] Please refer to Appendix Figure 3 , the diagnostic decision module includes: An interrogation unit, used to simulate the interrogation interaction scenario between the patient and the student, and the student obtains the patient's chief complaint and medical history by asking questions in natural language; A physical examination unit, which is used to provide an interactive 3D virtual patient model and generate corresponding examination results according to the examination actions of students; An auxiliary examination and analysis unit, which is used to perform tumor segmentation, lesion annotation and staging judgment on the medical image data of patients by combining a deep learning model.

[0051] The diagnosis decision-making module is closely connected with the case generation module. It is responsible for simulating the processes of medical history taking, physical examination and auxiliary examination after generating virtual cases, and providing support for the subsequent treatment plan design and radiotherapy dose optimization. By simulating the real clinical diagnosis process, the diagnosis decision-making module helps students understand the diagnosis logic of different cancer types and stages, and how to complete the diagnosis task by using multi-modal data (such as medical history, imaging and physical examination information).

[0052] Generally, the diagnosis decision-making module includes three parts: medical history taking, physical examination and auxiliary examination, and each part is implemented through a deep learning model and 3D modeling technology. As an option, the diagnostic data is generated in a modular manner and can be combined as needed to meet the requirements of different teaching scenarios.

[0053] Specifically, the implementation of the medical history taking dialogue is based on a deep learning language model. The patient's answers are generated by the model according to the condition, including symptom descriptions (such as chest pain, hemoptysis, etc.) and background information (such as occupational exposure history, family genetic history). In a possible implementation manner, the medical history taking dialogue can also generate emotional simulation responses. For example, when the patient faces a diagnosis of advanced cancer, the model will output emotional expressions such as anxiety or worry.

[0054] In some embodiments, to improve the authenticity of the medical history taking, the medical history taking module can introduce semantic errors or ambiguous expressions. For example, the patient may not describe the symptoms accurately enough, creating certain diagnostic difficulties for students. This simulation is closer to the actual clinical situation.

[0055] In this embodiment, the physical examination realizes the interactive operation of the student's physical examination of the patient through a virtual 3D model. The model of the virtual patient is generated based on the case data, and the external signs and palpation results consistent with the condition are displayed.

[0056] In a possible implementation manner, the student triggers the corresponding examination result by clicking on a specific body part. Generally, the examination result corresponds to the pathological data provided by the case generation module. For example: The palpation result of a lung cancer patient may show enlarged supraclavicular lymph nodes.

[0057] A gastric cancer patient may present an abdominal mass, which is hard and irregular.

[0058] In some embodiments, to simulate complex situations, the physical examination results may include additional information. For example, when a student performs percussion on the abdomen, decreased bowel sounds may be heard, indicating that a tumor is compressing the intestine.

[0059] As an option, the 3D model supports dynamic updates, allowing students to observe the patient from different angles. For example, in the case of breast cancer, students can view the location of the mass and skin manifestations (such as peau d'orange changes) from the front and side.

[0060] In this embodiment, the auxiliary examination automatically analyzes imaging and laboratory test data through a deep learning model to generate a staging report and a diagnosis result.

[0061] Specifically, the image analysis is based on an improved deep convolutional neural network (such as UNet). The input of the model is the three-dimensional image data generated by the case generation module, and the output includes the segmentation result of the tumor region, the identification result of key organs, and the size and location of the lesion region.

[0062] In one possible implementation, the tumor region segmentation is implemented using the following formula:

[0063] Where: is the predicted segmentation region; is the true segmentation label; and are the volumes of the predicted region and the true region, respectively.

[0064] The staging result of the auxiliary examination is generated according to the TNM staging standard. For example: The T stage is determined by the size and depth of invasion of the tumor; The N stage is determined by the lymph node metastasis situation; The M stage is completed by identifying distant metastasis lesions.

[0065] The laboratory test analysis is based on the patient's blood index data, and the risk score is calculated by combining malignant tumor markers. For example, high expression of AFP (alpha-fetoprotein) and CEA (carcinoembryonic antigen) may indicate liver cancer or gastrointestinal tumors.

[0066] The optimization of the segmentation accuracy in image analysis is achieved by combining the cross-entropy loss function with the Dice coefficient. The total loss function is as follows:

[0067] Where: is the cross-entropy loss, which is used to optimize the pixel classification accuracy of the segmentation region and the true label; is the Dice coefficient loss, which is used to measure the overlap degree between the segmented region and the true region; is the weight parameter that controls the balance between the two parts of the loss.

[0068] In the calculation of the TNM staging result, the tumor invasion depth is estimated by the following formula:

[0069] where: is the voxel intensity, representing the distribution of the tumor region; is the volume range where the tumor is located.

