Optimized human body structure model establishing method

Abstract

The invention discloses an optimized human body structure model establishing method which comprises the key steps of basic information acquisition, medical image data collection, model refined optimization and the like. A 3D modeling technology is combined with multi-modal medical image data to construct a high-precision human body structure model, and personalized customization is carried out according to individual physiological features. The method also relates to functional modules of image and model matching, structure readjustment, data viewing and the like to ensure that the model is highly consistent with a real human anatomical structure. In addition, applications of image analysis and diagnosis, disease probability evaluation, continuous model updating and the like in combination with clinical information are discussed, and powerful support is provided for precise medical treatment. The invention further relates to establishment and optimization of a tissue abnormal structure standard database and a physical specimen processing and recording method, and aims to continuously improve the diagnosis accuracy and model practicability of medical imaging.

Description

TECHNICAL FIELD

[0001] The present patent relates to the technical field of human structure model establishment, and is particularly concerned with the construction method of optimizing human structure model and its application. BACKGROUND

[0002] With the continuous progress of medical technology, accurate understanding and simulation of human structure have become a key requirement in the fields of clinical diagnosis and treatment, medical research, and teaching and training. Traditional human structure models are often based on simplified anatomical structures, which are difficult to meet the requirements of high precision, individualization, and multi-dimensional analysis. Therefore, it is particularly important to develop an optimized human structure model establishment method.

[0003] I. Limitations of existing technology

[0004] Insufficient accuracy:

[0005] Traditional human structure models are often based on standard anatomical atlases or simplified three-dimensional models, and cannot accurately reflect the anatomical differences between individuals.

[0006] The use of medical image data is not sufficient, resulting in a large deviation between the model and the real human structure.

[0007] Lack of individualization:

[0008] Existing models cannot be customized according to the physiological characteristics of specific individuals, and cannot meet the individualized needs in clinical diagnosis and treatment.

[0009] It is difficult to fully consider individual factors such as patient history, signs and living habits, affecting the practicality and accuracy of the model.

[0010] Insufficient multi-dimensional data integration capability:

[0011] Medical image data is diverse, including optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging, and nuclear medicine imaging, but existing models can only process a single or a few types of image data.

[0012] There is a lack of effective data integration and analysis methods, and it is difficult to fully exploit useful information from multi-dimensional image data.

[0013] Lack of dynamic updating and real-time performance:

[0014] Human structure changes over time and with disease progression, but existing models cannot be updated in real time to reflect these changes.

[0015] Clinical decision-making needs to be based on the latest and most comprehensive patient information, but traditional models cannot provide such dynamic support.

[0016] II. Demand driven by clinical and scientific research

[0017] Precision Medicine:

[0018] With the deepening of the concept of precision medicine, clinical diagnosis and treatment increasingly rely on individualized precision models. Optimized human structure models can provide a solid foundation for precision medicine.

[0019] Complex Surgical Planning:

[0020] For complex surgeries such as tumor resection and organ transplantation, high-precision human structure models can help doctors make preoperative plans, reduce surgical risks, and improve surgical success rates.

[0021] Medical Research and Teaching:

[0022] Medical research and teaching need detailed and accurate human structure models as support. Optimized models can provide rich anatomical information and pathological data, promoting the in-depth development of medical research.

[0023] Telemedicine and Intelligent Diagnosis:

[0024] With the rise of telemedicine and artificial intelligence technology, optimized human structure models can serve as the basis for intelligent diagnostic systems, providing timely and accurate diagnostic advice for remote patients.

[0025] III. Support for Technology Development

[0026] Advances in Medical Imaging Technology:

[0027] The rapid development of high-resolution, multi-dimensional medical imaging technology such as CT, MRI, and PET provides a rich source of data for establishing high-precision human structure models.

[0028] 3D Modeling and Image Processing Technology:

[0029] Advanced 3D modeling technology and image processing algorithms can handle complex medical image data, constructing accurate and detailed human structure models.

[0030] Big Data and Artificial Intelligence:

[0031] The application of big data technology and artificial intelligence algorithms makes it possible to process and analyze massive amounts of medical image data, providing strong support for model optimization and personalized customization.

[0032] In summary, in view of the limitations of the prior art and the needs of clinical, scientific and technological development, it is particularly important to develop an optimized human structure model establishment method. This method needs to make full use of multi-dimensional and high-resolution medical image data, combined with advanced 3D modeling and image processing technology, to construct a high-precision, personalized and dynamically updated human structure model, to meet the urgent needs of precision medicine, complex surgical planning, medical research and teaching, and remote medical and intelligent diagnosis. SUMMARY

[0033] This paper introduces an optimized human structure model establishment method, covering key steps such as basic information acquisition, medical image data collection, model refinement optimization, etc. Through 3D modeling technology combined with multi-modal medical image data, a high-precision human structure model is constructed, and personalized customization is made according to individual physiological characteristics. The method also involves image and model matching, structure readjustment and data viewing function modules, to ensure that the model is highly consistent with the real human anatomy.

[0034] In addition, the combination of clinical information for image analysis and diagnosis, disease probability assessment and model continuous updating are also discussed, which provides strong support for precision medicine. This paper also mentions the establishment and optimization of the standard database of abnormal tissue structures, and

[0035] The processing and recording method of physical specimens aims to continuously improve the diagnostic accuracy of medical imaging and the practicality of the model.

[0036] An optimized human structure model establishment method, characterized by comprising the following steps:

[0037] a) Basic information acquisition and preliminary modeling:

[0038] Detailed acquisition and recording of the height, weight, gender, age, race and any other related basic physiological information of the target individual; using advanced 3D modeling technology, combined with the basic physiological information, a human structure model with basic and accurate human characteristics is constructed;

[0039] b) Comprehensive medical image data collection and database establishment:

[0040] Systematically collect various high-quality medical image data of the target individual, including optical imaging (surface image, endoscopic image, pathological image), X-ray imaging (X-ray film, CT), ultrasonic imaging (B-mode ultrasound, color Doppler ultrasound), magnetic resonance imaging (MRI, fMRI) and nuclear medicine imaging (PET, SPECT);

[0041] Ensure the comprehensiveness, high resolution and accuracy of the image data, and establish an individual comprehensive database containing these data; c) Model refinement optimization and personalized customization:

[0042] Based on the individual comprehensive database, the human body structure model is deeply refined and optimized to ensure the high consistency and accuracy of the model with the real human anatomy;

[0043] According to the unique physiological characteristics of the target individual, the personalized model customization is carried out to meet the actual needs of the specific individual;

[0044] Constantly iterating and optimizing the model details, through repeated adjustment, correction and verification, continuously improving the accuracy and practicality of the model to adapt to diverse application scenarios.

[0045] An optimized human body structure model building method, characterized by comprising the following modules:

[0046] I. Basic information acquisition and preliminary modeling module

[0047] Information acquisition sub-module:

[0048] Detailed acquisition and recording of the height, weight, gender, age, race and any other related basic physiological information of the target individual; preliminary modeling sub-module:

[0049] Using advanced 3D modeling technology, combined with the basic physiological information, a human body structure model with basic and accurate human body characteristics is constructed;

[0050] II. Comprehensive medical image data collection and database establishment module

[0051] Image data collection sub-module:

[0052] The system collects various high-quality medical image data of the target individual, including optical imaging (surface image, endoscopic image, pathological image), X-ray imaging (X-ray film, CT), ultrasonic imaging (B-mode ultrasound, color Doppler ultrasound), magnetic resonance imaging (MRI, fMRI) and nuclear medicine imaging (PET, SPECT);

[0053] Database establishment sub-module:

[0054] Ensure the comprehensiveness, high resolution and accuracy of the image data, and establish an individual comprehensive database containing these data; III. Model refinement optimization and personalized customization module

[0055] Refinement optimization sub-module:

[0056] Based on the individual comprehensive database, the human body structure model is deeply refined and optimized to ensure the high consistency and accuracy of the model with the real human anatomy;

[0057] Personalized customization sub-module:

[0058] According to the unique physiological characteristics of the target individual, personalized model customization is carried out to meet the actual needs of the specific individual;

[0059] Iterative optimization sub-module:

[0060] Constantly iterating and optimizing model details through repeated adjustments, corrections, and verifications to continuously improve the accuracy and practicality of the model to adapt to diverse application scenarios.

[0061] Further, the method for establishing an optimized human body structure model comprises the following steps:

[0062] a) Matching of imaging data and model:

[0063] The collected imaging data or images of X-ray imaging, MRI imaging, and ultrasound imaging are precisely matched with the corresponding positions in the human body structure model;

[0064] The specific implementation steps for matching the imaging data and the model can be further refined into the following steps:

[0065] 1. Preparation of imaging data

[0066] Data collection: First, ensure that high-quality medical imaging data such as X-ray imaging, MRI imaging, and ultrasound imaging of the target individual have been systematically collected. These data should be comprehensive, high-resolution, and accurate;

[0067] Data organization: The collected imaging data are classified and organized according to type, shooting time, and shooting site, etc., to facilitate subsequent matching with the model;

[0068] 2. Preparation of human body structure model

[0069] Model loading: Open the human body structure model that has been established, which should be based on the basic physiological information (such as height, weight, gender, etc.) of the target individual and have a certain basic accuracy;

[0070] Model positioning: Determine the specific positions or regions in the model that need to be matched with the imaging data;

[0071] 3. Matching of imaging data and model

[0072] Initial matching: Use medical image processing software or tools to initially match the imaging data with the corresponding positions in the human body structure model;

[0073] Precise matching: Further adjust the subtle differences between the images and the model based on the initial matching to ensure that every detail matches the actual human body structure. Use image registration techniques to optimize the matching results by calculating the similarity or difference between the images and the model;

[0074] Verification and adjustment: Through the visualization tool or software, the matching results are verified. If there are mismatches or large errors found, it is necessary to return to the previous step for adjustment until a satisfactory matching result is achieved.

[0075] 4. Saving and applying the matching results

[0076] Result saving: Save the matched imaging data and human structure model as a whole or layered file for subsequent viewing and analysis.

[0077] b) Model structure readjustment:

[0078] According to the actual position in the human structure model, the specific structure in the model is readjusted, including bones, muscles and organs, to ensure that the anatomical structure of the model conforms to the real situation, and the human structure model is supplemented and corrected to improve the accuracy and integrity of the model;

[0079] 1. Bone structure adjustment: According to the collected X-ray imaging or CT imaging data, the bone shape, density and joint connection relationship in the human structure model are accurately identified and adjusted to ensure the accuracy and integrity of the bone structure;

[0080] 2. Muscle and soft tissue adjustment: Combined with MRI or ultrasound imaging data, the muscle shape, distribution and soft tissue structure in the model are adjusted in detail to reflect the muscle direction and soft tissue characteristics of the real human body;

[0081] 3. Organ position and shape adjustment: According to various medical imaging data, especially CT, MRI and nuclear medicine imaging data, the position, shape and spatial relationship between organs in the model are accurately determined and adjusted to ensure high consistency between the model and the real human anatomical structure;

[0082] 4. Supplement and correction: During the adjustment process, any missing or incorrect structure found is supplemented and corrected, including but not limited to small blood vessels, nerve branches, etc., to improve the accuracy and integrity of the model;

[0083] c) Structure data viewing function:

[0084] Provide the function of selecting to view the generated structure data in the personal structure diagram, so that users can understand and analyze the detailed anatomical structure information of the model at any time.

[0085] An optimized method for establishing a human structure model, particularly related to the modules of imaging data and model matching, model structure readjustment and structure data viewing function, characterized by comprising the following modules:

[0086] I. Imaging data and model matching module

[0087] 1.1 Submodule of Preparation of Imaging Data

[0088] Data Collection Unit: Responsible for the system to collect various high-quality medical imaging data of the target individual, such as X-ray imaging, MRI imaging, ultrasound imaging, etc., to ensure the comprehensiveness, high resolution and accuracy of the data;

[0089] Data Sorting Unit: Classify and sort the collected imaging data by type, shooting time, shooting site, etc., to provide convenience for subsequent model matching;

[0090] 1.2 Submodule of Preparation of Human Structure Model

[0091] Model Loading Unit: Responsible for opening the human structure model based on the basic physiological information of the target individual, ensuring the basic accuracy of the model;

[0092] Model Positioning Unit: Determine the specific location or area in the model that needs to be matched with the imaging data;

[0093] 1.3 Submodule of Matching of Imaging Data and Model

[0094] Preliminary Matching Unit: Use medical image processing software or tools to preliminarily match the imaging data with the corresponding position in the human structure model;

[0095] Accurate Matching Unit: Further adjust the subtle differences between the image and the model based on the preliminary matching, ensuring that every detail matches the actual human structure, and use image registration technology to optimize the matching result;

[0096] Verification and Adjustment Unit: Verify the matching result through visualization tools or software. If there are significant mismatches or errors, return to the previous step for adjustment;

[0097] 1.4 Submodule of Saving and Application of Matching Results

[0098] Result Saving Unit: Save the matched imaging data and human structure model as a whole or layered file for subsequent viewing and analysis;

[0099] II. Model Structure Adjustment Module

[0100] 2.1 Submodule of Adjustment of Skeletal Structure

[0101] Skeletal Morphology Adjustment Unit: Accurately identify and adjust the skeletal morphology in the human structure model based on X-ray imaging or CT imaging data;

[0102] Skeletal Density Adjustment Unit: Adjust the skeletal density according to the imaging data;

[0103] Joint connection relationship adjustment unit: ensures the accuracy of the skeletal joint connection relationship;

[0104] 2.2 Muscle and soft tissue adjustment sub-module

[0105] Muscle morphology adjustment unit: adjusts the muscle morphology in the model in combination with MRI or ultrasound imaging data;

[0106] Muscle distribution adjustment unit: reflects the muscle direction of the real human body;

[0107] Soft tissue structure adjustment unit: finely adjusts the soft tissue structure to conform to the characteristics of the real human body;

[0108] 2.3 Organ position and morphology adjustment sub-module

[0109] Organ position adjustment unit: accurately determines and adjusts the position of each organ in the model according to various medical imaging data;

[0110] Organ morphology adjustment unit: adjusts the organ morphology to ensure consistency with the real human anatomical structure;

[0111] Spatial relationship adjustment unit: adjusts the spatial relationship between organs to ensure the overall coordination of the model;

[0112] 2.4 Supplementary and correction sub-module

[0113] Missing structure supplement unit: supplements the missing structure in the model;

[0114] Error structure correction unit: corrects the error structure in the model, including but not limited to small blood vessels, nerve branches, etc.;

[0115] Three, structure data viewing function module

[0116] 3.1 Data viewing and analysis sub-module

[0117] Structure data selection viewing unit: provides the function of selecting and viewing the generated structure data in the personal structure diagram.

[0118] Detailed anatomical structure information analysis unit: facilitates users to understand and analyze the detailed anatomical structure information of the model at any time, supports clinical decision-making and disease monitoring.

[0119] Further, it is characterized in that, when the model is matched and updated in real time by using the body surface image data in the optical imaging technology, the specific steps are as follows:

[0120] a) Body surface image data collection and preparation:

[0121] General and detailed image shooting:

[0122] Overall image capture: High-resolution cameras or video cameras are used to capture comprehensive videos of the target individual's entire body and specific areas. The goal is to quickly locate key areas such as joints (shoulders, elbows, wrists, hips, knees, ankles), head, face, buttocks, back, perineum, forearms, etc. Ensure high image clarity, no distortion, and rich overall shape and dynamic information to provide a framework for subsequent detailed analysis;

[0123] Detailed image capture: Based on the positioning of the overall image, more detailed video or photo shooting is carried out for the key parts or suspected abnormal areas of the target individual. These detailed images need to cover the areas located by the overall image, and by adjusting the camera equipment to the best focal length, light conditions and angle, ensure that skin texture, pigmentation, scars, lumps, etc. Detailed features are clearly visible, providing accurate data for in-depth analysis;

[0124] Image sorting and classification:

[0125] Sort and organize the overall and detailed image data (including photos and videos) captured according to the shooting time, shooting area and purpose. Use video editing software to edit and label the video, ensure that the overall image can be efficiently positioned in the human body structure diagram, and then dig into the specific details of the detailed image, laying a solid foundation for subsequent model matching and reference;b) Accurate data matching:

[0126] Data matching:

[0127] Quick positioning: First, use the overall image to quickly locate in the human body structure diagram;

[0128] Accurate matching: Then, the collected detailed image (photo or video frame) is accurately matched with the positioning area. Use advanced image processing software or tools to make fine adjustments such as scaling, rotating, translating, etc. to ensure that the detailed image is perfectly aligned with the model in spatial position, achieving seamless transition from macro to micro. For video data, also need to consider the synchronization matching on the time axis;

[0129] Detailed feature attention:

[0130] During the matching process, special attention should be paid to the skin texture, pigmentation, dynamic changes and other subtle features in the detailed image to ensure that these key details are accurately and realistically reflected in the model, providing strong support for fine-tuning and optimization of the model;

[0131] c) Dynamic update structure:

[0132] Structure update:

[0133] Based on the new matching body surface image data (including photos and videos), especially the accurate information provided by the detailed image, the human body structure diagram is regenerated and updated. The information in the general and detailed images is seamlessly integrated into the model, fully and deeply reflecting the latest physiological state, dynamic changes and information of the target individual;

[0134] Abnormal labeling and updating:

[0135] During the updating process, any abnormalities or changes found in the detailed image (such as skin lesions, mass changes, etc.) need to be accurately labeled and updated in the model. For video data, the time point and process of abnormal changes also need to be recorded. This not only ensures the timeliness and accuracy of the model, but also provides more accurate and comprehensive support for clinical decision-making and disease monitoring. At the same time, through the dynamic display of video data, doctors are provided with more intuitive understanding and analysis basis.

[0136] A method for model matching and real-time updating using body surface image data in optical imaging technology, characterized by the following sub-modules:

[0137] I. Body surface image data acquisition and preparation module:

[0138] 1.1 General image shooting sub-module

[0139] Function: Use high-resolution cameras or video cameras to shoot the whole body and specific area videos of the target individual, quickly locate key parts such as joints, head, face, hips, back, perineum, forearm, etc., ensure high image clarity, no distortion, and rich in overall shape and dynamic information;

[0140] 1.2 Detailed image shooting sub-module

[0141] Function: Based on the positioning of general images, fine shooting is carried out for key parts or suspected abnormal areas to ensure that skin texture, pigmentation, scars, lumps, etc. are clearly visible, providing accurate data for in-depth analysis;

[0142] 1.3 Image sorting and classification sub-module

[0143] Function: Sort and classify the general and detailed image data according to shooting time, shooting part and purpose, use video editing software to edit and label the video, ensure efficient and accurate model matching and reference in the future;

[0144] II. Accurate matching data module:

[0145] 2.1 Data matching sub-module

[0146] Function: Utilize the macro image to quickly locate in the human structure diagram, then accurately match the detail image with the located area, adjust the image through image processing software such as scaling, rotating, translating, etc., to ensure the perfect alignment of the image and the model space position, realize seamless connection from macro to micro;

[0147] 2.2 Detail Feature Attention Submodule

[0148] Function: During the matching process, special attention is paid to the skin texture, pigmentation, dynamic changes and other subtle features in the detail image, to ensure that these key details are accurately and realistically reflected in the model;

[0149] Three, dynamic update structure diagram module

[0150] 3.1 Structure diagram update submodule

[0151] Function: Based on the newly matched body surface image data, regenerate and update the human structure diagram, seamlessly integrate the macro and detail image information into the model, and fully reflect the latest physiological state, dynamic changes and information of the target individual;

[0152] 3.2 Abnormal labeling and updating submodule

[0153] Function: During the update process, accurately label and update any abnormalities or changes found in the detail image, record the time point and process of abnormal changes, ensure the timeliness and accuracy of the model, and provide accurate support for clinical decision-making and disease monitoring.

[0154] Further, it is characterized in that it further comprises a method for matching and real-time updating of human structure model using endoscopic images in optical imaging technology, characterized in that it comprises the following steps:

[0155] a) Endoscopic image acquisition and preparation stage:

[0156] Comprehensive and systematic collection: systematically and comprehensively collect the endoscopic images and video data of the target individual, covering different shooting times, parts, diagnostic needs and endoscopic path records, to ensure that the image data has high definition, accuracy and completeness;

[0157] Orderly classification and arrangement: according to the classification standards of shooting time, shooting part, diagnostic need, endoscopic path, etc., the collected endoscopic images and video data are orderly classified and arranged, providing a solid foundation for subsequent efficient matching and fusion;

[0158] b) Endoscopic image recognition and preliminary matching stage:

[0159] Key feature automatic recognition: Utilize advanced image recognition technology to automatically identify key features in endoscopic images, including but not limited to lesser curvature, greater curvature, tracheal bifurcation, tracheal carina, pylorus, and the surrounding environment and direction of the endoscopic path, to improve the accuracy and efficiency of matching;

[0160] Endoscopic path matching and fusion: Specifically for endoscopic path records, by recognizing the moving track and surrounding environment of the endoscope, accurately match and fuse with the human body structure diagram, specifically combined with the human organ structure, digestive tract distribution and running, tracheal bronchial running, and the moving track of the endoscope and the running of the human body structure, to determine its specific position in the human body structure diagram;

[0161] c) Model loading and preliminary matching stage:

[0162] Model loading: Open the human body structure model based on the basic physiological information of the target individual, ensure the basic accuracy of the model, and support subsequent fine adjustment;

[0163] Preliminary matching and panoramic map mapping: Preliminary match the recognized images and video materials with the corresponding positions in the human body structure model, and realize the approximate alignment of spatial positions through image scaling, rotation, translation, etc., and ensure accurate mapping of images to model positions;

[0164] d) Precise matching and verification stage:

[0165] Optimized matching results: Based on the preliminary matching, use image registration technology to further optimize the matching results by calculating the similarity or difference between the endoscopic matching image and the model, to ensure that the details and actual human body structure are completely consistent;

[0166] Panoramic map stitching and mapping: Use image stitching technology to stitch the captured endoscopic images into seamless panoramic maps and accurately map them to the corresponding positions in the human body structure model, achieving a function similar to navigation in real scene viewing;

[0167] Careful verification and adjustment: Through visual tools or software, carefully verify the matching results, and if there are mismatches or large errors, return to the previous step for adjustment until the desired fusion effect is achieved;

[0168] e) Model structure update and optimization stage:

[0169] Fine adjustment and optimization: Based on the newly matched endoscopic panoramic map data, fine-tune and optimize the corresponding areas in the human body structure model, paying special attention to the detailed features displayed in the endoscopic images, such as gastric mucosa texture, lesion morphology, and blood vessel distribution, to ensure that the model's anatomical structure conforms to the actual situation;

[0170] Necessary supplement and correction: make necessary supplement and correction to the model to improve its accuracy and completeness in reflecting the structures seen by the endoscope, including the supplement of small blood vessels, nerve branches, etc.

[0171] f) Dynamic updating and integration with structural data stage:

[0172] Overall or layered preservation: save the updated endoscopic panoramic map and human body structure model as a whole or layered file for subsequent viewing, analysis and quick retrieval;

[0173] Stereoscopic linkage and real-time update: provide an intuitive and easy-to-use structure data viewing function, support users to view the current operation display screen and human body structure model display screen on different display screens at the same time, realize the stereoscopic linkage and real-time update of the structure model; pay special attention to the detailed information of the structures seen in the endoscopic panoramic map, provide strong support for clinical decision-making and disease monitoring. In addition, through the accurate fusion of panoramic map and model, the function similar to real scene navigation is realized, which provides intuitive visual guidance for surgical operation.

[0174] A system for matching and real-time updating of human body structure model using endoscopic images in optical imaging technology, characterized by comprising the following modules to realize the method, the specific modules are as follows:

[0175] a. Endoscopic image acquisition and preparation module

[0176] Comprehensive system collection submodule: responsible for collecting endoscopic images and video materials of the target individual systematically and comprehensively, covering different shooting times, parts, diagnosis needs and endoscopic path records, ensuring that the image data has high definition, accuracy and completeness; orderly classification and arrangement submodule: according to the classification standards of shooting time, shooting part, diagnosis need, endoscopic path, etc., the collected endoscopic images and video materials are orderly classified and arranged, providing a solid foundation for subsequent efficient matching and fusion; b. Endoscopic image recognition and preliminary matching module

[0177] Key feature automatic identification submodule: using advanced image recognition technology, automatically identifying key features in endoscopic images, including but not limited to lesser curvature, greater curvature, tracheal bifurcation, tracheal carina, pylorus and the surrounding environment of endoscopic path, direction, etc., to improve the accuracy and efficiency of matching;

[0178] Endoscopic path matching and fusion submodule: especially for endoscopic path records, through recognizing the moving track and surrounding environment of endoscope, accurately match and fuse with human body structure map, specifically combined with human body organ structure, digestive tract distribution and running, tracheal bronchial running, and endoscopic moving track and human body structure running, to determine its specific position in the human body structure map;

[0179] c. Model Loading and Preliminary Matching Module

[0180] Model Loading Submodule: Responsible for opening the human body structure model constructed based on the basic physiological information of the target individual, ensuring the basic accuracy of the model, and supporting subsequent refinement adjustment;

[0181] Preliminary Matching and Panorama Mapping Submodule: Preliminary matching of recognized images and video materials with corresponding positions in the human body structure model, through image scaling, rotation, translation, etc. operations, to achieve approximate alignment in space position, and ensure accurate mapping of images to model positions;

[0182] d. Precise Matching and Verification Module

[0183] Optimized Matching Results Submodule: Based on preliminary matching, using image registration technology, further optimizing matching results by calculating the similarity or difference between endoscopic matching images and the model, ensuring complete agreement of details with actual human body structure; Panorama Stitching and Mapping Submodule: Using image stitching technology, stitching the captured endoscopic images into seamless panoramas, and accurately mapping them to the corresponding positions in the human body structure model, achieving a function similar to navigation in real scene viewing;

[0184] Careful Verification and Adjustment Submodule: Through visual tools or software, carefully verify the matching results, if there are mismatches or large errors, return to the previous step for adjustment until the desired fusion effect is achieved;

[0185] e. Model Structure Update and Optimization Module

[0186] Fine Adjustment and Optimization Submodule: Based on newly matched endoscopic panorama data, fine-tune and optimize the corresponding areas in the human body structure model, paying special attention to details such as gastric mucosa texture, lesion morphology, and blood vessel distribution in endoscopic images, to ensure that the model's anatomical structure conforms to reality;

[0187] Necessary Supplement and Correction Submodule: Make necessary supplements and corrections to the model to improve its accuracy and completeness in reflecting endoscopic findings, including the addition of small blood vessels and nerve branches;

[0188] f. Dynamic Update and Structure Data Integration Module

[0189] Whole or Layered Saving Submodule: Responsible for saving the updated endoscopic panorama and human body structure model as a whole or layered file for subsequent viewing, analysis, and quick retrieval;

[0190] Stereoscopic linkage and real-time update sub-module: provides an intuitive and easy-to-use structural data viewing function, supports users to view the current operation display screen and human body structure model display screen at the same time on different display screens, realizes stereoscopic linkage and real-time update of the structure model; pay special attention to the detailed information of the structure seen by the endoscopic panoramic view, and provide strong support for clinical decision-making and disease monitoring. In addition, through the accurate fusion of panoramic view and model, the function similar to real scene navigation is realized, which provides intuitive visual guidance for surgical operation.

[0191] Further, characterized in that it further comprises the steps of model matching and real-time updating using pathological images in optical imaging technology, the method comprising the following steps:

[0192] I. Precise collection and detailed labeling of pathological specimens

[0193] Position confirmation and shooting: medical personnel need to use high-resolution cameras or video cameras to shoot the following materials when taking out pathological specimens:

[0194] One (or a video) shows the specific location and appearance of the specimen in the human body, ensuring that the image is clear, distortion-free, and accurately reflects the morphology, color, size, and position of the specimen;

[0195] One (or a video) records the space after the specimen is taken out, also ensuring the clarity and accuracy of the image;

[0196] Detailed labeling: label the specimen, including the collection site, possible diagnostic hints, and specific anatomical sites; and use directional terms and distance markers to further accurately describe the location of the specimen to ensure the accuracy of subsequent identification; II. Fine sampling and consistent labeling of microscopic specimens

[0197] Sampling operation: when performing section sampling, medical personnel need to make clear and consistent labels on the section or section record again, which should strictly correspond to the labels on the gross photograph (or video) to ensure the accuracy of the sampling location; Reconfirmation: after sampling, the labels on the section and the gross photograph (or video) need to be checked again for consistency to ensure accuracy;

[0198] III. Precise positioning of microscopic specimen sampling location in the structure model

[0199] Position identification: use advanced image processing technology to identify the specific location of the specimen photograph (or video frame) and combine the detailed labeling made by medical personnel;

[0200] Model matching: refer to the pre-established high-precision human body structure model, accurately determine the precise position of the microscopic specimen sampling location in the structure model through comparison and registration technology;

[0201] Clear labeling: Use advanced visualization tools such as 3D modeling software to clearly label the sampling location on the human structure model, ensuring the accuracy of subsequent operations;

[0202] IV. Detailed recording and accurate matching of pathological microscopic observation results

[0203] Observation record: Carefully conduct pathological microscopic observation and record the observation results in detail, including cell morphology, tissue structure, abnormal changes, etc.;

[0204] Model matching and verification: Accurately match the pathological microscopic observation results with the human structure model, and through comparison and verification, ensure that the observation results are completely consistent with the sampling location in the model; when matching video materials, the synchronization on the time axis also needs to be considered;

[0205] Consistency confirmation: During the matching process, the consistency between the observation results and the model needs to be repeatedly checked and verified, especially the dynamic changes in the video materials, to ensure accurate basis for subsequent clinical decision-making and disease monitoring.

[0206] The method for identifying the location mainly ensures the accurate location identification of the pathological specimen in the human structure model through the following steps:

[0207] Image data acquisition:

[0208] Location confirmation and shooting: Medical personnel use high-resolution cameras or video cameras to take a picture (or a video) when taking out the pathological specimen, showing the specific location and appearance of the specimen in the human body. Ensure that the image is clear, distortion-free, and can accurately reflect the morphology, color, size, and location of the specimen;

[0209] Space condition recording: Take a picture (or a video) to record the space condition after taking out the specimen, ensuring the clarity and accuracy of the image, which serves as a reference for subsequent location identification;

[0210] Detailed labeling:

[0211] Specimen labeling: Make detailed labeling for the specimen, including the collection site of the specimen, possible diagnostic hints, and specific anatomical sites;

[0212] Position description: Use directional terms and distance identifiers to further accurately describe the location of the specimen, ensuring the accuracy of subsequent identification.

[0213] Image processing and location identification:

[0214] Location identification: Use advanced image processing technology to process the specimen photos (or video frames) taken, and identify the specific location of the specimen;

[0215] Combined with detailed labeling by medical staff, further improve the accuracy of position recognition;

[0216] Model matching and labeling:

[0217] Model matching: Referring to the pre-established high-precision human body structure model, through comparison and registration technology, the identified specimen position is matched with the corresponding position in the model;

[0218] Clear labeling: Use advanced visualization tools such as 3D modeling software to clearly label the sampling position in the human body structure model, ensuring the accuracy of subsequent operations.

[0219] An optimized human body structure model establishment method, particularly relates to a model matching and real-time updating module using pathological images in optical imaging technology, characterized by the following sub-modules:

[0220] I. Pathological specimen collection and labeling module

[0221] Position confirmation and shooting sub-module

[0222] Position shooting unit: Responsible for taking photos of the specific position of the specimen in the body and its appearance when the medical staff takes out the pathological specimen, as well as the empty space after the specimen is taken out, ensuring that the photos are clear, distortion-free, and accurately reflect the shape, color, size, and position of the specimen;

[0223] Detailed labeling sub-module

[0224] Specimen labeling unit: Responsible for making comprehensive and accurate labeling for the specimen, including the collection site, possible diagnostic hints, specific anatomical sites (refined to organ names and their sub-regions / sub-sites), and using directional terms and distance markers to further accurately describe the specimen position;

[0225] II. Microscopic specimen sampling and labeling module

[0226] Sampling operation sub-module

[0227] Sampling labeling unit: Responsible for making clear and consistent labeling on the slice or in the slice record by medical staff when sampling, ensuring that the labeling strictly corresponds to the labeling on the gross photo, to ensure the accuracy of the sampling position;

[0228] Reconfirmation sub-module

[0229] Consistency checking unit: Responsible for checking the consistency of the labeling on the slice and the gross photo after sampling, to ensure accuracy;

[0230] Position recognition sub-module

[0231] Image recognition unit: responsible for identifying the specific location of the specimen photo using advanced image processing techniques, combined with detailed markings made by medical personnel;

[0232] Model matching sub-module

[0233] Registration technology unit: responsible for referring to the pre-established high-precision human body structure model, accurately determining the precise position of the microspecimen sampling location in the structure model through comparison and registration technology;

[0234] Clear labeling sub-module

[0235] Visual labeling unit: responsible for using advanced visualization tools such as 3D modeling software to clearly label the sampling location in the human body structure model, ensuring the accuracy of subsequent operations;

[0236] Four, pathological observation and model matching module

[0237] Observation recording sub-module

[0238] Microscopic observation unit: responsible for careful pathological microscopic observation and detailed recording of observation results, including cell morphology, tissue structure, abnormal changes, etc.

[0239] Model matching and verification sub-module

[0240] Matching verification unit: responsible for accurately matching the pathological microscopic observation results with the human body structure model, through comparison and verification to ensure that the observation results are completely consistent with the sampling location in the model;

[0241] Consistency confirmation sub-module

[0242] Repeated checking unit: responsible for repeatedly checking and verifying the consistency between the observation results and the model during the matching process, providing accurate basis for subsequent clinical decision-making and disease monitoring.

[0243] An optimized method for establishing a human body structure model, characterized in that it further comprises the steps of model optimization and updating using nuclear medicine imaging (PET, SPECT), specifically:

[0244] a) Nuclear medicine imaging data collection:

[0245] The system collects PET (positron emission tomography) and SPECT (single photon emission computed tomography) nuclear medicine imaging data of the target individual;

[0246] Ensure high resolution, accuracy and model matching of nuclear medicine imaging data, which reflects tissue metabolism and functional information.

[0247] b) Image data and model matching:

[0248] Accurately match the collected PET, SPECT imaging data with the corresponding positions in the human body structure model;

[0249] Use the metabolic and functional information reflected in the nuclear medicine imaging data to fine-tune and optimize the corresponding regions in the human body structure model.

[0250] c) Model structure adjustment and optimization:

[0251] According to the abnormal metabolism or functional changes of the tissues shown in the PET, SPECT imaging data, adjust the specific structures in the model, including but not limited to the suspected lesion area;

[0252] Supplement and correct the model to improve the accuracy and completeness of the model in reflecting tissue metabolism and function.

[0253] d) Structure data viewing and analysis:

[0254] Provide the function of selecting to view the generated nuclear medicine imaging structure data in the personal structure diagram;

[0255] Support users to understand and analyze the updated model information at any time, especially about the abnormalities or changes in tissue metabolism and function.

[0256] Further, the method for establishing an optimized human body structure model, characterized in that it further comprises the following steps:

[0257] a) Model storage and retrieval:

[0258] Store the established personal human body structure model and its related medical imaging data in the hospital server or the national medical server;

[0259] When the patient is treated, query whether there is a previously established human body structure model of the patient by retrieving the system;

[0260] b) Model retrieval and update:

[0261] If there is a previous model, retrieve the model and update and optimize it in combination with the new medical imaging data of the patient's current visit; if there is no previous model, establish a new human body structure model according to the medical imaging data of the current visit according to the method of claim 1;

[0262] c) Image data improvement and model iteration:

[0263] Continuously collect the patient's subsequent medical imaging data and regularly update the model to ensure that the model is highly consistent with the patient's real human anatomy;

[0264] Through continuous model optimization and image data improvement, the accuracy and practicality of the model are improved, and more accurate support is provided for the diagnosis and treatment of patients.