[0070] Generally, this module can be further enhanced by combining real patient data for training to improve the generality and accuracy of the model.

[0071] Please refer to Appendix Figure 4 , the treatment plan design module includes: The treatment plan recommendation unit is used to generate comprehensive treatment suggestions according to the patient's tumor type, stage and physical condition, including surgery, radiotherapy, chemotherapy, targeted therapy and immunotherapy; The student plan evaluation unit is used to analyze the treatment plan formulated by the student, output a rationality evaluation report, and provide optimization suggestions.

[0072] The treatment plan design module is used to formulate a reasonable comprehensive treatment plan according to the patient's stage, pathological features and other clinical data after the diagnosis decision module generates the patient's diagnosis result. By simulating the process of multidisciplinary consultation in actual clinical practice, this module provides targeted treatment suggestions for users and supports students in formulating personalized treatment plans. Through this module, students can learn various strategies from surgery, radiotherapy to drug treatment and evaluate the rationality of the treatment plan.

[0073] Generally, the treatment plan design module is linked with the case generation module and the diagnosis decision module, and uses the case data and diagnosis conclusion to provide the input for the comprehensive treatment plan. As an option, this module can dynamically adjust the complexity of the treatment suggestions to meet the needs of different teaching scenarios.

[0074] In this embodiment, the treatment plan design module generates a recommended treatment plan based on the patient's diagnosis data. The recommendation process combines the clinical guidelines and actual cases of malignant tumors and is implemented through a deep learning model and a rule inference system.

[0075] Specifically, the input for treatment plan recommendation includes data such as the patient's diagnostic stage, tumor size and location, tissue type, and molecular marker expression. Through a matching algorithm, the model judges the treatment path of the current case and outputs a reasonable plan. For example: In early-stage lung cancer patients, the system may recommend surgery as the main treatment method and supplement it with postoperative adjuvant chemotherapy.

[0076] In advanced breast cancer patients, targeted therapy and immunotherapy are recommended as the main treatments, combined with radiotherapy for local control of specific lesions.

[0077] In a possible implementation, the recommended plan is completed by combining logical rules and deep learning. The rule part is based on standardized clinical guidelines, such as the NCCN guidelines, to extract the relationship between diagnostic stage and treatment options. The deep learning part is trained with historical case data to optimize the refinement of the plan. For example, for stage II gastric cancer patients, the recommended chemotherapy plan may vary according to molecular subtypes.

[0078] In some embodiments, the recommended treatment plan will be marked with priorities. For example, surgery is the first choice and radiotherapy is an adjuvant treatment method. Students can adjust the specific treatment steps and timing based on this.

[0079] In this embodiment, the treatment plan design module allows students to design personalized treatment plans according to the recommended plan. The system will analyze and provide feedback on the student's plan, pointing out the rationality and deficiencies.

[0080] Specifically, students can modify the following contents of the recommended plan: Setting of radiotherapy dose and target area planning; Drug selection and administration cycle; Surgery timing and method.

[0081] In a possible implementation, the student's treatment plan is compared with the recommended plan through a logical reasoning module to generate a scoring result. The scoring indicators include the integrity, rationality, and innovation of the treatment plan. For example: If a student recommends radical surgery for an advanced patient, the system will prompt that there may be an over-treatment problem.

[0082] If the selected radiotherapy dose does not match the tumor characteristics, the system will recommend adjusting the dose range.

[0083] In some embodiments, the evaluation results of the system include a written report and visual feedback. For example, a heat map is used to show the priorities and coverage of treatment methods in the plan to help students understand the basis for adjustment.

[0084] In this embodiment, the treatment plan design module also supports multi-disciplinary consultation (MDT) simulation. Generally, MDT is jointly completed by multiple departments such as the surgery department, radiotherapy department, and chemotherapy department, comprehensively discussing the treatment path of the patient.

[0085] Specifically, the MDT module generates treatment plans from different perspectives by simulating the suggestions of experts from different departments. For example: The suggestions of surgeons may tend to surgical resection, and the feasibility and risks of the surgery are described in detail.

[0086] Radiation oncologists may pay more attention to the target area dose distribution and organ protection.

[0087] Medical oncologists may put forward optimization suggestions on drug selection and treatment course design.

[0088] As an option, the MDT module allows students to adjust the treatment plan according to the expert suggestions. After adjustment, the system regenerates the evaluation results, pointing out the balance point between the suggestions of different disciplines. For example, in the case of advanced rectal cancer, the MDT module may recommend radiotherapy and chemotherapy first to shrink the lesion, and then perform surgical resection.

[0089] The treatment plan design module supports the dynamic adjustment function. The system can update the rationality analysis of the plan in real time according to the treatment strategy input by the student. For example, when new lesions appear in the patient, the system will re-evaluate the radiotherapy target area range and drug efficacy.

[0090] In some embodiments, the system will record the adjustment steps of the student and generate a learning curve report. For example, it points out the logical progress of the student in plan optimization, helping them gradually master the treatment design of complex cases.

[0091] Generally, this module can also be linked with the post-treatment evaluation module. For example, after the plan is generated, the system will predict the possible treatment response and recurrence risk of the patient and provide support for follow-up planning.

[0092] In some embodiments, the system can combine the molecular marker characteristics of the patient to provide precision medicine suggestions. For example, in patients with HER2-positive breast cancer, the recommended targeted treatment plan will give priority to trastuzumab.

[0093] Please refer to the appendix Figure 5 , the ray dose optimization module includes: A dose initialization unit for calculating the initial value of the ray dose distribution based on the Poisson scattering model, and the Poisson scattering model describes the absorption and scattering laws of the ray dose; A dose optimization unit for adjusting the ray dose distribution by the variational mathematical optimization method, and the optimization objectives include tumor dose coverage, critical organ protection, and dose smoothness; A dose feedback unit, which is used to display the optimized dose distribution to students through 3D visualization and provide the evaluation results of dose coverage rate and organ overdose rate; The optimization objectives of the dose optimization unit are expressed in the following mathematical form: The first objective is the dose coverage of the tumor region, requiring the actual dose distribution to be as close as possible to the target dose; The second objective is the protection of critical organs, requiring the cumulative dose in the critical organ region to be as small as possible; The third objective is dose smoothness, restricting the dose gradient change to avoid dose concentration or severe fluctuation; The variational mathematical optimization is defined by the following objective function:

[0094] Where: represents the current dose distribution of the optimization degree; is defined at the spatial position the ray dose value at; is defined at the spatial position the ray dose value at; is the volume of the tumor region; is the volume of the critical organ region; is the dose deviation in the tumor region; is the cumulative dose value in the critical organ region; is the gradient smoothness of the ray dose distribution; is the weight parameter for critical organ protection; is the weight parameter for dose distribution smoothness.

[0095] The ray dose optimization module is used to precisely optimize the distribution of ray dose after the treatment plan design module generates a preliminary plan. This module takes the high-dose coverage of the tumor region as the core objective, and at the same time reduces the radiation dose of critical organs as much as possible to achieve precise radiotherapy. Through mathematical modeling and deep optimization methods, the module can dynamically balance between multiple optimization objectives to meet the complex needs of clinical radiotherapy.

[0096] Generally, the radiation dose optimization module works closely with the diagnostic decision-making module and the treatment plan design module. The diagnostic results provide the structural information of the tumor region and key organs, while the treatment plan design provides input parameters for dose planning. As an option, the radiation dose optimization module can adjust the optimization target weights according to the stages and pathological characteristics of different patients to support personalized radiotherapy strategies.

[0097] In this embodiment, the radiation dose optimization module first initializes the dose distribution. The initialization process is based on the patient's image segmentation data, and the Poisson scattering equation is used to calculate the initial distribution of the radiation dose in the tissue.

[0098] Specifically, the Poisson scattering equation describes the absorption and attenuation process of radiation in human tissues:

[0099] Where: represents the radiation dose distribution in the tissue; is the absorption coefficient of the tissue, which depends on the density of different tissues; is the intensity distribution of the radiation source; is the gradient operator.

[0100] In some embodiments, the calculation of the tissue absorption coefficient is based on the Hounsfield unit (HU) of the patient's CT image. Generally, the absorption coefficients of soft tissues and bone tissues are relatively high, while that of lung tissue is relatively low. As an option, the module normalizes the input image data through preprocessing to ensure the accuracy of the absorption coefficient calculation.

[0101] In a possible implementation, the distribution of the radiation source intensity is generated according to the size and shape of the target area set in the treatment plan. For example, for cases of localized tumors, the distribution of the radiation source is more concentrated, while for cases of extensive infiltration, the distribution is wider.

[0102] In this embodiment, the optimization objectives of the radiation dose optimization module include three parts: tumor dose coverage, key organ protection, and dose smoothness. The optimization process is achieved by defining the objective function and using mathematical optimization algorithms.

[0103] The optimization objective function is:

[0104] Where: represents the degree of optimization of the current dose distribution ; is the ray dose value defined at the spatial position ; is the ray dose value defined at the spatial position ; is the volume of the tumor region; is the volume of the critical organ region; is the dose deviation of the tumor region; is the dose accumulation value of the critical organ region; is the gradient smoothness of the ray dose distribution; is the critical organ protection weight parameter; is the dose distribution smoothness weight parameter.

[0105] In some embodiments, the weight parameters and of the optimization objective can be dynamically adjusted. For example, when the tumor is adjacent to an important organ, the weight of critical organ protection may increase, while the requirement for dose smoothness may decrease.