[0265] An optimized human body structure model establishment method, particularly relates to a model storage and retrieval, model retrieval and update, and image data improvement and model iteration module, characterized by comprising the following modules:

[0266] I. Model storage and retrieval module

[0267] Storage management submodule

[0268] Model and data storage unit: responsible for storing the established personal human body structure model and its related medical image data into the hospital server or the national medical server, ensuring the security and accessibility of the data;

[0269] Retrieval system submodule

[0270] Model retrieval unit: when a patient is treated, an efficient retrieval system is used to query whether there is a previously established human body structure model for the patient, and a fast and accurate retrieval result is provided;

[0271] II. Model retrieval and update module

[0272] Model retrieval submodule

[0273] Previous model retrieval unit: if the patient has a previous model, the model is retrieved to provide a basis for subsequent updating and optimization;

[0274] New model establishment unit: if no previous model is retrieved, a new human body structure model is established according to the medical image data of this visit according to the preset modeling method;

[0275] Model update submodule

[0276] Data fusion unit: responsible for fusing the new medical image data of the patient's current visit with the retrieved previous model or newly established model, ensuring the timeliness and accuracy of the model;

[0277] Model optimization unit: based on the newly fused image data, the model is updated and optimized as necessary to improve the consistency of the model with the real human anatomical structure;

[0278] III. Image data improvement and model iteration module

[0279] Data collection submodule

[0280] Image data continuous collection unit: responsible for continuously collecting subsequent medical image data of the patient, ensuring the integrity and continuity of the data;

[0281] Model iteration submodule

[0282] Periodic updating unit: according to the latest image data collected, the model is updated regularly to ensure that the model always reflects the latest physiological state of the patient;

[0283] Precision and practicality improvement unit: through continuous model optimization and image data improvement, the precision and practicality of the model are continuously improved, providing more accurate support for the diagnosis and treatment of patients.

[0284] Further, a method for identifying and marking abnormal data in a human structure diagram, characterized in that it comprises the following steps:

[0285] a) Abnormal data identification:

[0286] Using advanced image processing and comparison techniques, the individual data model is compared with the group standard model;

[0287] Through detailed analysis, data in the individual model that do not conform to the original organs are identified. These abnormal data may show obvious differences in structure, density, or location (for example, bronchial structure replaced by white shadow, or high-density shadow in fat layer, etc.);

[0288] The specific steps of abnormal data identification are as follows:

[0289] 1. Data preparation and preprocessing:

[0290] Obtain high-resolution, high-quality medical image data of the target individual, including but not limited to X-ray imaging, MRI imaging, ultrasound imaging, CT and nuclear medicine imaging, etc., to ensure the comprehensiveness, accuracy and timeliness of the data;

[0291] Load the individual's human structure model, which should be based on the basic physiological information of the target individual (such as height, weight, gender, etc.) and have a certain basic accuracy;

[0292] Preprocess the medical image data, including denoising, contrast enhancement, standardization, etc., to improve the accuracy and efficiency of subsequent image processing;

[0293] Image comparison and difference analysis:

[0294] Using advanced image processing and comparison techniques, such as image registration, feature extraction and matching, the individual data model is compared with the group standard model;

[0295] Through detailed analysis, data in the individual model that do not conform to the original organs are identified. These abnormal data may show obvious differences in structure, density, texture or location;

[0296] In the specific identification process, a certain threshold or standard can be set. When the difference between the individual model and the standard model exceeds the threshold, it is determined as abnormal data.

[0297] Abnormal data preliminary screening:

[0298] The identified abnormal data is preliminarily screened to exclude false positives caused by image artifacts, noise or individual differences, etc.

[0299] Combining multiple medical image data for comprehensive analysis to improve the accuracy and reliability of abnormal data identification;

[0300] Abnormal data precise positioning and marking:

[0301] For the screened abnormal data, image processing software or tools are used for precise positioning to determine its specific location in the human body structure model;

[0302] In the human body structure model, precise marking of abnormal data can be done using different colors, symbols or labeling methods to ensure the accuracy and clarity of the marking;

[0303] Abnormal data verification and confirmation:

[0304] Through comparison and verification with other medical image data or clinical information, the authenticity and reliability of the abnormal data are further confirmed;

[0305] Professional doctors or image experts can be invited to review and confirm to ensure the accuracy and clinical value of abnormal data identification; Abnormal data record and report:

[0306] The identified and confirmed abnormal data are recorded in detail, including the type, location, size, shape, etc. of the abnormal data; Generate abnormal data report to provide strong support for clinical decision-making and serve as an important basis for subsequent treatment and monitoring.

[0307] b) Abnormal data marking:

[0308] In combination with the recorded human body structure model, the identified abnormal data is precisely marked to ensure the accuracy and clarity of the marking;

[0309] Use different colors, symbols or labeling methods to make abnormal data stand out in the model, making it easier for subsequent analysis and processing; c) Diagnosis information acquisition and import:

[0310] Automatically retrieve diagnostic information related to the marked part of the abnormal data. These information may come from the patient's medical history, previous examination reports or professional doctor's diagnosis;

[0311] If there is relevant diagnostic information, it is imported into the human structure model and associated with the abnormal data markers to provide strong support for clinical decision-making.

[0312] If there is no relevant diagnostic information, a reminder is issued in the model to prompt the image recorder or relevant medical staff to conduct further examination or improve the diagnostic information of the part;

[0313] d) Abnormal data comparison and analysis:

[0314] Provide the function of selecting to view and compare abnormal data of multiple examinations or different time points in the personal structure diagram, which facilitates doctors to observe the trend of abnormal data changes;

[0315] Through comparison and analysis, further evaluate the clinical significance of abnormal data, and provide important basis for early diagnosis and treatment of diseases.

[0316] A method for identifying and marking abnormal data in a human structure diagram, characterized in that it comprises the following modules:

[0317] I. Abnormal data identification module

[0318] 1.1 Data preparation and preprocessing submodule

[0319] Data acquisition unit: responsible for acquiring high-resolution and high-quality medical image data of the target individual, covering X-ray imaging, MRI imaging, ultrasound imaging, CT and nuclear medicine imaging, etc., to ensure the comprehensiveness, accuracy and timeliness of the data;

[0320] Model loading unit: responsible for loading the human structure model based on the basic physiological information of the target individual, ensuring the basic accuracy of the model;

[0321] Image preprocessing unit: responsible for preprocessing medical image data such as denoising, contrast enhancement, standardization, etc., to improve the accuracy and efficiency of subsequent image processing;

[0322] 1.2 Image comparison and difference analysis submodule

[0323] Image registration unit: using image registration technology, compare the individual's data model with the standard group model;

[0324] Feature extraction and matching unit: through feature extraction and matching, identify the data in the individual model that does not conform to the original tissue and organs, and pay attention to the differences in structure, density, texture or position;

[0325] Threshold setting and determination unit: set the threshold or standard, when the difference between the individual model and the standard model exceeds the threshold, determine it as abnormal data;

[0326] 1.3 Abnormal data preliminary screening submodule

[0327] Artifact exclusion unit: responsible for excluding false positives caused by image artifacts, artifacts, or individual differences, etc.

[0328] Comprehensive analysis unit: combined with multiple medical image data for comprehensive analysis, to improve the accuracy and reliability of abnormal data recognition;

[0329] 1.4 Abnormal data precise positioning and labeling submodule

[0330] Precise positioning unit: using image processing software or tools to precisely position the screened abnormal data;

[0331] Precise labeling unit: precisely label the abnormal data in the human structure model, using different colors, symbols or labeling methods to ensure the accuracy and clarity of the label;

[0332] 1.5 Abnormal data verification and confirmation submodule

[0333] Comparison verification unit: through comparison and verification with other medical image data or clinical information, to confirm the authenticity and reliability of abnormal data;

[0334] Expert review unit: invite professional doctors or image experts to review and confirm, to ensure the accuracy of abnormal data identification;1.6 Abnormal data recording and reporting submodule

[0335] Detailed recording unit: responsible for recording the identified and confirmed abnormal data, including type, location, size, shape, etc.

[0336] Report generation unit: generate abnormal data report to support clinical decision-making, and serve as the basis for subsequent treatment and monitoring;

[0337] Two, abnormal data labeling module

[0338] 2.1 Precise labeling submodule

[0339] Labeling accuracy unit: combined with the recorded human structure model, to ensure the accuracy and clarity of abnormal data labeling;

[0340] Labeling prominence unit: use different colors, symbols or labeling methods to make abnormal data stand out in the model;

[0341] Three, diagnosis information acquisition and import module

[0342] 3.1 Information retrieval submodule

[0343] Automatic retrieval unit: responsible for automatically retrieving diagnostic information related to the abnormal data marked part, the information sources include medical history records, previous examination reports or professional doctors' diagnosis opinions;

[0344] 3.2 Information import and association sub-module

[0345] Information import unit: if there is relevant diagnostic information, import it into the human body structure model and associate it with the abnormal data mark;

[0346] Reminding unit: if there is no relevant diagnostic information, issue a reminder in the model to prompt further examination or improve the diagnostic information;

[0347] Four, abnormal data comparison and analysis module

[0348] 4.1 Data comparison sub-module

[0349] Multiple examination comparison unit: provides the function of selecting to view and compare abnormal data of multiple examinations or different time points in the personal structure diagram;

[0350] Trend analysis unit: through comparison and analysis, evaluate the clinical significance of abnormal data, and provide basis for early diagnosis and treatment of diseases.

[0351] Further, the diagnostic information acquisition and import method in a method of identifying and marking abnormal data in a human body structure diagram, characterized in that, comprising the following steps:

[0352] a) Efficient abnormal data association retrieval:

[0353] Use the abnormal data accurately marked in the human body structure model as the core retrieval keyword or index to automatically and comprehensively retrieve the patient's medical history records, previous examination reports and professional doctors' diagnosis opinions;

[0354] The retrieval range should cover all diagnostic information related to the abnormal data, including but not limited to diagnosis conclusion, treatment history, disease evolution and pathological diagnosis, etc.

[0355] The method of using the abnormal data as the core retrieval keyword or index, characterized in that, comprising the following steps and specific abnormal data types:

[0356] a) Abnormal data type and keyword setting:

[0357] Location: such as "left upper lobe of the lung", "lower edge of the right liver", etc., used to locate the specific position of the abnormal data in the human body structure model;

[0358] Structural morphology abnormalities: such as "mass", "nodule", "stenosis", "dilation", etc., describe the morphological changes of abnormal data;

[0359] Density anomalies: such as "high-density shadows" and "low-density areas," reflecting the density characteristics of abnormal data in the image;

[0360] Texture anomalies: such as "rough", "smooth", "mesh", etc., describe the texture features of the surface of abnormal data;

[0361] Abnormal position: such as "displacement", "missing", "extraterrestrial", etc., indicating abnormal data changes relative to the normal anatomical position;

[0362] b) Setting search keywords or indexes:

[0363] Use the above-mentioned abnormal data types and their specific descriptions as core search keywords or indexes, such as "mass in the upper lobe of the left lung", "high-density shadow at the lower edge of the right liver", and "cardiac displacement".

[0364] Each abnormal data point is set as an independent retrieval entry point for subsequent comprehensive retrieval of relevant diagnostic information;

[0365] c) Comprehensive search process:

[0366] Search scope determination: Using the set search keywords or indexes, the system automatically and comprehensively searches the patient's medical history records, previous examination reports and professional doctors' diagnostic opinions;

[0367] Information Retrieval Association: The retrieval scope covers all diagnostic information related to abnormal data, including but not limited to diagnostic conclusions, treatment history, disease progression and pathological diagnosis, and forms an intuitive and close association between the retrieved diagnostic information and the corresponding abnormal data tags;

[0368] Result verification: The accuracy and reliability of the search results are ensured by comparing them with other medical imaging data or clinical information. If necessary, professional doctors or imaging experts are invited to review and confirm the results.

[0369] b) Importing and linking precise diagnostic information:

[0370] If diagnostic information closely related to the abnormal data is retrieved, advanced medical information system technology should be used to ensure that this information is accurately, completely and promptly imported into the human structural model.

[0371] Imported diagnostic information needs to be intuitively and closely linked with abnormal data markers so that users can simultaneously and conveniently obtain relevant diagnostic information when viewing abnormal data, providing comprehensive and intuitive support for clinical decision-making;

[0372] c) Intelligent missing information reminders and supplements:

[0373] If no relevant diagnostic information is found during the search process, the system should automatically issue a clear and prominent warning signal in the human body structure model, such as highlighting, flashing, etc.

[0374] The warning signal should contain specific instructions to guide the image recording personnel or relevant medical staff to conduct further detailed examination or supplement necessary diagnostic information on the abnormal site, such as repeating sampling, adding other related examination items, etc.

[0375] The system provides an intuitive and easy-to-use interface or tool to facilitate medical staff to quickly and accurately supplement new diagnostic information and ensure that these information are closely associated with the abnormal data marking;

[0376] d) Comprehensive image data recording and tracking:

[0377] For all image data related to abnormal data, including but not limited to body surface image, endoscopic image, X-ray image, ultrasound image, magnetic resonance image and nuclear medicine image, etc., a one-to-one relationship should be established in the human body structure model;

[0378] The system should record and track the changes of these image data, especially after pathological diagnosis or molecular detection, to provide rich and accurate data support for intelligent recommended diagnosis in the later stage;

[0379] e) Construction and optimization of intelligent recommended diagnosis system:

[0380] Based on the abnormal data in the human body structure model, the imported diagnostic information and the associated pathological diagnosis and molecular detection results, the intelligent recommended diagnosis system is constructed and continuously optimized;

[0381] The system should be able to automatically analyze, integrate and deeply mine various data to provide personalized, precise and timely diagnostic suggestions for patients, and assist doctors in making more efficient and accurate treatment decisions;

[0382] f) Model continuous iteration and optimization:

[0383] Through continuous collection of subsequent medical image data, pathological diagnosis and molecular detection results of patients, the model is updated regularly to ensure that the model is highly consistent with the real human anatomy and physiological state of the patient;

[0384] The model's diagnostic information acquisition and import function is continuously optimized to improve the accuracy, practicality and timeliness of the model, providing more precise, comprehensive and personalized support for patient diagnosis and treatment.

[0385] An optimized diagnostic information acquisition and import system in a human body structure model, characterized by the following modules: a high-efficiency abnormal data correlation search module

[0386] Abnormal data indexing sub-module:

[0387] Responsible for using the abnormal data accurately marked in the human structure model as the core retrieval keywords or index;

[0388] Comprehensive retrieval sub-module:

[0389] Responsible for automatically and comprehensively retrieving the patient's medical history records, past examination reports and professional doctor's diagnosis opinions, and the retrieval range covers all diagnostic information related to abnormal data, including but not limited to diagnosis conclusion, treatment history, disease evolution and pathological diagnosis, etc.;

[0390] II. Accurate diagnosis information import and association module

[0391] Information import sub-module:

[0392] If the closely related diagnostic information of the abnormal data is retrieved, it is responsible for using advanced medical information system technology to ensure that these information is accurately, completely and timely imported into the human structure model;

[0393] Information association sub-module:

[0394] Responsible for forming an intuitive and close association between the imported diagnostic information and the abnormal data mark, ensuring that users can synchronously and conveniently obtain related diagnostic information when viewing abnormal data, providing comprehensive and intuitive support for clinical decision-making;

[0395] III. Intelligent missing information prompting and supplementing module

[0396] Missing information prompting sub-module:

[0397] If no diagnostic information directly related to the marked part of the abnormal data is found in the retrieval process, it is responsible for issuing a prominent and clear reminder signal in the human structure model, such as highlighting, flashing and so on;

[0398] Supplementary examination guiding sub-module:

[0399] Responsible for including specific instructions in the reminder signal to guide the image recording personnel or related medical staff to conduct further detailed examination or supplement necessary diagnostic information on the abnormal part, such as repeated sampling, additional related examination items, etc.;

[0400] Information supplementing input sub-module:

[0401] Provide intuitive and easy-to-use interface or tools to facilitate medical staff to quickly and accurately supplement new diagnostic information, and ensure that these information and abnormal data mark form a close association;

[0402] IV. Comprehensive image data recording and tracking module

[0403] Image data association sub-module:

[0404] Responsible for establishing a one-to-one correspondence between all abnormal data-related image data, including but not limited to body surface images, endoscopic images, X-ray images, ultrasound images, magnetic resonance images, and nuclear medicine images, etc., in the human body structure model;

[0405] Data change tracking sub-module:

[0406] Responsible for recording and tracking changes in these image data, especially after pathological diagnosis or molecular detection, to provide rich and accurate data support for intelligent recommendation diagnosis in the later stage;

[0407] Five, intelligent recommendation diagnosis system construction and optimization module

[0408] System construction sub-module:

[0409] Based on abnormal data in the human body structure model, imported diagnostic information, and associated pathological diagnosis and molecular detection results, responsible for building an intelligent recommendation diagnosis system;

[0410] System optimization sub-module:

[0411] Responsible for continuously optimizing the intelligent recommendation diagnosis system, enabling it to automatically analyze, integrate, and deeply mine various data to provide personalized, precise, and timely diagnostic recommendations for patients, while assisting doctors in making more efficient and accurate treatment decisions;

[0412] Six, model continuous iteration and optimization module

[0413] Data collection and update sub-module:

[0414] Responsible for continuously collecting patients' subsequent medical image data, pathological diagnosis and molecular detection results, and regularly updating the model to ensure that the model is highly consistent with the patient's real human anatomy and physiological state;

[0415] Function optimization sub-module:

[0416] Responsible for continuously optimizing the model's diagnostic information acquisition and import functions to improve the model's accuracy, practicality, and timeliness, providing more precise, comprehensive, and personalized support for patient treatment.

[0417] Further, a method of combining clinical information for image analysis and diagnosis and combining with a human body structure model, characterized by the following steps:

[0418] a) Obtain and integrate medical history information and human body structure model association:

[0419] Detailed record of the patient's history, including symptom characteristics, onset time and progression, treatment history, and correlate these information with the corresponding parts in the human body structure model;

[0420] Collect the patient's history, covering the underlying disease, surgery / trauma history and allergy history, provide background information for image analysis, and mark the location of relevant medical history information in the model;

[0421] b) Combine physical examination results with model verification:

[0422] Analysis of patient's vital signs, such as body temperature, blood pressure, etc., associated with imaging findings, and verify the consistency of imaging findings and clinical signs in the human body structure model;

[0423] Evaluate the special signs, through the accurate position matching in the model, ensure that the imaging findings and clinical signs are consistent, and improve the accuracy of diagnosis;

[0424] c) Fusion of laboratory test results and model analysis:

[0425] Use the data of inflammation indicators, tumor markers and liver and kidney function in blood tests to assist in judging the nature of the lesions seen in imaging, and mark the corresponding parts of abnormal indicators in the human body structure model;

[0426] Combine the results of body fluid tests, such as routine, biochemical and culture results of pleural effusion or cerebrospinal fluid, to further clarify the cause of the lesion, and update the relevant information in the model;

[0427] d) Evaluate the dynamic changes of clinical manifestations and update the model:

[0428] Match the symptoms and the timeliness of the image to ensure that the diagnosis conforms to the development process of the disease, and update the image data in real time in the human body structure model to reflect the progress of the disease (such as the disappearance of lung CT shadow when cough and expectoration improve);

[0429] Compare the image data before and after treatment to evaluate the treatment effect, adjust the treatment plan in time, and record the changes in the treatment process in the model;

[0430] e) Consider other clinical background factors and model individualization:

[0431] Analyze the patient's age, gender, occupation, lifestyle and epidemiological history, etc., to provide personalized basis for imaging diagnosis, and consider the influence of these factors on the model in the human body structure model;

[0432] Integrate all clinical information to narrow the range of disease identification and improve the accuracy and pertinence of diagnosis, and visually display and assist in analysis through the model;

[0433] f) Comprehensive analysis and diagnosis and model assisted decision strengthening:

[0434] Comprehensive analysis and diagnosis: Based on the comprehensive integration of clinical information (including detailed medical history records, meticulous physical examination, accurate laboratory test results, etc.) and multi-dimensional, high-resolution imaging data (including optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging, and nuclear medicine imaging, etc.), combined with highly accurate human structure models for in-depth comprehensive analysis; By comparing normal and abnormal tissue structures, accurately assessing the extent and severity of lesions, effectively avoiding misdiagnosis risks, ensuring the high accuracy and reliability of the diagnosis results;

[0435] Model-assisted decision reinforcement: With the help of high-precision human structure models, the lesion site and its surrounding complex anatomical structure, including important blood vessels, nerves, and other key information, are visually and stereoscopically displayed, providing doctors with unprecedented all-around visual support. The model not only helps doctors quickly locate the lesion area, but also assists them in understanding the spatial relationship between the lesion and the surrounding tissue, so as to make more accurate and safe clinical decisions. In addition, the model also supports dynamic updating and real-time data analysis, ensuring that doctors can always obtain the latest and most comprehensive patient information, further improving the efficiency and effectiveness of diagnosis and treatment;

[0436] The process of combining highly accurate human structure models for in-depth comprehensive analysis includes the following steps:

[0437] I. Data integration stage

[0438] 1. Clinical information integration:

[0439] Medical history records: Collect the patient's current medical history (symptom characteristics, onset time, treatment history, etc.) and past history (underlying diseases, surgery / trauma history, allergy history, etc.);

[0440] Physical examination: Record the patient's vital signs (body temperature, blood pressure, etc.) and specialized signs to ensure the comprehensiveness and accuracy of the data; 2. Imaging data integration:

[0441] Collect and organize the patient's multi-dimensional, high-resolution imaging data, including optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging, and nuclear medicine imaging, etc.;

[0442] Ensure the clarity, completeness and timeliness of the imaging data to facilitate subsequent accurate matching and analysis with the model;

[0443] 3. Laboratory test result integration:

[0444] Integrate blood tests (inflammatory markers, tumor markers, liver and kidney function, etc.), body fluid tests (pleural effusion, cerebrospinal fluid, etc.), and other laboratory data;

[0445] These data provide important basis for the judgment of lesion nature;

[0446] II. Model Matching and Validation Phase

[0447] 1. Model Loading and Positioning:

[0448] Open the human structure model based on the patient's basic physiological information, ensuring the model has basic accuracy;

[0449] Determine the specific site or area in the model that needs to be analyzed;

[0450] 2. Image and Model Matching:

[0451] Use medical image processing software or tools to accurately match the integrated imaging data with the corresponding position in the human structure model;

[0452] Through image scaling, rotation, translation and other operations, achieve perfect alignment of the image and the model in spatial position;

[0453] 3. Clinical Signs and Image Verification:

[0454] Verify the consistency of imaging findings and clinical signs (such as vital signs, specialized signs) in the model;

[0455] Ensure that the imaging findings are consistent with the patient's actual symptoms, improving the accuracy of diagnosis;

[0456] III. Deep Comprehensive Analysis Phase

[0457] 1. Normal and Abnormal Tissue Comparison:

[0458] Use the human structure model to compare normal tissue structure with abnormal areas shown in imaging data;

[0459] Accurately identify the type, location, size, shape and other characteristics of abnormal data;

[0460] 2. Lesion Range and Severity Assessment:

[0461] Through the three-dimensional visualization function of the model, stereoscopically display the anatomical structure of the lesion site and its surrounding tissues;

[0462] Accurately assess the range, severity of the lesion and its impact on surrounding important structures (such as blood vessels, nerves, etc.);

[0463] 3. Multi-modal Image Fusion Analysis:

[0464] Fuse and analyze different modalities of imaging data (such as CT, MRI, PET, etc.), forming a comprehensive imaging evidence chain;

[0465] Utilizing image registration technology, precise alignment of different modal image data in space and time is achieved, improving the accuracy of diagnosis;

[0466] 4. Clinical information and image combined analysis:

[0467] In combination with the patient's medical history, physical examination, laboratory examination and other clinical information, the lesions found in imaging are analyzed in depth; Comprehensive judgment of the nature of the lesion, the possible development trend and the impact on the patient's health;

[0468] Four, decision support and result output stage

[0469] 1. Model-assisted decision-making:

[0470] With the help of high-precision human body structure model, intuitive visual support is provided for doctors;

[0471] The model shows the lesion site and its surrounding complex anatomical structure, helping doctors quickly locate the lesion area and understand the spatial relationship between the lesion and the surrounding tissue;

[0472] 2. Diagnosis and treatment plan:

[0473] Based on the results of deep comprehensive analysis, formulate targeted diagnosis and treatment plan;

[0474] Consider the individual factors of patients (such as age, gender, occupation, lifestyle, etc.), ensure the accuracy and safety of the diagnosis and treatment plan;

[0475] 3. Result report generation:

[0476] Automatically generate detailed reports containing diagnosis results, lesion description, treatment recommendations, etc.;

[0477] The report content is visualized to facilitate doctors to quickly understand and grasp the key information.

[0478] g) Model storage and retrieval, easy for subsequent tracking and analysis:

[0479] The human body structure model combined with clinical information and image analysis diagnosis is stored in the hospital server or the national medical server;

[0480] In subsequent patient visits, the past model is quickly queried and retrieved through the retrieval system, combined with new clinical information and image data for updating and optimization, providing strong support for the continuous diagnosis and treatment of patients.

[0481] A system that combines clinical information for image analysis and diagnosis and combines with human body structure model, characterized by comprising the following modules:

[0482] I. Medical history information and model association module

[0483] 1.1 Present illness record and association sub-module

[0484] Present illness record unit: responsible for detailed recording of patient's present illness history, including symptom characteristics, onset time and progression, treatment history;

[0485] Information association unit: responsible for accurately associating these information with the corresponding parts in the human body structure model;

[0486] 1.2 Past history collection and labeling sub-module

[0487] Past history collection unit: responsible for collecting patient's past history, covering basic diseases, surgery / trauma history and allergy history;

[0488] Medical history labeling unit: responsible for labeling the location of relevant medical history information in the model, providing background information for image analysis;

[0489] II. Physical examination results and model verification module

[0490] 2.1 Vital sign analysis and association sub-module

[0491] Vital sign analysis unit: responsible for analyzing patient's vital signs such as body temperature, blood pressure, etc., and associating them with imaging findings;

[0492] Model verification unit: responsible for verifying the consistency of imaging findings and clinical signs in the human body structure model;

[0493] 2.2 Specialized sign evaluation and matching sub-module

[0494] Specialized sign evaluation unit: responsible for evaluating specialized signs to ensure that imaging findings and clinical signs are consistent;

[0495] Precise position matching unit: through precise position matching in the model, improve the accuracy of diagnosis;

[0496] III. Laboratory examination results and model analysis module

[0497] 3.1 Blood index auxiliary judgment sub-module

[0498] Blood index analysis unit: responsible for using inflammatory markers, tumor markers and liver and kidney function data in blood tests to assist in judging the nature of the lesions seen in imaging;

[0499] Abnormal index labeling unit: responsible for labeling the corresponding parts of abnormal indexes in the human body structure model;

[0500] 3.2 Body fluid examination result updating sub-module

[0501] Body fluid result analysis unit: responsible for combining body fluid examination results such as routine, biochemical and culture results of pleural effusion or cerebrospinal fluid to further clarify the cause of the lesion;

[0502] Model information updating unit: responsible for updating relevant information in the model;

[0503] Four, clinical manifestation dynamic change and model updating module

[0504] 4.1 Symptom and image timeliness matching sub-module

[0505] Timeliness matching unit: responsible for matching the timeliness of symptoms and images to ensure that the diagnosis conforms to the disease development process;

[0506] Image data updating unit: responsible for updating image data in real time in the human body structure model to reflect disease progression;

[0507] 4.2 Treatment effect evaluation and recording sub-module

[0508] Treatment effect evaluation unit: responsible for comparing image data before and after treatment to evaluate treatment effect;

[0509] Diagnosis and treatment scheme adjustment unit: responsible for timely adjusting diagnosis and treatment scheme and recording changes in treatment process in the model;

[0510] Five, clinical background factors and model individualization module

[0511] 5.1 Individualization factor analysis unit

[0512] Individualization factor analysis unit: responsible for analyzing patient's age, gender, occupation, lifestyle and epidemiological history, etc. to provide individualized basis for image diagnosis;

[0513] Model influence consideration unit: responsible for considering the influence of these factors on the model in the human body structure model.

[0514] 5.2 Disease identification and model display sub-module

[0515] Disease identification range narrowing unit: responsible for narrowing the disease identification range by integrating all clinical information;

[0516] Model intuitive display unit: responsible for intuitive display and auxiliary analysis through the model to improve the accuracy and pertinence of diagnosis; six, comprehensive analysis diagnosis and model auxiliary decision-making module

[0517] 6.1 Comprehensive analysis diagnosis sub-module

[0518] Comprehensive analysis unit: responsible for comprehensive analysis based on integrated clinical information and imaging data, combined with human body structure model to avoid misdiagnosis;

[0519] Treatment plan making unit: responsible for making targeted treatment plan according to clear cause;

[0520] 6.2 Model-assisted decision-making sub-module

[0521] Visual support providing unit: responsible for providing intuitive visual support through the model;

[0522] Precise positioning information providing unit: responsible for providing precise positioning information to guide clinical diagnosis and treatment decisions;

[0523] Seven, model storage retrieval and continuous diagnosis and treatment support module

[0524] 7.1 Model storage and retrieval sub-module

[0525] Model storage unit: responsible for storing the human body structure model combined with clinical information and image analysis diagnosis to the hospital server or national medical server;

[0526] Model retrieval unit: responsible for quickly querying and calling the past model through the retrieval system when the patient is followed up;

[0527] 7.2 Model update and optimization and continuous diagnosis and treatment support sub-module

[0528] New data fusion unit: responsible for updating and optimizing the model by combining new clinical information and image data;

[0529] Continuous diagnosis and treatment support unit: responsible for providing strong support for the patient's continuous diagnosis and treatment.

[0530] Further, a human body structure model construction system that deeply integrates clear diagnosis and review strategy, characterized by comprising the following modules:

[0531] I. Clear diagnosis module

[0532] 1. Comprehensive examination sub-module

[0533] Image data acquisition unit: responsible for using a variety of high-quality medical image data, including but not limited to optical imaging, X-ray imaging, ultrasonic imaging, magnetic resonance imaging and nuclear medicine imaging, to conduct comprehensive examination of the human body;

[0534] 2. Model construction sub-module

[0535] 3D modeling unit: based on the above medical image data, combined with advanced 3D modeling technology, to construct a human body structure model with basic and accurate human body features;

[0536] 3. Abnormal data matching and marking sub-module

[0537] Image comparison unit: accurately match and mark abnormal data in the human structure model, identify data that does not conform to the original tissue and organs through image comparison and difference analysis technology;

[0538] 4. Comprehensive analysis and diagnosis sub-module

[0539] Medical history and examination integration unit: combine the patient's medical history records, past examination reports and professional doctor's diagnosis opinions, comprehensively analyze and diagnose the abnormal data, and determine the nature and extent of the disease;

[0540] II. Re-examination combination module

[0541] 1. Re-examination plan development sub-module

[0542] Disease nature assessment unit: based on the clear diagnosis, according to the nature and extent of the disease, develop a targeted re-examination plan;

[0543] Technology selection unit: the re-examination plan gives priority to the use of economic, convenient and low-radiation medical imaging technology for patients, such as B-ultrasound and other ultrasonic imaging technology;

[0544] 2. Re-examination execution and data collection sub-module

[0545] Regular re-examination unit: according to the re-examination plan, regularly re-examine the patient and collect new medical imaging data;

[0546] 3. Data comparison and analysis sub-module

[0547] Trend observation unit: compare and analyze the new medical imaging data with the original human structure model, observe the change trend of the disease, evaluate the treatment effect, and adjust the treatment plan in time;

[0548] III. Model optimization and update module

[0549] 1. Fine optimization and individual customization sub-module

[0550] Image fusion unit: combine new medical imaging data and re-examination results to fine optimize and individualize the human structure model;

[0551] 2. Information update sub-module

[0552] Continuous update unit: continuously update the images and diagnosis information in the model to ensure that the model is highly consistent with the patient's real human anatomy and physiological state;

[0553] 3. Data comparison and viewing sub-module

[0554] Trend analysis unit: provides the function of selecting to view and compare abnormal data of multiple examinations or different time points in the personal structure diagram, facilitating doctors to observe the trend of disease changes and provide strong support for clinical decision-making.

[0555] A comprehensive analysis and diagnosis sub-module in a human structure model construction system that deeply integrates explicit diagnosis and review strategies, characterized by the following specific analysis methods and steps:

[0556] 1. Data integration and preprocessing

[0557] Medical history integration: Collect and integrate detailed medical history records of patients, including current medical history (symptom characteristics, onset time, treatment history, etc.) and past medical history (underlying diseases, surgery / trauma history, allergy history, etc.);

[0558] Image data integration: Summarize and analyze multi-dimensional, high-resolution medical image data from previous examinations, including optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging, and nuclear medicine imaging, to ensure the clarity, completeness, and timeliness of the image data;

[0559] Laboratory test result integration: Integrate laboratory data such as blood tests (inflammatory markers, tumor markers, liver and kidney function, etc.), body fluid tests (pleural effusion, cerebrospinal fluid, etc.), to provide auxiliary evidence for the judgment of lesion nature;

[0560] 2. Precise positioning and labeling of abnormal data

[0561] Image comparison and difference analysis: Use advanced image processing and comparison techniques to compare the current image data of the patient with the normal human structure model or previous image data, accurately identify and label abnormal data, including type, location, size, shape, etc.;

[0562] Multi-modal image fusion analysis: Perform fusion analysis on image data from different modalities, achieve precise spatial and temporal alignment through image registration technology, and improve the accuracy of abnormal data identification;

[0563] 3. Comprehensive analysis and diagnosis

[0564] History and image combined analysis: Combine patient medical history records, physical examination results, laboratory test results, and current and past image data to conduct in-depth comprehensive analysis of abnormal data. By comparing normal and abnormal tissue structures, assess the lesion range, invasion degree, and impact on patient health;

[0565] Disease nature and degree evaluation: Based on the comprehensive analysis results, determine the nature (such as inflammation, tumor, etc.) and degree (such as mild, moderate, severe, etc.) of the disease, providing a basis for developing targeted treatment plans;

[0566] Risk of misdiagnosis avoidance: By meticulous comparison and analysis, effectively avoid the risk of misdiagnosis caused by insufficient or misjudgment of single-dimensional information, ensure the high accuracy and reliability of the diagnosis result;

[0567] 4. Diagnosis report generation and auxiliary decision-making

[0568] Automatic generation of diagnosis report: According to the comprehensive analysis results, automatically generate detailed reports containing diagnosis conclusions, lesion descriptions, treatment suggestions, etc. The report content should be visualized to facilitate doctors to quickly understand and grasp the key information;

[0569] Model-assisted decision support: With the help of high-precision human structure model, the lesion site and its surrounding complex anatomical structure are intuitively displayed, including important blood vessels, nerves and other key information. Provide comprehensive visual support for doctors to quickly locate the lesion area and understand the spatial relationship between the lesion and the surrounding tissue, so as to make more accurate and safe clinical decisions.