[0106] As an option, the module also supports customizing the optimization objectives for different types of tumors. For example, for lung cancer cases, the target region dose may require a gentler gradient change, while for head and neck tumors, the importance of organ protection is higher.

[0107] In this embodiment, the variational optimization method is used to solve the objective function. Generally, variational optimization minimizes the objective function through iterative updates.

[0108] Specifically, the optimization algorithm combines the gradient descent method and the deep reinforcement learning method. The gradient descent method is used for initial optimization and quickly converges to a local optimal solution. The deep reinforcement learning method dynamically adjusts the weights of the objective function by training an intelligent agent to explore a better solution.

[0109] In a possible implementation, the reinforcement learning model adopts the Actor-Critic architecture: The Actor network generates a weight adjustment strategy based on the current dose distribution state; The Critic network evaluates the reward value of the current strategy, and the reward function includes the weighted difference between the tumor dose coverage rate and the critical organ dose overdose rate.

[0110] In this embodiment, the optimization results of the radiation dose optimization module are presented in a 3D visualization manner. The optimized dose distribution is displayed on the patient's three-dimensional image, including the following contents: Dose coverage of the tumor area, with a color map showing the radiation dose in different areas; Cumulative dose to critical organs, with specific markers showing areas of excess dose.

[0111] As an option, the module supports interactive adjustment of results. Students can modify optimization parameters by dragging sliders, such as adjusting the priority of critical organ protection. The modified results are updated in real time, helping students understand the impact of parameter adjustment on optimization results.

[0112] In general, the application scope of the radiation dose optimization module is not limited to a single treatment plan. In some embodiments, the module supports the optimization of multi-stage treatment plans. For example, for patients who need fractionated radiotherapy, the module will recalculate the dose distribution before each treatment to adapt to the dynamic changes in tumor morphology.

[0113] Please see attached Figure 6 , post-treatment evaluation and follow-up modules include: The efficacy evaluation unit is used to simulate the dynamic changes of patients' conditions after treatment and generate efficacy reports based on the solid tumor efficacy evaluation criteria; The follow-up management unit is used to simulate the long-term follow-up process of patients, including recurrence monitoring, post-treatment complication management and quality of life assessment.

[0114] Specifically, the post-treatment evaluation and follow-up module is used to simulate the patient's condition changes, efficacy evaluation, and follow-up management after the radiation dose optimization module generates radiotherapy results. Through a comprehensive analysis of the dynamic changes of tumors after treatment and the health status of patients, it helps students master efficacy evaluation methods, strengthen long-term management skills, and support the optimization of follow-up plans. This module realizes the quantitative analysis of treatment effects and the dynamic management of follow-up data, and is an indispensable part of clinical scenario teaching.

[0115] In general, the post-treatment evaluation and follow-up module combines the input information of the diagnosis decision module and the treatment plan design module to build a post-treatment management model based on the individual characteristics of the patient. As an option, the module supports dynamic adjustment of the follow-up frequency and content based on the feedback of the treatment results to better simulate the long-term management process in actual clinical practice.

[0116] In this embodiment, the efficacy evaluation function generates a detailed efficacy report by simulating the tumor changes and physical recovery process of the patient after treatment. The evaluation content includes changes in tumor size, new lesions, and complications.

[0117] Specifically, the efficacy evaluation generates evaluation indicators based on RECIST (Response Evaluation Criteria In Solid Tumors). The calculation formula of the RECIST standard is as follows:

[0118] Generally, the efficacy evaluation includes the following categories: Complete response (CR): The tumor completely disappears and there are no new lesions; Partial response (PR): The tumor volume shrinks by ≥30% and there are no new lesions; Stable disease (SD): The tumor volume changes between +20% and -30%; Progressive disease (PD): The tumor volume increases by ≥20%, or new lesions appear.

[0119] As an option, the module can also model the complications after treatment. For example, for patients with head and neck tumors, the possible radiation stomatitis can be presented in the form of symptom simulation and diagnostic tips can be provided for students.

[0120] In some embodiments, the efficacy evaluation results also include the analysis of molecular imaging data. For example, the degree of reduction in tumor metabolic activity in the post-treatment PET image is used as an auxiliary evaluation indicator.

[0121] In this embodiment, the follow-up plan management function simulates the process of long-term health monitoring of patients by dynamically adjusting the follow-up frequency and content. The follow-up content includes tumor recurrence monitoring, complication management, quality of life assessment, etc.

[0122] Specifically, the formulation of the follow-up plan is based on the following factors: The patient's initial diagnosis stage and treatment method; The results of the efficacy evaluation; The patient's individual characteristics (such as age, underlying diseases).