[0570] A human structure model construction method that deeply integrates explicit diagnosis and review strategy, characterized by the following steps:

[0571] 1. Explicit diagnosis: a) Use a variety of high-quality medical imaging data, including but not limited to optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging, and nuclear medicine imaging, to conduct a comprehensive examination of the human body; b) Based on the above medical imaging data, combined with advanced 3D modeling technology, construct a basic and accurate human structure model; c) Accurately match and mark abnormal data in the human structure model, identify data that do not conform to the original tissues and organs through image comparison and difference analysis technology; d) Combine the patient's medical history records, previous examination reports and professional doctor's diagnosis opinions, comprehensively analyze and diagnose the abnormal data, and determine the nature and extent of the disease.

[0572] 2. Review combination: a) On the basis of explicit diagnosis, according to the nature and extent of the disease, formulate targeted review plan; b) Review plan gives priority to the use of economical, convenient and low-radiation medical imaging technology such as B-ultrasound imaging technology; c) According to the review plan, regularly review the patient and collect new medical imaging data; d) Compare and analyze the new medical imaging data with the original human structure model, observe the trend of the disease, evaluate the treatment effect, and adjust the treatment scheme in time.

[0573] 3. Model optimization and updating: a) Fine-tuning and personalized customization of the human structure model by incorporating new medical image data and review results; b) Continuous updating of image and diagnosis information in the model to ensure that the model is highly consistent with the patient's real human anatomy and physiological state; c) Provide the function of selecting and comparing abnormal data at different time points in the personal structure diagram, which is convenient for doctors to observe the trend of disease changes and provide strong support for clinical decision-making.

[0574] Further, a method of ultrasound recording combined with a human structure model, characterized in that it comprises the following steps:

[0575] 1. Model retrieval: Before the ultrasound examination, retrieve the high-precision human structure model of the target individual from the hospital server or the national medical server;

[0576] Model positioning: Pre-determine the target site or area that needs to be examined in the model;

[0577] 2. Ultrasound data acquisition and preliminary matching

[0578] Data acquisition: Use ultrasound reflection imaging technology to collect real-time ultrasound data of the target site, ensuring the clarity and accuracy of the data. During the collection process, adjust the position, angle, and frequency of the ultrasound probe according to the target site to obtain the best imaging effect;

[0579] Preliminary matching: Through medical image processing software or tools, preliminarily match the real-time collected ultrasound data with the corresponding position in the human structure model. Use image scaling, rotation, translation, etc. to achieve rough alignment in space;

[0580] 3. Real-time matching and data fusion

[0581] Fine matching: Based on preliminary matching, use advanced image registration technology to further optimize the matching result by calculating the similarity or difference between ultrasound data and the model. Ensure that every detail in the ultrasound data is accurately matched with the actual human structure;

[0582] Data fusion: Gradually fuse the optimized and matched ultrasound data into the human structure model. Through visualization tools or software, observe the fusion effect in real time to ensure the accurate position of the data in the model;

[0583] 4. Gradual filling and model updating

[0584] Gradual filling: As the ultrasound examination progresses, continuously fill the newly collected ultrasound data into the human structure model. Through layering or regionalization, gradually complete the data filling of the entire target site;

[0585] Model updating: During the data filling process, necessary updates and optimizations are made to the model. Based on the ultrasound examination results, structural details in the model are adjusted to ensure that the model remains highly consistent with the real human anatomy;

[0586] 5. Model viewing and dynamic data analysis

[0587] Real-time viewing: Provides the function of real-time viewing of filled ultrasound data in the personal structure diagram. Users can always know the ultrasound image information of the target site through the intuitive structure diagram;

[0588] Dynamic analysis: Supports users to perform dynamic analysis on the filled ultrasound data. By comparing ultrasound data at different time points or different examination times, the trend of lesion changes is observed, providing strong support for clinical decision-making;

[0589] 6. Result saving and subsequent application

[0590] Result saving: Save the filled ultrasound data human structure model as a whole or layered file for subsequent viewing, analysis and quick retrieval and reference;

[0591] Subsequent application: In the follow-up visit of patients, the model is quickly queried and called through the retrieval system, combined with new clinical information and image data for updating and optimization, providing strong support for the continuous diagnosis and treatment of patients.

[0592] An ultrasound recording and human structure model combined system, characterized in that it comprises the following modules:

[0593] I. Human structure model acquisition and preparation module

[0594] 1.1 Model retrieval sub-module

[0595] Function: Responsible for retrieving the high-precision human structure model of the target individual from the hospital server or the national medical server before performing ultrasound examination;

[0596] 1.2 Model positioning sub-module

[0597] Function: Responsible for pre-determining the target site or area that needs to be examined by ultrasound in the model;

[0598] II. Ultrasound data acquisition and preliminary matching module

[0599] 2.1 Data acquisition sub-module

[0600] Function: Use ultrasound reflection imaging technology to collect real-time ultrasound data of the target site, ensuring the clarity and accuracy of the data. During the collection process, adjust the position, angle and frequency of the ultrasound probe according to the target site to obtain the best imaging effect;

[0601] 2.2 Preliminary Matching Submodule

[0602] Function: Preliminary matching of real-time ultrasound data with corresponding positions in the human structure model is achieved through image scaling, rotation, translation, etc. using medical image processing software or tools.

[0603] Three, Real-time Matching and Data Fusion Module

[0604] 3.1 Fine Matching Submodule

[0605] Function: Based on preliminary matching, advanced image registration techniques are used to further optimize the matching results by calculating the similarity or difference between ultrasound data and the model, ensuring that every detail in the ultrasound data matches the actual human structure accurately.

[0606] 3.2 Data Fusion Submodule

[0607] Function: Responsible for gradually fusing the optimized matching ultrasound data into the human structure model, observing the fusion effect in real time through visualization tools or software to ensure accurate positioning of data in the model.

[0608] Four, Gradual Filling and Model Updating Module

[0609] 4.1 Gradual Filling Submodule

[0610] Function: As the ultrasound examination progresses, new ultrasound data is continuously filled into the human structure model, and through hierarchical or regional methods, the data filling of the entire target area is gradually completed.

[0611] 4.2 Model Updating Submodule

[0612] Function: During data filling, the model is updated and optimized as necessary, adjusting the structural details in the model according to the ultrasound examination results to ensure that the model is highly consistent with the real human anatomy.

[0613] Five, Model Viewing and Dynamic Data Analysis Module

[0614] 5.1 Real-time Viewing Submodule

[0615] Function: Provides real-time viewing of filled ultrasound data in the personal structure diagram, allowing users to understand the ultrasound image information of the target area at any time through the intuitive structure diagram.

[0616] 5.2 Dynamic Analysis Submodule

[0617] Function: Supports users in dynamically analyzing filled ultrasound data, observing the trend of changes in lesions by comparing ultrasound data at different time points or different examination times, providing strong support for clinical decision-making.

[0618] VI. Result Saving and Subsequent Application Module

[0619] 6.1 Result Saving Submodule

[0620] Function: It is responsible for saving the human body structure model after filling in the ultrasound data as a whole or layered file, which is convenient for subsequent viewing, analysis and quick retrieval and reference;

[0621] 6.2 Subsequent Application Submodules

[0622] Function: During subsequent patient visits, the model can be quickly retrieved and accessed through the search system, and updated and optimized by combining new clinical information and imaging data, providing strong support for the patient's ongoing diagnosis and treatment.

[0623] Furthermore, a method for applying a human structural model that combines anomaly detection, anomaly comparison, and disease probability assessment is characterized by the following steps:

[0624] a) Anomaly detection and preliminary assessment:

[0625] Using high-precision medical imaging data from human structural models, image processing and comparison techniques are employed to discover and identify abnormal data that do not conform to the structure of normal tissues and organs.

[0626] A preliminary assessment of the identified abnormal data is conducted, including the type, location, size, morphology, and possible clinical significance of the abnormality.

[0627] b) Anomaly Comparison and Analysis:

[0628] The currently discovered abnormal data is compared with the patient's previous medical imaging data to analyze the changing trends and possible developments of the abnormal data.

[0629] By combining the patient's medical history, previous examination reports, and the diagnostic opinions of professional doctors, abnormal data are analyzed and interpreted in depth.

[0630] c) Disease probability assessment:

[0631] Based on the characteristics of abnormal data, patients' clinical information, and medical imaging data, advanced medical data analysis algorithms are used to assess the probability of disease occurrence and possible disease types.

[0632] Taking into account individual factors such as the patient's age, gender, and lifestyle habits, the probability of disease is further revised and adjusted;

[0633] d) Identification needs and selection of auxiliary imaging methods:

[0634] According to the disease probability assessment results and clinical needs, determine whether it is necessary to assist other imaging methods for further identification; consider the probability of identification, cost, radiation risk and clinical value, and select appropriate auxiliary imaging methods such as CT, MRI, PET-CT, etc.

[0635] e) New discovery and diagnosis notification:

[0636] For new abnormalities or new disease probabilities discovered through abnormal contrast and disease probability assessment, inform the original patient in a timely manner, and explain the clinical significance and possible impact of the new discovery in detail;

[0637] At the same time, inform relevant medical staff so that they can make necessary corrections and adjustments to the patient's diagnosis and treatment plan according to the new discovery;

[0638] f) Continuous tracking and model updating:

[0639] Continuously track the patient, regularly collect new medical imaging data, and update it to the human body structure model;

[0640] Combine new clinical information and imaging data to iteratively optimize the human body structure model, ensuring that the model is highly consistent with the patient's real human anatomy and physiological state.

[0641] The disease probability assessment method, characterized by the following optimized steps:

[0642] a) Data deep integration and advanced analysis:

[0643] 1. Deep extraction of abnormal data features:

[0644] Based on high-precision human body structure models and medical imaging data, deeply extract multi-dimensional features of abnormal data, including but not limited to abnormal types, accurate positions, sizes, shapes, densities, and textures, etc.

[0645] Use advanced image processing techniques to further refine the boundary and internal structure features of abnormal data;

[0646] 2. Comprehensive integration of clinical information:

[0647] Integrate the patient's detailed clinical information, including medical history records, symptom descriptions, physical signs, family medical history, and lifestyle habits, etc.; introduce the patient's lifestyle, environmental factors, and psychological status, etc. Additional dimensions to enrich the clinical information background;

[0648] 3. Multi-dimensional fusion of medical imaging data:

[0649] Fuse X-ray imaging, ultrasonic imaging, magnetic resonance imaging, nuclear medicine imaging and other medical imaging data to form a comprehensive imaging evidence chain;

[0650] Utilizing image registration technology, precise alignment of different modalities of image data in space and time is achieved;

[0651] 4. Intelligent integration of past case data:

[0652] Collect and intelligently integrate massive amounts of past diagnosis data of diseases with the same or similar characteristics, including disease types, various abnormal data, treatment paths, prognosis, etc.

[0653] Apply machine learning techniques to mine implicit patterns and association rules in past cases, providing strong support for disease probability calculation.

[0654] b) Precise calculation of disease probability:

[0655] 1. Deep application of medical data analysis algorithms:

[0656] Utilize cutting-edge medical data analysis algorithms such as deep learning and reinforcement learning to comprehensively analyze abnormal data features, clinical information, medical image data, and past case data, and accurately calculate the probability of disease occurrence.

[0657] Introduce algorithm interpretability techniques to improve the transparency and understandability of the disease probability calculation process.

[0658] 2. Disease type and risk level prediction:

[0659] Based on algorithm output results, not only predict possible disease types and their probability distribution, but also assess the risk level of the disease.

[0660] Combine the diagnosis results and treatment outcomes of similar characteristic diseases in past cases to calibrate and optimize the prediction results.

[0661] c) Fine-tuned adjustment of individual factors:

[0662] 1. Comprehensive consideration of patient characteristics:

[0663] Fine-tune the disease probability by considering individual factors such as age, gender, lifestyle, genetic background, and psychological state;

[0664] Introduce patient biomarker detection data to further individualize disease probability adjustment.

[0665] 2. Super-fine risk assessment stratification:

[0666] According to individual factors and the disease development trajectory of similar patients in past cases, stratify the disease risk level into multiple levels;

[0667] Use risk stratification results to provide personalized health management recommendations and medical intervention plans for patients.

[0668] d) Intelligent output of results and in-depth interpretation:

[0669] 1. Intelligent generation of probability and type reports:

[0670] Automatically generate detailed reports containing disease occurrence probability, possible disease types, risk levels, and comprehensive assessment results considering the impact of previous cases;

[0671] Visualize the report content for easy understanding and grasping of key information by doctors;

[0672] 2. In-depth interpretation of clinical significance:

[0673] Provide in-depth clinical interpretation of the assessment results, clarifying their impact on the patient's current and future health status, potential medical intervention needs, and the reference value and significance of previous cases;

[0674] Provide personalized treatment recommendations based on the assessment results to assist doctors in making more accurate diagnosis and treatment decisions;

[0675] e) Continuous iteration and intelligent upgrade:

[0676] 1. New data fusion and model adaptive update:

[0677] Continuously integrate new clinical information, medical image data, and newly added previous case data of patients to achieve adaptive iterative update of disease probability assessment models;

[0678] Introduce online learning mechanisms to enable the model to learn and adapt to new medical data and knowledge in real time;

[0679] 2. Algorithm intelligent optimization and performance improvement:

[0680] Intelligently optimize medical data analysis algorithms based on actual application feedback, the latest advances in medical research, and the continuous accumulation of previous case data;

[0681] Improve the computational efficiency, accuracy, and generalization ability of the algorithm to ensure the stability and reliability of disease probability assessment results.

[0682] A human structure model application system combining abnormality discovery, abnormality comparison, and disease probability assessment, characterized by containing the following modules:

[0683] I. Abnormality discovery and preliminary assessment module

[0684] 1.1 Abnormal data identification sub-module

[0685] Image processing and comparison unit: responsible for using high-precision medical image data in human structure models to find and identify abnormal data that do not conform to normal tissue and organ structures through image processing and comparison technology;

[0686] Preliminary evaluation unit: responsible for preliminary evaluation of identified abnormal data, including type, location, size, shape and possible clinical significance of abnormalities;

[0687] II. Abnormal contrast and analysis module

[0688] 2.1 Abnormal data comparison sub-module

[0689] Historical data comparison unit: responsible for comparing the current discovered abnormal data with the patient's past medical image data, analyzing the trend and possible development of abnormal data;

[0690] In-depth analysis unit: responsible for combining patient medical history records, past examination reports and professional doctor's diagnosis opinions to conduct in-depth analysis and interpretation of abnormal data;

[0691] III. Disease probability evaluation module

[0692] 3.1 Probability evaluation sub-module

[0693] Data analysis algorithm unit: responsible for using advanced medical data analysis algorithms based on the characteristics of abnormal data, patient clinical information and medical image data to evaluate the probability of disease occurrence and possible disease types;

[0694] Personalized factor adjustment unit: responsible for considering patient's age, gender, lifestyle and other personalized factors to further correct and adjust disease probability;

[0695] IV. Identification needs determination and auxiliary imaging method selection module

[0696] 4.1 Identification needs determination sub-module

[0697] Need evaluation unit: responsible for determining whether auxiliary imaging methods are needed for further identification according to disease probability evaluation results and clinical needs;

[0698] 4.2 Imaging method selection sub-module

[0699] Method selection unit: responsible for selecting appropriate auxiliary imaging methods such as CT, MRI, PET-CT, etc. by considering identification probability, cost, radiation risk and clinical value;

[0700] V. New discovery and diagnosis notification module

[0701] 5.1 New discovery notification sub-module

[0702] Patient notification unit: responsible for timely informing the original patient of new abnormalities or new disease probabilities found through abnormal contrast and disease probability assessment, and explaining the clinical significance and possible impact of the new findings in detail;

[0703] Medical staff notification unit: responsible for simultaneously informing relevant medical staff so that they can make necessary corrections and adjustments to the patient's diagnosis and treatment plan based on the new findings;

[0704] Six, continuous tracking and model updating module

[0705] 6.1 Continuous tracking sub-module

[0706] Image data collection unit: responsible for continuous tracking of patients and regular collection of new medical image data;

[0707] 6.2 Model updating sub-module

[0708] Model iterative optimization unit: responsible for iterative optimization of the human structure model combined with new clinical information and image data, to ensure that the model is highly consistent with the patient's real human anatomy and physiological state.

[0709] A human structure model application method combined with precise disease probability calculation, characterized by the following steps: a) data assignment and weight setting fusion in human structure model: in the human structure model, classify and set weights for various data according to their influence on disease probability calculation, specifically including:

[0710] Clinical information:

[0711] Pathological diagnosis: give high weight value in the model and accurately label the pathological diagnosis result and its position, as it directly reflects the nature of tissue lesions and has a decisive influence on disease probability calculation;

[0712] Symptom description and physical sign manifestation: according to specificity and severity, give medium weight value in the model and label the specific manifestations and positions of related symptoms and signs;

[0713] Chief complaint, family history, lifestyle, environmental factors and psychological status, etc.: as auxiliary information, give lower weight value in the model, but may mark higher impact in specific disease-related areas;

[0714] Medical image data:

[0715] High-resolution images (such as CT, MRI): give high weight value in the model and accurately match image data with model structure, as they can clearly show tissue structure and lesion details;

[0716] Functional imaging (e.g. PET, SPECT): Reflects tissue metabolism and functional status, assigned medium to high weight values in the model, especially in suspected tumor areas;

[0717] Conventional imaging (e.g. X-ray plain film, ultrasound imaging): Provides basic anatomical information, assigned medium or lower weight values in the model;

[0718] Prior case data:

[0719] Diagnosis results of similar cases: Assigned high weight values in the model, and associated with possible disease types of the current case;

[0720] Treatment path and prognosis outcome: Provides reference for treatment decision, assigned medium weight values in the model;

[0721] Implicit patterns and association rules in cases: Obtained through machine learning mining, assigned lower but not ignored weight values in the model;

[0722] b) Data integration, probability calculation and model optimization:

[0723] Integrate classified clinical information, medical imaging data and prior case data into the human structure model, use medical data analysis algorithms (such as deep learning, reinforcement learning, etc.), consider the weight values of each data, and perform accurate calculation of disease probability in the model;

[0724] Improve the transparency and understandability of the disease probability calculation process through algorithmic interpretability techniques, so that doctors can understand the impact of each data on the final probability, and visually display in the model;

[0725] c) Result output, interpretation and model application:

[0726] Generate detailed reports containing disease probability, possible disease types, risk levels and comprehensive evaluation results considering the influence of prior cases, and the report content is visualized in the human structure model, highlighting the impact area of high weight value data on the results; Interpret the evaluation results in depth in the clinical sense, clarify the specific meaning of high weight value data in the current case, and its impact on the patient's health status and medical intervention needs, and mark the corresponding area and suggestions in the model;

[0727] Integrate the updated disease probability evaluation results and interpretation information into the human structure model in real time, provide intuitive visual support and accurate positioning information for doctors, and assist clinical diagnosis and treatment decisions.

[0728] Further, a method for optimizing the establishment of an abnormal structure standard database of human body, the core goal of which is to gather and enrich the abnormal and confirmed data in the model of human body structure, and at the same time, through in-depth learning of the abnormal performance of various medical image data corresponding to the confirmed diagnosis, continuously improve the accuracy of imaging in the diagnosis of abnormal structures. The method is divided into the following steps in detail: 1, data collection and sorting

[0729] Multi-source data acquisition:

[0730] Systematic collection: Collect abnormal human structure data from different medical institutions, research laboratories and medical image centers, covering high-quality medical image data such as optical imaging (body surface image, endoscopic image), X-ray imaging (X-ray film, CT), ultrasonic imaging (B-ultrasound, color Doppler ultrasound), magnetic resonance imaging (MRI, fMRI) and nuclear medicine imaging (PET, SPECT); Diagnosis coverage: Ensure that the collected data comprehensively cover the morphological, functional and metabolic abnormalities caused by various diseases diagnosed by pathology, molecular detection or other methods, and ensure the comprehensiveness, high resolution and accuracy of the data;

[0731] Data sorting and classification:

[0732] Fine classification: According to the dimensions of disease type, abnormal morphology, functional influence and metabolic changes, the collected abnormal structure data is finely classified and sorted to support subsequent efficient retrieval and in-depth analysis;

[0733] Pretreatment improvement: Through denoising, standardization and other pretreatment methods, the data quality and consistency are improved to ensure the accurate application of data in the model;

[0734] 2, abnormal structure recognition, labeling and diagnosis verification

[0735] Image processing and comparison:

[0736] Comprehensive comparison: Use advanced image processing and comparison technology to comprehensively compare abnormal structure data with normal human structure model to accurately identify abnormal data that does not conform to normal tissues and organs;

[0737] Precise positioning and labeling: Precisely position the abnormal data and label the abnormal type, location, size, shape and possible clinical significance in detail;

[0738] Diagnosis data verification:

[0739] Strict verification: Strictly collect and verify the diagnosis data of medical personnel to ensure that the abnormal structure data in the database comes from diagnosed cases;

[0740] Intuitive Association: Clearly label the results of pathological diagnosis, molecular detection, or other definitive methods in the human structure model, and form an intuitive association with the corresponding medical image data;

[0741] 3. Database Construction and Management

[0742] Database Design:

[0743] Efficient Architecture: Design a highly optimized database architecture, including data tables, fields, indexes, etc., to ensure efficient storage and fast retrieval of data;

[0744] Advanced System: Use advanced database management systems such as relational databases or NoSQL databases to handle large data volumes and high concurrency access requirements;

[0745] Data Import and Storage:

[0746] Complete Import: Import the abnormal structure data that has been cleaned, recognized, labeled, and verified by definitive diagnosis into the database, ensuring data integrity, consistency, and security;

[0747] Encrypted Backup: Encrypt and backup data to ensure security and reliability during storage and transmission;

[0748] 4. Data Retrieval, Analysis, and Learning

[0749] Efficient Retrieval Mechanism:

[0750] Multi-dimensional Retrieval: Establish a multi-dimensional retrieval mechanism based on keywords, disease types, and abnormal features to achieve fast and accurate data retrieval;

[0751] Visual Interface: Provide an intuitive and easy-to-use visual retrieval interface for users to efficiently browse and query abnormal structure data;

[0752] In-depth Analysis: Use data mining and machine learning techniques to analyze abnormal structure data in depth, discovering potential disease associations, abnormal patterns, and development trends;

[0753] Image Learning: Specifically learn the abnormal manifestations of various medical image data (including optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging, and nuclear medicine imaging) corresponding to definitive diagnosis, continuously improving the diagnostic probability of imaging in specific abnormal structure identification;

[0754] Decision Support: Provide data analysis reports and visual displays to support doctors' decision-making and promote the in-depth development of medical research;

[0755] 5. Continuous Update and Optimization

[0756] Continuous Data Collection:

[0757] Long-term Cooperation: Establish long-term stable cooperation with medical institutions, research laboratories, etc. to continuously collect the latest abnormal structure data and its diagnosis information, ensuring the timeliness and comprehensiveness of the database;

[0758] Database Iterative Optimization:

[0759] Performance Improvement: According to user feedback, data analysis results and the development of medical imaging, continuously optimize database architecture, retrieval mechanism and data analysis algorithm to improve database performance and user experience;

[0760] Model Update: Regularly update the model to ensure that the abnormal structure data in the model is highly consistent with the real human anatomy and physiological state, providing solid support for medical imaging diagnosis.

[0761] An optimized system for establishing a database of abnormal structure standards, characterized by the following modules to achieve the core goal of gathering and enriching abnormal and diagnosed data in human structure models, and improving the accuracy of imaging in diagnosing abnormal structures through in-depth learning. The specific modules are as follows:

[0762] I. Data Collection and Organization Module

[0763] Multi-source data acquisition sub-module

[0764] Systematic collection unit: responsible for collecting abnormal human structure data from different medical institutions, research laboratories and medical imaging centers, covering high-quality medical imaging data such as optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging and nuclear medicine imaging;

[0765] Diagnosis coverage unit: ensure that the collected data fully cover the morphological, functional and metabolic abnormalities caused by various diseases diagnosed by pathology, molecular detection or other methods, ensuring the comprehensiveness, high resolution and accuracy of the data;

[0766] Data organization and classification sub-module

[0767] Fine classification unit: according to disease types, abnormal morphology, functional impact and metabolic changes, etc., the collected abnormal structure data is classified and organized in detail to support subsequent efficient retrieval and in-depth analysis;

[0768] Preprocessing improvement unit: through denoising, standardization and other preprocessing methods, improve data quality and consistency to ensure accurate application of data in the model;

[0769] II. Abnormal structure recognition, labeling and diagnosis verification module

[0770] Image processing and comparison sub-module

[0771] Overall comparison unit: Using advanced image processing and comparison techniques, compare abnormal structure data with normal human structure model, accurately identify abnormal data inconsistent with normal tissues and organs;

[0772] Precise positioning and labeling unit: Precise positioning of abnormal data, and detailed labeling of abnormal type, location, size, shape and possible clinical significance;

[0773] Diagnosis data verification sub-module

[0774] Strict verification unit: Strictly collect and verify the diagnosis data of medical personnel, ensure that the abnormal structure data in the database comes from diagnosed cases;

[0775] Intuitive association unit: Clearly label the results of pathological diagnosis, molecular detection or other diagnosis methods in the human structure model, and form intuitive association with the corresponding medical image data;

[0776] III. Database construction and management module

[0777] Database design sub-module

[0778] Efficient architecture unit: Design highly optimized database architecture, including data table, field, index, etc., to ensure efficient storage and fast retrieval of data;

[0779] Advanced system unit: Use relational database or NoSQL database and other advanced database management systems to meet the needs of large data volume and high concurrency access;

[0780] Data import and storage sub-module

[0781] Complete import unit: Import abnormal structure data that has been sorted, recognized, labeled and verified by diagnosis into the database, to ensure data integrity, consistency and security;

[0782] Encryption backup unit: Encrypt and backup data to ensure security and reliability during storage and transmission;

[0783] Efficient retrieval mechanism sub-module

[0784] Multi-dimensional retrieval unit: Establish multi-dimensional retrieval mechanism based on keywords, disease types, abnormal features, etc., to achieve fast and accurate data retrieval;

[0785] Visual interface unit: Provide intuitive and easy-to-use visual retrieval interface for users to efficiently browse and query abnormal structure data;

[0786] Data analysis, mining and learning sub-module

[0787] In-depth analysis unit: using data mining and machine learning techniques, in-depth analysis of abnormal structure data, find potential disease association, abnormal pattern and development trend;

[0788] Image learning unit: especially for the diagnosis of various medical imaging data corresponding to the abnormal performance of the learning, constantly improve the image in the specific abnormal structure recognition diagnosis probability;

[0789] Decision support unit: provide data analysis report and visualization, provide decision support for doctors, and promote the in-depth development of medical research;

[0790] Five, continuous update and optimization module

[0791] Data collection sub-module

[0792] Long-term cooperation unit: establish long-term stable cooperation with medical institutions, research laboratories and other institutions, continuously collect the latest abnormal structure data and its diagnosis information, ensure the timeliness and comprehensiveness of the database;

[0793] Database iteration optimization sub-module

[0794] Performance improvement unit: according to user feedback, data analysis results and the development of medical imaging, constantly optimize the database architecture, retrieval mechanism and data analysis algorithm, improve the performance and user experience of the database;

[0795] Model update unit: regularly update the model, ensure that the abnormal structure data in the model is highly consistent with the real human anatomy and physiological state, and provide solid support for the diagnosis of medical imaging.

[0796] A method for processing and recording physical specimens, characterized by the following steps:

[0797] a) Specimen acquisition and pretreatment:

[0798] Acquire physical specimens of target human tissues or organs, ensure the integrity and representativeness of the specimens;

[0799] Wash, fix and preserve the specimen as necessary to prevent decay and deformation, maintain the original morphology and structural characteristics of the specimen.

[0800] b) Detailed dissection and observation:

[0801] Use dissection tools to finely dissect the specimen, separate and expose each tissue, organ and its internal structure;

[0802] Through naked eye observation, image, microscope observation and special staining technology, record the morphology, color, texture, structural characteristics and their mutual relationship of the specimen in detail.

[0803] c) Data recording and organization:

[0804] Detailed records of data obtained during the dissection observation, including but not limited to written descriptions, medical image records, photographs, and videos.

[0805] Classify and organize the recorded data by organs, systems, and other categories, and establish a specimen database for future retrieval and analysis.

[0806] d) Comparison and verification with medical image data and model data:

[0807] Compare the physical specimen data with the corresponding medical image data and human structure model data to verify the accuracy and reliability of the model.

[0808] In-depth analysis of the differences, if necessary, to modify and optimize the model to ensure consistency with the physical specimen. DETAILED DESCRIPTION

[0809] A specific embodiment of an optimized human structure model establishment method: Name: Zhang San, Gender: Male, Age: 45 years old, Height: 175 cm, Weight: 70 kg, Ethnicity: Asian

[0810] a) Basic information acquisition and preliminary modeling

[0811] Information acquisition: Detailed records of Zhang San's height, weight, gender, age, ethnicity, and other basic information.

[0812] Preliminary modeling: Using advanced 3D modeling software (such as Mimics, 3D Slicer, etc.), combined with Zhang San's basic physiological information, a 3D human structure model with basic human characteristics (such as bones, muscles, major organs, etc.) is constructed.

[0813] b) Comprehensive medical image data collection and database establishment

[0814] Image data collection:

[0815] Optical imaging: Collect Zhang San's body surface images (such as full-body photos), endoscopic images (such as gastroscopy, colonoscopy), and pathological images (such as tissue sections).

[0816] X-ray imaging: Take X-ray plain film, CT scan images.

[0817] Ultrasound imaging: Perform B-mode ultrasound and color Doppler ultrasound examinations to obtain ultrasound images of organs such as liver, kidneys, and heart.

[0818] Magnetic resonance imaging: Perform MRI and fMRI scans to obtain detailed structure images of the brain and spine.

[0819] Nuclear Medicine Imaging: Perform PET and SPECT scans to obtain tissue metabolism and function information.

[0820] Database Establishment:

[0821] All collected medical image data are classified and organized by type, shooting time, and shooting site, and stored in the hospital server or cloud storage platform to establish Zhang San's individual comprehensive database.

[0822] c) Model refinement optimization and individual customization

[0823] Refinement Optimization:

[0824] Based on Zhang San's individual comprehensive database, use medical image processing software to deeply refine and optimize the 3D human body structure model. For example, adjust the bone structure according to the CT scan image, optimize the muscle and soft tissue morphology according to the MRI image, and adjust the tissue metabolism information according to the PET image.

[0825] Individual Customization:

[0826] According to Zhang San's unique physiological characteristics (such as specific disease history, surgery history, etc.), customize the model. For example, if Zhang San has a history of heart disease, pay special attention to the accurate modeling of the heart structure.

[0827] Iterative Optimization:

[0828] Through repeated adjustment, correction and verification of model details, ensure that the model is highly consistent with Zhang San's real human anatomy. For example, invite professional doctors to review the model and make necessary adjustments based on feedback.

[0829] One embodiment of using endoscopic images in optical imaging technology for human body structure model matching and real-time updating:

[0830] Duodenal bulb ulcer patient visit

[0831] I. Endoscopic image acquisition and preparation stage

[0832] 1. Comprehensive and systematic collection

[0833] Image data collection: Collect the endoscopic images and video data of the duodenal bulb ulcer patient from the hospital's endoscopic center system comprehensively. Ensure that the data covers different shooting times (such as initial diagnosis, review, etc.), sites (focus on the duodenal bulb), diagnostic needs (such as ulcer size, depth assessment), and endoscopic path records (complete path from the esophagus to the duodenum);

[0834] Clarity and accuracy: Ensure that all collected endoscopic images and video materials have high definition, accuracy and completeness, which can clearly show the details of the duodenal bulb and ulcer site;

[0835] 2. Orderly classification

[0836] Classification criteria: According to the shooting time, shooting site (duodenal bulb), diagnostic needs (ulcer evaluation), endoscopic path, etc. Classification criteria, the collected endoscopic images and video materials are orderly classified and arranged;

[0837] Arrangement results: Form a structured endoscopic image database, which is convenient for subsequent efficient matching and fusion;

[0838] II. Endoscopic image recognition and preliminary matching stage

[0839] 1. Key feature automatic identification

[0840] Feature recognition: Use advanced image recognition technology to automatically identify key features in endoscopic images, such as duodenal bulb, ulcer edge, blood vessel distribution, etc.

[0841] Accuracy and efficiency: Through automatic identification technology, improve the accuracy and efficiency of matching, reduce the time and error of manual identification:

[0842] 2. Endoscopic path matching and fusion

[0843] Path recognition: Especially for endoscopic path records, through identifying the moving track of endoscope and the surrounding environment, and accurately matching and fusing with the pre-established human body structure diagram;

[0844] Specific position determination: Combined with the duodenal anatomical structure, digestive tract distribution and running in the human body structure diagram, and the moving track of endoscope, determine the specific position of ulcer in the human body structure diagram;

[0845] III. Model loading and preliminary matching stage

[0846] 1. Model loading

[0847] Model retrieval: Open the human body structure model based on the basic physiological information of the patient (such as height, weight, gender, etc.), ensure the basic accuracy of the model, and support the subsequent fine adjustment;

[0848] 2. Preliminary matching and panoramic map mapping

[0849] Preliminary matching: Preliminary matching of the identified endoscopic images and video materials with the duodenal bulb region in the human body structure model, through image scaling, rotation, translation, etc. Operation, realize the approximate alignment of spatial position;

[0850] Panorama mapping: Ensures that the endoscopic images are accurately mapped to their corresponding positions in the model, laying the foundation for subsequent precise matching;

[0851] Four, precise matching and verification phase

[0852] 1. Optimize matching results

[0853] Fine adjustment: Based on the preliminary matching, use image registration technology to further optimize the matching results by calculating the similarity or difference between the endoscopic matching images and the model;

[0854] Detail agreement: Ensure that the ulcer edge, blood vessel distribution and other detailed features are completely consistent with the actual human structure;

[0855] 2. Panorama stitching and mapping

[0856] Panorama stitching: Use image stitching technology to stitch the captured endoscopic images into seamless panoramas, fully displaying the duodenal bulb and ulcer site;

[0857] Precise mapping: Precisely map the panorama to the corresponding position in the human structure model to achieve a function similar to navigation in real scene viewing;

[0858] 3. Careful verification and adjustment

[0859] Verification tool: Carefully verify the matching results through visual tools or software.

[0860] Adjust and optimize: If there are mismatches or large errors, return to the previous step for adjustment until the desired fusion effect is achieved.

[0861] Five, model structure update and optimization phase

[0862] 1. Fine adjustment and optimization

[0863] Pay attention to details: Based on the newly matched endoscopic panorama data, fine-tune and optimize the duodenal bulb area in the human structure model, paying special attention to details such as ulcer shape, depth, and surrounding blood vessel distribution.

[0864] Anatomical structure compliance: Ensure that the model's anatomical structure conforms to reality, improving the model's accuracy and practicality.

[0865] 2. Necessary supplement and correction

[0866] Supplement and correction: Make necessary supplements and corrections to the model, such as supplementing small blood vessels, nerve branches and other structures, to improve the model's completeness and accuracy in reflecting endoscopic findings.

[0867] Six, dynamic update and structure data integration phase

[0868] 1. Integrated or Layered Preservation

[0869] Preservation Format: Save the updated endoscopic panoramic view and human structure model as a whole or layered file for future viewing, analysis, and quick retrieval.

[0870] 2. Stereoscopic Linkage and Real-time Update

[0871] Viewing Function: Provide an intuitive and easy-to-use structure data viewing function, allowing doctors to simultaneously view the current operation display screen and the human structure model display screen on different display screens.

[0872] Real-time Navigation: Through the precise fusion of panoramic views and models, achieve a real-time navigation-like function, providing intuitive visual guidance for surgical operations.