[0123] In a possible implementation, the follow-up frequency is dynamically optimized by the reinforcement learning algorithm. The reward function of the reinforcement learning model is defined as follows:

[0124] Where: The recurrence risk is the probability of the patient's recurrence, calculated by the statistical model; The monitoring cost represents the occupation of medical resources by the follow-up frequency and content; 、 are weight parameters used to balance the recurrence risk and cost control.

[0125] Generally, the calculation of recurrence risk is based on the expression levels of molecular markers in patients and the dynamic changes of tumors after treatment. For example, in breast cancer patients, those with HER2 positivity may have a higher recurrence risk and require more frequent follow-up.

[0126] In some embodiments, the follow-up plan also supports the input of multimodal data. For example, by combining imaging data and blood tumor marker test results, a more accurate monitoring strategy is generated.

[0127] In this embodiment, the post-treatment evaluation and follow-up module supports the simulation of the dynamic changes in the patient's condition, so that students can understand the long-term treatment effects and the changes in the natural history of tumors.

[0128] Specifically, the module simulates the following through a time series model: The probability and time node of tumor recurrence; The occurrence and distribution of metastatic lesions; The changes in the patient's quality of life (such as pain score, functional status score).

[0129] As an option, the dynamic change simulation can be combined with the student's follow-up plan input. For example, if a student chooses to ignore some important examination items, the system will show an increased risk of tumor recurrence and prompt the consequences in the simulation.

[0130] In some embodiments, the dynamic change simulation also considers the recovery process of normal tissues after treatment. For example, in lung cancer patients, the degree of pulmonary fibrosis after radiotherapy can gradually decrease, specifically manifested as a decrease in density values in imaging.

[0131] In the efficacy evaluation and follow-up, the modeling formula for recurrence risk is as follows:

[0132] Where: is the probability of recurrence risk; , , ⋯, are the clinical factors affecting recurrence (such as tumor size, treatment method, molecular marker expression); , , ⋯, are the weights of the corresponding factors, obtained by fitting historical data.

[0133] Generally, the calculation of recurrence risk is dynamically adjusted in combination with the individual characteristics of patients. For example, in patients with metastatic tumors, the number and size of metastatic lesions contribute more to the recurrence risk.

[0134] In some embodiments, the module supports long-term follow-up management scenarios. For example, in early-stage cancer patients, the follow-up plan may cover a time span of five to ten years, and the follow-up frequency is gradually reduced to save resources.

[0135] Please refer to the appendix Figure 7 , the humanistic care and feedback module includes: An emotion modeling unit for simulating the emotional state of patients during the diagnosis and treatment process, including psychological changes such as anxiety, depression, hope, and helplessness; A communication evaluation unit for analyzing the communication language, tone, and emotion coping ability of students during the consultation and follow-up process, and providing optimization suggestions.

[0136] Specifically, the purpose of the humanistic care and feedback module is to cultivate the sensitivity to the psychological state of patients and communication skills during the process of students participating in diagnosis and treatment simulation and treatment decision-making. The module helps students understand the complexity of the doctor-patient relationship in clinical practice by simulating the emotional reactions of patients, providing communication evaluation and feedback. In the entire teaching system, this module is closely associated with the post-treatment evaluation and follow-up module and the diagnostic decision-making module to supplement the clinical education of students beyond the technical level.

[0137] Generally, the humanistic care and feedback module takes the patient's condition data and treatment plan as inputs, combines the simulated emotional state with the consultation or follow-up interaction, and generates an evaluation of the student's communication ability. As an option, the module supports the analysis of doctor-patient dialogue content in different scenarios and provides specific improvement suggestions.

[0138] In this embodiment, the humanistic care and feedback module first performs emotion modeling to simulate the emotional state of patients at different stages of diagnosis and treatment. These emotional reactions are dynamically generated based on the severity of the condition, treatment expectations, and the communication content of students.

[0139] Specifically, emotion modeling is achieved through emotion computing technology. The model inputs include the patient's diagnosis data, treatment plan, and relevant social background information, such as: Patients with advanced cancer may show anxiety, helplessness, or little hope; Early-stage patients may more likely show nervousness and questions about treatment.

[0140] In a possible implementation, emotion modeling uses a multi-modal emotion analysis algorithm. The emotional state of the patient is jointly determined by text emotion features (the content of the consultation answer) and tone features (such as the pitch and speech rate of the simulated voice). For example, when a student communicates with a patient for diagnosis, if the diagnosis result is advanced metastatic disease, the system may simulate the patient's sad emotion and reflect it through language or expression.

[0141] In some embodiments, the emotion modeling can also change dynamically. For example, when a student alleviates a patient's doubts by explaining a treatment plan, the patient's emotional state may gradually transition from anxiety to calmness.

[0142] In this embodiment, the humanistic care and feedback module generates a communication assessment report by analyzing the language expressions and emotional responses of students during the consultation and follow-up processes. The assessment content includes the accuracy of language, the appropriateness of emotional expression, and the sensitivity to the patient's psychological state.

[0143] Specifically, the communication assessment is achieved by combining natural language processing techniques and emotion computing models. The system will perform word segmentation and semantic analysis on the student's consultation content and extract the following features: Whether the language content is clear. For example, whether simple and easy-to-understand language is used in the diagnostic explanation; Whether the tone is gentle and can effectively relieve the patient's tension; Whether the patient's questions or emotional changes are accurately responded to. For example, whether a reassuring response is given when the patient expresses concern.

[0144] In a possible implementation, the score of the communication assessment is calculated by the following formula:

[0145] Where: is the communication assessment score; represents the language clarity score; represents the emotional resonance score; represents the response score to the patient's emotions; , , are weight parameters, which are dynamically adjusted according to specific teaching objectives.

[0146] Generally, the system will generate detailed improvement suggestions for the deficiencies in communication. For example, if a student does not respond when the patient expresses concern, the system will prompt that more attention should be paid to emotional comfort.

[0147] In this embodiment, the humanistic care and feedback module supports diverse scenario simulations, including initial diagnosis, treatment plan discussion, and post-treatment follow-up. The communication needs and the patient's psychological state vary in each scenario.

[0148] As an option, during the treatment plan discussion, the module will simulate the patient's concerns about treatment side effects. For example, for a chemotherapy plan, the patient may ask about the specific impacts of hair loss and nausea, and the student needs to provide a detailed and reassuring explanation.

[0149] In the follow-up scenario, the module pays more attention to the patient's quality of life and mental health. For example, the system may simulate a patient expressing fear of recurrence, and students need to communicate to reduce the patient's anxiety.

[0150] In some embodiments, the module supports generating personalized scenarios based on the patient's specific background. For example, among middle-aged and young patients, work and family responsibilities may be more concerned, while elderly patients may be more concerned about the side effects of treatment and quality of life.

[0151] In this embodiment, the feedback mechanism of the humanistic care and feedback module includes two parts: a scoring report and detailed suggestions. The scoring report intuitively presents the communication performance of students in a quantitative form, while the improvement suggestions provide specific optimization directions in text form.

[0152] Generally, the scoring report includes the following content: Total communication ability score; Sub-scores for each ability, for example, clarity score, empathy ability score; Gap analysis with standard excellent communication.

[0153] As an option, the system's improvement suggestions may include specific tips for optimizing communication language. For example, when a patient says "Can I still live a normal life?", the system may suggest that the student reply "After treatment, you can resume a normal life, and you may need to appropriately adjust your daily activities."

[0154] In some embodiments, the feedback mechanism also supports a real-time correction function. For example, during a student's interrogation, the system will remind of tone or content problems in the form of a pop-up prompt. For example, when a student uses overly technical terms, the system will prompt to simplify the expression.

[0155] In the humanistic care and feedback module, the formula for calculating the emotional intensity of emotional modeling is as follows:

[0156] Where: is the emotional intensity; is the emotional feature extraction function, including text semantic features and voice features; is the weight of the emotional feature.

[0157] Generally, the weights of different emotional features are dynamically adjusted according to the scenario. For example, in the diagnosis scenario, the weight of text features is higher, while in the follow-up scenario, the weight of voice features may be higher.

[0158] The teaching interaction module is implemented through the following steps: Quantitatively evaluate the operations of students in aspects such as diagnosis, treatment plan, and dosage optimization; Generate personalized learning reports, including students' strengths, error points, and improvement suggestions; Dynamically adjust the difficulty of subsequent simulation scenarios to adapt to the learning progress of students.

[0159] Specifically, the teaching interaction module analyzes the operation behaviors and decision-making processes of students during the entire case simulation process, provides multi-dimensional feedback, and guides students to improve deficiencies through interactive teaching. The module is closely connected after the diagnostic decision-making module, treatment plan design module, and humanistic care and feedback module to form a complete teaching closed-loop, ensuring that students can continuously improve their clinical skills and response abilities in practice.

[0160] Generally, this module uses a combination of deep learning and rule engines to achieve real-time evaluation and dynamic guidance of students' behaviors. As an option, the module supports an adaptive teaching mode, adjusting teaching content and feedback intensity according to students' learning progress and operation levels, so as to more effectively meet personalized teaching needs.

[0161] In this embodiment, the teaching interaction module first records the operation data of students during the diagnosis and treatment process through an embedded behavior monitoring module. The collected data includes but is not limited to diagnostic steps, treatment plan adjustments, content of doctor-patient communication language, and details of follow-up plans.