[0873] Clinical Decision Support: Pay special attention to the detailed information of the ulcer site seen in the endoscopic panoramic view, providing strong support for clinical decision-making and disease monitoring.

[0874] A method for identifying and marking abnormal data in a human structure map, specific embodiment: identification and marking of abnormal data of a patient with lung infection

[0875] I. Abnormal Data Identification

[0876] 1. Data Preparation and Preprocessing

[0877] Obtain Medical Imaging Data: Obtain high-resolution, high-quality medical imaging data of the patient with lung infection, including X-ray imaging (such as chest X-ray), CT scan images, MRI imaging (if more detailed soft tissue information is needed), etc., to ensure the comprehensiveness, accuracy, and timeliness of the data;

[0878] Load Human Structure Model: Based on the patient's basic physiological information (such as height, weight, gender, etc.), load the pre-constructed human structure model, which should have accurate basic human anatomy;

[0879] Image Data Preprocessing: Preprocess the obtained medical imaging data, including denoising, contrast enhancement, standardization, etc., to improve the accuracy and efficiency of subsequent image processing;

[0880] 2. Image Comparison and Difference Analysis

[0881] Comprehensive Comparison: Use advanced image processing and comparison techniques such as image registration, feature extraction and matching to comprehensively compare the patient's data model with the standard lung model;

[0882] Abnormality identification: Through meticulous analysis, identify data in the patient model that do not conform to normal lung tissue, such as high-density shadows appearing in the lungs (which may indicate areas of inflammation or infection), abnormalities in bronchial structure, etc.

[0883] Threshold setting and determination: Set a certain threshold or standard, and when the difference between the patient model and the standard model exceeds this threshold, it is determined to be abnormal data;

[0884] 3. Preliminary screening of abnormal data

[0885] Exclusion of false positives: Preliminary screening of identified abnormal data to exclude false positives due to image artifacts, noise or individual differences, etc.

[0886] Comprehensive analysis: Comprehensive analysis of multiple medical imaging data such as X-ray imaging, CT scanning and MRI imaging to improve the accuracy and reliability of abnormal data identification;

[0887] 4. Precise positioning and labeling of abnormal data

[0888] Precise positioning: Use image processing software or tools to precisely position the screened abnormal data and determine its specific location in the human structure model, such as the left upper lobe of the lung.

[0889] Precise labeling: Precise labeling of abnormal data in the human structure model using red circles and "I" (indicating infection) symbols to ensure the accuracy and clarity of the labeling.

[0890] 5. Verification and confirmation of abnormal data

[0891] Comparative verification: Comparative verification with other medical imaging data (such as CT scan images at different time points) or clinical information to further confirm the authenticity and reliability of the abnormal data.

[0892] Expert review: Invite professional doctors or imaging experts to review and confirm to ensure the accuracy and clinical value of abnormal data identification.

[0893] 6. Recording and reporting of abnormal data

[0894] Detailed recording: Detailed recording of identified and confirmed abnormal data, including the type (such as lung infection), location (left upper lobe of the lung), size, shape, etc.

[0895] Report generation: Generate an abnormal data report including imaging data, labeling results and clinical recommendations to provide strong support for clinical decision-making and serve as an important basis for subsequent treatment and monitoring.

[0896] II. Abnormal data labeling

[0897] Combination model: Combine the recorded human structure model to accurately mark the recognized lung infection abnormal data;

[0898] Highlight: Use red circles and "I" symbols and other labeling methods to make abnormal data stand out in the model, making it easier for doctors to analyze and handle later;

[0899] Three, diagnosis information acquisition and import

[0900] Automatic search: Automatically search for diagnosis information related to lung infection marker sites, including patient medical history, previous examination reports (such as previous CT scan results) or professional doctor's diagnosis;

[0901] Information import: If there is relevant diagnosis information (such as previous diagnosis of pneumonia), import it into the human structure model and associate it with the abnormal data marker to provide strong support for clinical decision-making;

[0902] Reminders: If there is no relevant diagnosis information, issue a reminder in the model to prompt the image recorder or relevant medical staff to conduct further examination (such as sputum culture) or improve the diagnosis information;

[0903] Four, abnormal data comparison and analysis

[0904] Multiple examination comparison: Provide the function of selecting and comparing abnormal data in personal structure diagrams at multiple examinations (such as initial visit and review after treatment) or different time points;

[0905] Trend analysis: Through comparison and analysis, observe the change trend of lung infection area (such as shadow reduction indicating effective treatment), further evaluate the clinical significance of abnormal data, and provide important basis for early diagnosis and treatment of diseases.

[0906] A specific embodiment of the lung infection patient in the method of identifying and marking abnormal data in the human structure diagram:

[0907] One, efficient abnormal data correlation search

[0908] Step overview: Use the lung infection abnormal data (such as high-density shadow area) accurately marked in the human structure model as the core search keyword to automatically and comprehensively search the patient's medical history, previous examination reports and professional doctor's diagnosis:

[0909] Detailed implementation:

[0910] 1. Set the search keyword:

[0911] In the human structure model, the high-density shadow area of the lung has been accurately marked as abnormal data and used as a keyword or index for retrieval;

[0912] 2. Comprehensive retrieval:

[0913] The system automatically retrieves all diagnostic information related to the abnormal data, including but not limited to:

[0914] Medical history records: the patient's chief complaint, history of present illness (such as symptoms of fever, cough, sputum, etc.), and past history (such as underlying diseases, history of allergies, etc.);

[0915] Past examination reports: such as previous X-ray chest radiography, CT scan results, etc., especially reports related to lung shadows;

[0916] Professional doctors' diagnosis opinions: such as initial diagnosis of pneumonia, tuberculosis, etc.;

[0917] II. Accurate diagnosis information import and association

[0918] Step overview: If closely related diagnostic information is retrieved for lung infection abnormal data, use medical information system technology to accurately, completely, and timely import these information into the human structure model, and form a direct association with the abnormal data mark;

[0919] Detailed implementation:

[0920] 1. Information import:

[0921] The retrieved CT scan report of the patient shows "left upper lobe inflammation", which is accurately imported into the human structure model and associated with the high-density shadow area of the lung;

[0922] 2. Direct association:

[0923] When viewing the high-density shadow area of the lung, the user can simultaneously see the diagnostic information of "left upper lobe inflammation", providing direct support for clinical decision-making;

[0924] III. Intelligent missing information reminder and supplement

[0925] Step overview: If no directly related diagnostic information is found for lung infection abnormal data during the retrieval process, the system will issue a prominent reminder in the human structure model and guide medical personnel to conduct further examination or supplement diagnostic information;

[0926] Detailed implementation:

[0927] 1. Missing information reminder:

[0928] The system finds that there is no pathological diagnosis information directly related to the high-density shadow area of the lung, so it issues a high-light display and flashing prompt in the model;

[0929] 2. Further examination guidance:

[0930] The reminder signal contains specific instructions, suggesting medical staff to perform sputum culture, bronchoscopy, etc., to obtain more diagnostic information;

[0931] 3. Information supplement entry:

[0932] Medical staff quickly supplement new diagnostic information through an intuitive and easy-to-use interface, such as sputum culture results showing "Streptococcus pneumoniae positive", which is timely associated with the lung high-density shadow area.

[0933] Four, comprehensive image data recording and tracking

[0934] Step overview: All image data related to lung infection abnormalities are established in a one-to-one correspondence relationship in the human structure model, and the changes in image data are recorded;

[0935] Detailed implementation:

[0936] 1. Image data association:

[0937] Body surface images (such as chest X-rays), X-ray images (such as chest X-rays), CT scans, MRI images, etc. are in a one-to-one correspondence with the lung high-density shadow area;

[0938] 2. Data change tracking:

[0939] The system records and tracks changes in these image data, especially the follow-up CT scan after treatment showing that the shadow has shrunk, indicating that the treatment is effective.

[0940] Five, intelligent recommended diagnosis system construction and optimization

[0941] Step overview: Based on abnormal data, imported diagnostic information, and pathological diagnosis results, build and optimize the intelligent recommended diagnosis system to provide personalized and precise diagnostic recommendations for patients;

[0942] Detailed implementation:

[0943] 1. System construction:

[0944] Combined with lung high-density shadows, sputum culture results (Streptococcus pneumoniae positive), and patient history, the intelligent recommended diagnosis system initially judges "pneumonia caused by Streptococcus pneumoniae";

[0945] 1. System optimization:

[0946] The system continuously learns and integrates more data, constantly optimizes the diagnosis algorithm, and improves the accuracy and timeliness of diagnosis;

[0947] Six, model continuous iteration and optimization

[0948] Step overview: By continuously collecting subsequent medical imaging data of patients, pathological diagnosis results, regularly updating the model, ensuring that the model is highly consistent with the patient's real human anatomy and physiological state.

[0949] Detailed implementation:

[0950] 1. Data collection and update:

[0951] During the treatment of patients, regular lung CT scans are performed, and new imaging data is timely integrated into the human structure model.

[0952] 2. Model optimization:

[0953] According to the new image data and diagnosis information, the model is finely adjusted and optimized to ensure that the model reflects the latest physiological state of the patient, and provides continuous support for diagnosis and treatment decisions.

[0954] A specific embodiment of a system that combines clinical information for image analysis and diagnosis with a human structure model: taking the treatment of a patient with lung infection as an example

[0955] I. History information and model association

[0956] Present history record and association

[0957] Symptom characteristics: The patient complained of fever, cough, and yellowish purulent sputum, accompanied by chest tightness and shortness of breath;

[0958] Time of onset and progression: The symptoms have lasted for a week, initially with mild cough, then gradually worsening with high fever;

[0959] Treatment history: The patient took antibiotics and fever-reducing drugs at home, but the symptoms did not improve significantly;

[0960] Information association: The above symptoms, time of onset, and treatment history are associated with the lung region in the human structure model, and the affected parts are marked;

[0961] Collection and marking of past history

[0962] Underlying diseases: hypertension, diabetes;

[0963] Operation / trauma history: none;

[0964] Allergy history: allergic to penicillin;

[0965] History marking: Mark the location of hypertension, diabetes-related history information in the model, and mark the penicillin allergy warning;

[0966] II. Physical Examination Results and Model Verification

[0967] Vital Signs Analysis and Correlation

[0968] Temperature: 38.5°C;

[0969] Model Verification: In the human structure model, verify the consistency of pulmonary imaging findings (such as lung shadows shown by CT) and clinical signs such as high fever;

[0970] Specialized Sign Evaluation and Matching

[0971] Pulmonary Auscultation: Coarse breath sounds can be heard in both lungs, and wet rales can be heard;

[0972] Accurate Position Matching: Through accurate position matching in the model, ensure that the lung wet rales match the lung inflammation area in the CT image;

[0973] III. Laboratory Test Results and Model Analysis

[0974] Blood Index Auxiliary Judgment

[0975] White Blood Cell Count: 12,000 / μL (elevated), indicating the presence of infection;

[0976] C-reactive Protein: 50 mg / L (elevated), indicating active inflammatory response;

[0977] Abnormal Index Labeling: In the human structure model, label the lung area corresponding to the white blood cell count and C-reactive protein abnormal index;

[0978] Body Fluid Test Result Update

[0979] Sputum Culture: Streptococcus pneumoniae was cultured;

[0980] Model Information Update: Update the sputum culture results in the model to clearly indicate Streptococcus pneumoniae infection information;

[0981] IV. Dynamic Changes in Clinical Manifestations and Model Updates

[0982] Symptom and Image Timeliness Matching

[0983] Symptom Changes: After antibiotic treatment, the patient's cough and sputum symptoms have decreased, and the body temperature has returned to normal;

[0984] Image Data Update: Re-examination CT shows that lung shadows have significantly absorbed;

[0985] Model Update: Update the image data in real time in the human structure model to reflect the improvement of lung inflammation;

[0986] Treatment Effectiveness Evaluation and Record

[0987] Pre-treatment image: Lung CT shows large patchy shadows;

[0988] Post-treatment image: Shadows significantly reduced;

[0989] Treatment plan adjustment: According to the treatment effect, adjust the type and dosage of antibiotics, and record the changes during treatment in the model.

[0990] Five, clinical background factors and model personalization

[0991] Personalized factor analysis

[0992] Age: 65 years old;

[0993] Gender: male;

[0994] Occupation: retired teacher;

[0995] Habits: 30 years of smoking history;

[0996] Epidemiological history: No recent travel history;

[0997] Model impact considerations: Consider the impact of age, gender, smoking history, etc. on the development of lung inflammation in the human body structure model;

[0998] Disease differentiation and model display

[0999] Integrated clinical information: Combine patient symptoms, signs, laboratory tests and imaging data to narrow the disease differential diagnosis range to bacterial pneumonia;

[1000] Model visual display: Visual display of lung inflammation area and changes before and after treatment through the model to assist in analysis;

[1001] Six, comprehensive analysis and model-assisted decision-making

[1002] Comprehensive analysis and diagnosis

[1003] Identify the cause: According to the comprehensive information, the diagnosis is bacterial pneumonia caused by Streptococcus pneumoniae;

[1004] Treatment plan: Develop a targeted antibiotic treatment plan, taking into account the patient's allergy history to avoid using penicillin drugs;

[1005] Model-assisted decision-making

[1006] Visual support: Provide visual support for lung inflammation areas through the model;

[1007] Precise positioning information: Provide precise positioning information for the inflammation area to guide clinical diagnosis and treatment decisions, such as puncture and drainage operations;

[1008] Seven, model storage and retrieval

[1009] Model Storage

[1010] Store the human structure model diagnosed by combining clinical information and image analysis to the hospital server;

[1011] Model Retrieval and Continuous Diagnosis and Treatment Support

[1012] In the follow-up visit of the patient, the past model is quickly queried and called through the retrieval system;

[1013] Combine new clinical information and image data to update and optimize the model, and provide strong support for the continuous diagnosis and treatment of patients.

[1014] A specific embodiment of a human structure model combined with abnormality discovery, abnormality comparison and disease probability assessment: the visit of a lung tumor patient

[1015] I. Abnormality discovery and preliminary assessment

[1016] Step summary: Use high-precision medical image data (such as CT, MRI, etc.) in the human structure model to find and identify lung tumor abnormal data through image processing and comparison technology;

[1017] Detailed implementation:

[1018] Image data acquisition:

[1019] Get high-resolution CT scan images of the patient to ensure that the image data is clear and comprehensive, covering the lung area;

[1020] Abnormal data identification:

[1021] Use image processing software to analyze the CT images in detail and identify abnormal areas that do not conform to normal lung tissue, such as lumps, nodules, etc.

[1022] Preliminary assessment:

[1023] Preliminary assessment of the identified abnormal data to determine the type (such as solid nodules, ground glass shadows, etc.), location (such as the left upper lobe of the lung), size (such as 2cm in diameter), shape (such as round, irregular), and possible clinical significance (such as suspected malignant tumor);

[1024] II. Abnormality comparison and analysis

[1025] Step summary: Compare the current lung tumor abnormal data found with the patient's past medical image data to analyze the change trend and possible development of the tumor;

[1026] Detailed implementation:

[1027] Historical data comparison:

[1028] Retrieve the patient's past lung CT image data and compare it with the current image to observe changes in tumor size, shape, and location;

[1029] In-depth analysis:

[1030] Combine the patient's medical history (such as smoking history, family tumor history, etc.), previous examination reports (such as previous CT, X-ray reports), and professional doctor's diagnosis opinions to conduct in-depth analysis on the growth rate and invasiveness of the tumor;

[1031] III. Disease probability assessment

[1032] Step overview: Based on the characteristics of lung tumors, patient clinical information, and medical image data, use medical data analysis algorithms to assess the probability of disease occurrence and possible disease types;

[1033] Detailed implementation:

[1034] Data analysis:

[1035] Input the characteristic parameters of the tumor (such as size, shape, location, density, etc.), patient clinical information (such as age, gender, smoking history, etc.) into the medical data analysis algorithm;

[1036] Probability assessment:

[1037] The algorithm outputs the probability of disease occurrence (such as the probability of lung cancer being 80%) and the possible disease type (such as non-small cell lung cancer).

[1038] Personalized factor adjustment:

[1039] Consider the patient's age (such as 65 years old, which increases the risk of malignancy), gender (such as male, which has a higher incidence of lung cancer), lifestyle habits (such as long-term smoking, which increases the risk of lung cancer), and other personalized factors to correct the disease probability and ultimately determine the risk level of lung cancer;

[1040] IV. Determination of differential needs and selection of auxiliary imaging methods

[1041] Step overview: Based on the results of disease probability assessment and clinical needs, determine whether additional imaging methods are needed for further differentiation;

[1042] Detailed implementation:

[1043] Determination of differential needs:

[1044] Given the high risk of lung cancer, it is determined that further imaging differentiation is needed to determine the nature of the tumor.

[1045] Method selection:

[1046] PET-CT examination is selected for comprehensive consideration of the probability of identification (high), cost (moderate), radiation risk (low-dose CT is acceptable), and clinical value (crucial for treatment decision-making) to evaluate the metabolic activity of the tumor and the presence of distant metastasis.

[1047] Five, new discovery and diagnosis notification

[1048] Step summary: For new abnormalities or new disease probabilities discovered through abnormal contrast and disease probability assessment, timely notify patients and medical staff;

[1049] Detailed implementation:

[1050] Patient notification:

[1051] Timely inform patients of the new discovery of lung tumors, explain in detail the clinical significance of the tumor (such as possible malignancy, the need for further examination and treatment) and possible impact (such as surgery, chemotherapy, etc.);

[1052] Medical staff notification:

[1053] Also notify relevant medical staff (such as thoracic surgeons, oncologists, etc.) so that they can adjust the patient's diagnosis and treatment plan based on the new discovery;

[1054] Six, continuous tracking and model updating

[1055] Step summary: Track patients continuously, regularly collect new medical image data, and update the human body structure model;

[1056] Detailed implementation:

[1057] Continuous tracking:

[1058] Arrange for patients to have regular lung CT or PET-CT examinations to monitor changes in tumors;

[1059] Model updating:

[1060] Integrate new image data from each examination into the human body structure model, and through iterative optimization, ensure that the model accurately reflects the patient's latest physiological state and tumor progression;

[1061] Clinical decision support:

[1062] Based on the updated model, provide doctors with intuitive visual support and accurate positioning information to assist in clinical diagnosis and treatment decisions, such as surgery planning and radiotherapy target setting.