[0162] Specifically, the collection of behavior data is based on an event-triggered mechanism. For example: When a student selects a certain imaging examination, the system records the time point of the operation, the selected content, and the associated case characteristics.

[0163] During the treatment plan adjustment stage, the system records the modifications made by the student to dose distribution, drug selection, and treatment timing.

[0164] In a possible implementation, the behavior data is transformed into a high-dimensional behavior vector through a feature extraction algorithm. Each dimension of this vector represents the weight of a specific behavior, such as the scientificity of the selected examination, the rationality of the treatment plan, etc.

[0165] In some embodiments, the module also analyzes the language content of students in doctor-patient communication through natural language processing (NLP) technology, extracting keywords and emotional features. For example, when a student explains the diagnosis result, the system judges whether their language is clear and empathetic.

[0166] In this embodiment, the feedback module provides immediate operation feedback to students in the form of quantitative scores and text suggestions. The scoring indicators cover the following dimensions: Scientificity of decision-making; Communication skills; Integrity of the diagnosis and treatment process; Attention to the patient's mental state.

[0167] Specifically, the generation of real-time feedback is based on the combination of multi-layer rule logic and deep learning models. For example: The system generates a scientific score based on the matching degree between the diagnostic examination content selected by the student and the case data.

[0168] If the student ignores the key medical history or imaging examination, the system will point out the specific problems through text prompts and suggest reviewing the case information.

[0169] Generally, the intensity of real-time feedback will be adaptively adjusted according to the student's performance level. For example, when the student performs excellently, the system will provide more expansion suggestions; while when the student has obvious deficiencies in certain links, the system will prioritize emphasizing improvement measures.

[0170] In this embodiment, the teaching interaction module also supports the generation of interactive teaching content based on the student's behavior. This function dynamically generates personalized teaching tasks and cases by analyzing the student's learning trajectory and knowledge blind spots.

[0171] Specifically, the generation process of teaching content includes the following steps: Collect the student's behavior data in the current learning stage, including operation preferences, error frequencies, and knowledge coverage; Combined with the built-in case library of the module, screen out the typical cases that can best target the student's knowledge blind spots; Dynamically adjust the case difficulty, for example, adding complex cases with multiple lesions and multiple complications for students with good performance.

[0172] As an option, the interactive teaching task can also enhance the learning effect by simulating actual clinical scenarios. For example: In the diagnosis stage, the system may simulate a patient to provide vague or incomplete medical history information and require the student to supplement key information through interrogation.

[0173] In the treatment stage, the system may introduce sudden complications (such as chemotherapy-related side effects) and require the student to adjust the treatment plan in a timely manner.

[0174] In some embodiments, the generation of teaching content also combines contextual language interaction technology. For example, the system will simulate a patient or family member asking challenging questions, and the student needs to explain in plain language and soothe the emotions.

[0175] In this embodiment, the teaching interaction module provides a detailed learning curve analysis to help students understand their progress trajectory and weak links. The learning curve includes the following dimensions: Technical capabilities (such as diagnostic accuracy, treatment rationality); Communication capabilities (such as clarity of language expression, emotional resonance ability); Comprehensive performance (such as operation fluency, overall score).

[0176] Specifically, the learning curve is calculated based on the following formula:

[0177] Where: is the comprehensive learning ability curve over time; represents the time series of the th capability; is the weight of each capability.

[0178] Generally, the learning curve is presented in the form of a visual chart. For example, a line chart is used to show the changing trend of a student's technical ability score over time. As an extension, the module also generates personalized long-term improvement suggestions based on the learning curve. For example, for a student with relatively low diagnostic scientificity, the system may recommend strengthening the training of the ability to analyze imaging data.

[0179] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A clinical scenario simulation teaching system for malignant tumor radiotherapy based on deep learning, characterized by: include: Case generation module, which is used to generate diverse virtual cases of malignant tumors, covering different cancer types, stages and patient characteristics, and providing medical history, examination data and imaging data; The diagnostic decision module is used to simulate the interview, physical examination and auxiliary examination in the diagnosis and treatment process, and generate the patient's diagnostic staging and clinical decision basis; Treatment plan design module, which is used to formulate a comprehensive treatment plan based on the patient's diagnostic stage and condition, and provide rationality analysis of the treatment plan; Radiation dose optimization module, which is used to optimize radiation dose distribution according to the patient's treatment plan and generate the optimal dose distribution that meets the requirements of tumor dose coverage and key organ protection; Post-treatment evaluation and follow-up module, which is used to simulate the changes in the patient's condition and efficacy evaluation after treatment, and optimize the strategy for long-term follow-up management of patients; Humanistic care and feedback module, which is used to simulate the patient's emotional state and assess students' doctor-patient communication skills and humanistic care awareness; The teaching interaction module is used to guide students to improve their deficiencies through interactive teaching.

2. The deep learning-based clinical scenario simulation teaching system for malignant tumor radiotherapy according to claim 1 is characterized in that: The case generation module comprises: A text generation unit, which is used to generate patient background information of virtual cases based on deep learning technology, including age, gender, occupation, past medical history, and family history; An image generation unit, used to generate three-dimensional medical imaging data of the patient, including CT, MRI and pathological slice images; The dynamic case configuration unit is used to dynamically adjust the complexity and characteristics of cases according to teaching needs, such as generating cases with different cancer stages, comorbidities or special pathological manifestations.

3. The deep learning-based clinical scenario simulation teaching system for malignant tumor radiotherapy according to claim 1 is characterized in that: The diagnosis decision module comprises: The consultation unit is used to simulate the consultation interaction scenario between patients and students. Students use natural language questions to obtain the patient's chief complaint and medical history; Physical examination unit, used to provide an interactive 3D virtual patient model and generate corresponding examination results according to the students' examination actions; The auxiliary examination and analysis unit is used to perform tumor segmentation, lesion annotation and staging judgment on the patient's medical imaging data in combination with a deep learning model.

4. The deep learning-based clinical scenario simulation teaching system for malignant tumor radiotherapy according to claim 1 is characterized in that: The treatment plan design module includes: Treatment plan recommendation unit, which is used to generate comprehensive treatment recommendations based on the patient's tumor type, stage and physical condition, including surgery, radiotherapy, chemotherapy, targeted therapy and immunotherapy; The student program evaluation unit is used to analyze the treatment plans developed by students, output rationality evaluation reports, and provide optimization suggestions.

5. The deep learning-based clinical scenario simulation teaching system for malignant tumor radiotherapy according to claim 1 is characterized in that: The radiation dose optimization module comprises: A dose initialization unit is used to calculate the initial value of the radiation dose distribution based on the Poisson scattering model, which describes the absorption and scattering laws of the radiation dose; Dose optimization unit, used to adjust the radiation dose distribution through variational mathematical optimization method. The optimization objectives include tumor dose coverage, key organ protection and dose smoothness. The dose feedback unit is used to display the optimized dose distribution to students through 3D visualization and provide evaluation results of dose coverage and organ excess rate.

6. The deep learning-based clinical scenario simulation teaching system for malignant tumor radiotherapy according to claim 5 is characterized in that: The optimization target of the dose optimization unit is expressed in the following mathematical form: The first goal is to achieve dose coverage in the tumor area, requiring the actual dose distribution to be as close to the target dose as possible; The second goal is critical organ protection, which requires that the cumulative dose to critical organ areas be as small as possible; The third goal is dose smoothness, which limits the dose gradient to avoid dose concentration or sharp fluctuations.

7. The deep learning-based clinical scenario simulation teaching system for malignant tumor radiotherapy according to claim 5 is characterized in that: The variational mathematical optimization is defined by the following objective function: in: To represent the current dose distribution The degree of optimization; To define the position in space The radiation dose value at To define the position in space The radiation dose value at is the volume of the tumor area; is the volume of the critical organ region; is the dose deviation in the tumor area; is the cumulative dose value for critical organ areas; is the gradient smoothness of the radiation dose distribution; is the key organ protection weight parameter; is the dose distribution smoothness weight parameter.

8. The deep learning-based clinical scenario simulation teaching system for malignant tumor radiotherapy according to claim 1 is characterized in that: The post-treatment evaluation and follow-up module includes: The efficacy evaluation unit is used to simulate the dynamic changes of patients' conditions after treatment and generate efficacy reports based on the solid tumor efficacy evaluation criteria; The follow-up management unit is used to simulate the long-term follow-up process of patients, including recurrence monitoring, post-treatment complication management and quality of life assessment.

9. The deep learning-based clinical scenario simulation teaching system for malignant tumor radiotherapy according to claim 1 is characterized in that: The humanistic care and feedback module includes: The emotional modeling unit is used to simulate the patient's emotional state during the diagnosis and treatment process, including psychological changes such as anxiety, depression, hope and helplessness; The communication assessment unit is used to analyze students' communication language, tone and emotional coping ability during consultation and follow-up, and provide optimization suggestions.

10. The deep learning-based clinical scenario simulation teaching system for malignant tumor radiotherapy according to claim 1 is characterized in that: The teaching interaction module is implemented by the following steps: Quantitative evaluation of student performance in diagnosis, treatment planning, and dose optimization; Generate personalized learning reports, including students' strengths, weaknesses, and suggestions for improvement; Dynamically adjust the difficulty of subsequent simulation scenarios to suit students' learning progress.

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