[1063] A method for optimizing the establishment of a standard database of tissue abnormal structures, with lung tumor patient visits as a specific implementation example

[1064] 1. Multi-source data acquisition

[1065] Systematic Collection:

[1066] Collect medical imaging data of lung tumor patients from multiple medical institutions, research laboratories, and medical imaging centers, including but not limited to high-quality imaging data such as CT, MRI, PET-CT, etc.

[1067] Ensure that the collected data covers lung tumor patients of different pathological types (such as small cell lung cancer, non-small cell lung cancer), different stages (early, middle, and late), and different treatment states (untreated, in treatment, post-treatment).

[1068] Diagnosis Coverage:

[1069] All collected data has been confirmed by pathological diagnosis, molecular detection, or other methods to ensure the accuracy and reliability of the data.

[1070] 2. Data Processing and Classification

[1071] Detailed Classification:

[1072] According to tumor type (such as adenocarcinoma, squamous cell carcinoma), location (such as left upper lobe, right lower lobe), size, shape (such as solid nodule, ground glass shadow), and metabolic activity, etc. dimensions, the collected abnormal structure data is classified in detail.

[1073] Preprocessing Improvement:

[1074] Denoising and standardizing image data to improve data quality and consistency, ensuring the accuracy of subsequent analysis.

[1075] II. Abnormal Structure Recognition, Labeling, and Diagnosis Verification

[1076] 1. Image Processing and Comparison

[1077] Comprehensive Comparison:

[1078] Using advanced image processing technology, compare lung tumor imaging data with normal lung structure models to accurately identify tumor regions.

[1079] Precise Positioning and Labeling:

[1080] Precise positioning of tumors, detailed labeling of tumor type, location, size, shape, and possible clinical significance (such as invasiveness, metastasis risk).

[1081] 2. Diagnosis Data Verification

[1082] Strict Verification:

[1083] Collect and verify medical personnel's diagnosis data to ensure that tumor data in the database comes from diagnosed cases.

[1084] Intuitive Association:

[1085] In the human structure model, clearly mark the pathological diagnosis results (such as non-small cell lung cancer), molecular detection information (such as EGFR gene mutation), and form an intuitive association with the corresponding medical image data.

[1086] III. Database Construction and Management

[1087] 1. Database Design

[1088] Efficient Architecture:

[1089] Design a highly optimized database architecture containing data tables, fields, indexes, etc., to ensure efficient storage and fast retrieval of data.

[1090] Advanced System:

[1091] Use a relational database management system to handle large data volumes and high concurrency access requirements.

[1092] 2. Data Import and Storage

[1093] Complete Import:

[1094] Import lung tumor data that has been sorted, recognized, labeled, and verified by diagnosis into the database to ensure data integrity, consistency, and security.

[1095] Encrypted Backup:

[1096] Encrypt and backup data to ensure security during storage and transmission.

[1097] IV. Data Retrieval, Analysis, and Learning

[1098] 1. Efficient Retrieval Mechanism

[1099] Multi-dimensional Retrieval:

[1100] Establish a multi-dimensional retrieval mechanism based on keywords (such as "lung tumor" and "non-small cell lung cancer"), disease types, and abnormal features (such as "ground glass shadow"), enabling fast and accurate data retrieval.

[1101] Visual Interface:

[1102] Provide an intuitive and easy-to-use visual retrieval interface for doctors to efficiently browse and query lung tumor data.

[1103] 2. Data Analysis, Mining, and Learning

[1104] In-depth Analysis:

[1105] Using data mining techniques, analyze the potential correlations, abnormal patterns and development trends of lung tumor data, providing new perspectives for disease research and treatment.

[1106] Image Learning:

[1107] Learn from diagnosed lung tumor image data to improve the accuracy of imaging in specific tumor recognition, such as distinguishing between benign and malignant tumors, and assessing tumor invasiveness.

[1108] Decision Support:

[1109] Provide data analysis reports and visualizations to support doctors' decision-making, such as treatment options and surgical planning.

[1110] Five, continuous update and optimization

[1111] 1. Continuous data collection

[1112] Long-term cooperation:

[1113] Establish long-term cooperation with multiple medical institutions and research laboratories to continuously collect the latest lung tumor data and its diagnosis information, ensuring the timeliness and comprehensiveness of the database.

[1114] 2. Iterative optimization of database

[1115] Performance improvement:

[1116] According to user feedback, data analysis results and the development of medical imaging, continuously optimize the database architecture, retrieval mechanism and data analysis algorithm to improve the performance of the database.

[1117] Model update:

[1118] Regularly update the model to ensure that the lung tumor data in the model is highly consistent with the real human anatomy and physiological state, providing solid support for the diagnosis and treatment of lung tumors.

Claims

1. An optimized method for establishing a human body structural model, characterized in that, Includes the following steps: a) Basic information acquisition and preliminary modeling: Detailed information on the target individual's height, weight, sex, age, race, and any other relevant basic physiological information was obtained and recorded. Using advanced 3D modeling technology and combining the basic physiological information, a human structural model with basic and accurate human characteristics is constructed. b) Comprehensive medical imaging data collection and database establishment: The system collects a variety of high-quality medical imaging data from target individuals, including optical imaging (surface images, endoscopic images, pathological images), X-ray imaging (plain X-ray, CT), ultrasound imaging (B-mode ultrasound, color Doppler ultrasound), magnetic resonance imaging (MRI, fMRI), and nuclear medicine imaging (PET, SPECT); ensuring the comprehensiveness, high resolution, and accuracy of the imaging data, and establishing a comprehensive individual database containing this data; c) Model refinement optimization and personalized customization: Based on a comprehensive individual database, the human body structure model is deeply and meticulously optimized to ensure a high degree of consistency and accuracy between the model and the real human anatomy. Personalized models are customized based on the unique physiological characteristics of the target individual to meet the specific needs of that individual. We continuously iterate and optimize model details, and through repeated adjustments, corrections, and verifications, we continuously improve the accuracy and practicality of the model to adapt to diverse application scenarios.

2. The optimized human body structure model establishment method according to claim 1, characterized in that, It also includes the following steps: a) Matching imaging data with the model: The collected X-ray, MRI, and ultrasound imaging data or images are precisely matched with the corresponding locations in the human body structural model. b) Model structure readjustment: Based on their actual positions in the human anatomy model, the specific structures in the model, including bones, muscles, and organs, are readjusted to ensure that the anatomical structure of the model conforms to reality. The human anatomy model is then supplemented and corrected to improve its accuracy and completeness.

1. Skeletal structure adjustment: Based on collected X-ray or CT imaging data, accurately identify and adjust the skeletal morphology, density, and joint connection relationships in the human structural model to ensure the accuracy and integrity of the skeletal structure; 2. Muscle and soft tissue adjustment: Based on MRI or ultrasound imaging data, the morphology, distribution and soft tissue structure of the muscles in the model are carefully adjusted to reflect the muscle direction and soft tissue characteristics of the real human body.

3. Adjustment of organ position and morphology: Based on various medical imaging data, especially CT, MRI and nuclear medicine imaging data, the position, morphology and spatial relationship between each organ in the model are accurately determined and adjusted to ensure that the model is highly consistent with the real human anatomical structure.

4. Supplementation and Correction: During the adjustment process, any missing or erroneous structures discovered will be supplemented and corrected, including but not limited to small blood vessels and nerve branches, to improve the accuracy and completeness of the model; c) Structure data viewing function: It provides the ability to view the generated structural data in the personal structural diagram, making it easy for users to understand and analyze the detailed anatomical information of the model at any time.

3. An optimized method for establishing a human body structural model, characterized in that, This also includes the following steps for model matching and real-time updating using body surface image data from optical imaging technology: a) Collection and preparation of body surface imaging data: Overall and detailed image shooting: Gross imaging: Using high-resolution cameras or video cameras, comprehensively capture videos of the target individual's entire body and specific areas. The aim is to quickly locate key areas such as joints (shoulder, elbow, wrist, hip, knee, ankle), head, face, buttocks, back, perineum, and forearms. Ensure high image clarity, no distortion, and rich information on overall morphology and dynamics to provide a framework for subsequent detailed analysis. Detailed image capture: Based on the general image localization, more detailed video or photographic captures are taken of key areas or suspected abnormalities of the target individual. These detailed images must cover the area located in the general image. By adjusting the camera equipment to the optimal focal length, lighting conditions, and angle, detailed features such as skin texture, pigmentation, scars, and lumps are clearly visible, providing accurate data for in-depth analysis. Image organization and classification: The captured general and detailed image data (including photos and videos) were systematically categorized and organized according to shooting time, shooting location, and purpose. Video editing software was used to edit and annotate the videos to ensure efficient location of general images in the human anatomy diagram. Then, detailed images were used to delve into specific details, laying a solid foundation for subsequent model matching and application. b) Exactly match data: Data matching: Rapid localization: First, rapid localization is performed on the human anatomy diagram using a general image; Precise matching: Subsequently, the collected detailed images (photos or video frames) are precisely matched with the positioning area. Advanced image processing software or tools are used to perform fine adjustments such as scaling, rotation, and translation on the images to ensure perfect spatial alignment between the detailed images and the model, achieving a seamless transition from macro to micro. For video data, synchronous matching along the timeline also needs to be considered. Pay attention to detailed features: During the matching process, special attention should be paid to subtle features such as skin texture, pigmentation, and dynamic changes in the detailed images to ensure that these key details are accurately and realistically reflected in the model, providing strong support for the fine-tuning of the model. c) Dynamically update the structure diagram: Structure diagram updated: Based on newly matched body surface imaging data (including photographs and videos), especially the precise information provided by detailed images, the human anatomy diagram is regenerated and updated. Information from both gross and detailed images is seamlessly integrated into the model, comprehensively and deeply reflecting the target individual's latest physiological state, dynamic changes, and information. Anomaly labeling and updating: During the update process, any abnormalities or changes found in detailed images (such as skin lesions, changes in masses, etc.) must be accurately labeled and updated in the model in a timely manner. For video data, the time point and process of abnormal changes also need to be recorded. This not only ensures the timeliness and accuracy of the model, but also provides more precise and comprehensive support for clinical decision-making and disease monitoring. At the same time, the dynamic display of video data provides doctors with a more intuitive understanding and analytical basis.

4. An optimized method for establishing a human body structural model, characterized in that, It also includes a method for matching and updating human structural models in real time using endoscopic images from optical imaging technology, characterized by the following steps: a) Endoscopic image acquisition and preparation stage: Comprehensive and systematic collection: Systematically and comprehensively collect endoscopic images and video data of the target individual, covering different shooting times, locations, diagnostic needs, and endoscopic pathway records, to ensure that the image data has high definition, accuracy, and completeness; Organized classification and organization: The collected endoscopic images and video data are classified and organized in an orderly manner according to classification criteria such as shooting time, shooting location, diagnostic needs, and endoscopic path, providing a solid foundation for efficient matching and fusion in the future; b) Endoscopic image recognition and preliminary matching stage: Automatic identification of key features: Utilizing advanced image recognition technology, key features in endoscopic images are automatically identified, including but not limited to the lesser curvature of the stomach, greater curvature of the stomach, tracheal bifurcation, tracheal carina, pylorus, and the surrounding environment and direction of the endoscopic path, in order to improve the accuracy and efficiency of matching. Endoscopic path matching and fusion: Specifically for endoscopic path recording, by identifying the movement trajectory of the endoscope and the surrounding environment, it is accurately matched and fused with the human anatomy diagram. Specifically, it combines the structure of human organs, the distribution and course of the digestive tract, the course of the trachea and bronchi in the human anatomy diagram, as well as the movement trajectory of the endoscope and the course of human structures, to determine its specific location in the human anatomy diagram. c) Model loading and initial matching stage: Model loading: Open the human body structure model built based on the basic physiological information of the target individual, ensure that the model has basic accuracy, and support subsequent fine-tuning; Preliminary matching and panoramic mapping: The identified image and video data are initially matched with the corresponding positions in the human body structure model. Through operations such as image scaling, rotation, and translation, the approximate spatial alignment is achieved, and the image is accurately mapped to the model position. d) Precise matching and verification stage: Optimize matching results: Based on the initial matching, image registration technology is used to further optimize the matching results by calculating the similarity or difference between the endoscopic matching image and the model, so as to ensure that the details completely match the actual human body structure. Panoramic image stitching and mapping: Using image stitching technology, the captured endoscopic images are stitched into a seamless panoramic image and accurately mapped to the corresponding position in the human body structure model, realizing a real-view function similar to navigation. Careful verification and adjustment: Carefully verify the matching results using visualization tools or software. If mismatches or large errors are found, return to the previous step for adjustment until a satisfactory fusion effect is achieved. e) Model structure update and optimization stage: Refined adjustments and optimizations: Based on the newly matched endoscopic panoramic image data, the corresponding areas in the human structural model are refined and optimized, with particular attention paid to the detailed features displayed in the endoscopic images, such as gastric mucosal texture, lesion morphology, and blood vessel distribution, to ensure that the model's anatomical structure conforms to the real situation. Necessary additions and corrections: Make necessary additions and corrections to the model to improve its accuracy and completeness in reflecting the structures seen by endoscopy, including the addition of structures such as small blood vessels and nerve branches; f) Dynamic update and structured data integration phase: Save as a whole or in layers: Save the updated endoscopic panoramic image and human body structure model as a whole or in layers for easy viewing, analysis and quick retrieval; 3D Linkage and Real-time Updates: Provides an intuitive and user-friendly structural data viewing function, allowing users to simultaneously view the current operation display screen and the human anatomy model display screen on different screens, achieving 3D linkage and real-time updates of the structural model. It pays particular attention to detailed information about structures seen in the endoscopic panoramic view, providing strong support for clinical decision-making and disease monitoring. Furthermore, through precise fusion of the panoramic view and the model, it achieves a function similar to real-world navigation, providing intuitive visual guidance for surgical procedures.

5. An optimized method for establishing a human body structural model, characterized in that, It also includes the step of using pathological images in optical imaging technology for model matching and real-time updating, which includes the following steps: I. Accurate Collection and Detailed Labeling of Pathological Specimens Location confirmation and photography: When retrieving pathology specimens, medical personnel must use a high-resolution camera or video camera to photograph the following information: A single image (or a video) showing the specific location and appearance of the specimen inside the human body, ensuring that the image is clear, undistorted, and accurately reflects the specimen's shape, color, size, and location; A single image (or a video clip) records the empty spaces after the specimen is removed, ensuring both image clarity and accuracy. Detailed labeling: Label the specimen, including the collection site, possible diagnostic clues, and specific anatomical location; and use directional terms and distance markers to further refine the specimen's location to ensure accurate subsequent identification; II. Fine sampling and homogeneous labeling of microscopic specimens Sampling procedure: When performing slide sampling, medical staff must make clear and consistent markings on the slide or in the slide record. These markings must strictly correspond to the markings on the gross photograph (or video) to ensure the accuracy of the sampling location. Double-check: After taking the sample, you need to double-check the consistency between the markings on the slide and the general photograph (or video) to ensure that there are no errors; III. Precise Positioning of Microscopic Specimen Collection Sites in Structural Models Location identification: Utilizing advanced image processing technology, the specific location of the extracted specimen photograph (or video frame) is identified, combined with detailed markings made by medical staff; Model matching: By referring to a pre-established high-precision human body structure model, the precise location of the microscopic specimen sampling site in the structure model is accurately determined through comparison and registration techniques; Clear labeling: Using advanced visualization tools such as 3D modeling software, the sampling locations are clearly marked in the human body structure model to ensure the accuracy of subsequent operations; IV. Detailed Recording and Precise Matching of Pathological Microscopic Observation Results Observation record: Carefully perform pathological microscopic observation and record the observation results in detail, including cell morphology, tissue structure, abnormal changes, etc. Model matching and validation: Accurately match the pathological microscopic observation results with the human structural model. Through comparison and validation, ensure that the observation results are completely consistent with the sampling locations in the model. When matching video data, the synchronization on the timeline also needs to be considered. Consistency confirmation: During the matching process, it is necessary to repeatedly check and verify the consistency between the observation results and the model, especially the dynamic changes in the video data, to ensure that accurate basis is provided for subsequent clinical decision-making and disease monitoring.

6. The optimized human body structure model establishment method according to any one of claims 1 to 5, characterized in that, It also includes the following steps: a) Model storage and retrieval: The established personal human anatomy model and its related medical imaging data will be stored on the hospital server or the national medical server. When a patient seeks medical attention, the system searches for previously established human anatomy models of the patient. b) Model retrieval and updating: If a previous model exists, it will be retrieved and updated and optimized in conjunction with the patient's new medical imaging data from this visit. If no prior model exists, a new human body structure model shall be established based on the medical imaging data of this visit in accordance with the method described in claim 1. c) Image data improvement and model iteration: We continuously collect subsequent medical imaging data from patients and regularly iterate and update the model to ensure that the model remains highly consistent with the patient's real human anatomy. Through continuous model optimization and improvement of imaging data, the accuracy and practicality of the model are enhanced, providing more precise support for patient diagnosis and treatment.

7. A method for identifying and marking abnormal data in human anatomy diagrams, characterized in that, Includes the following steps: a) Anomaly data identification: Using advanced image processing and comparison technologies, a comprehensive comparison is made between individual data models and group standard models; Through meticulous analysis, data in individual models that do not conform to the original tissues and organs were identified. These abnormal data may manifest as significant differences in structural morphology, density, or location (e.g., bronchial structures being replaced by white shadows, or high-density shadows appearing in the fat layer). b) Abnormal data identification: By combining the already entered human body structure model, the identified abnormal data is accurately marked to ensure the accuracy and clarity of the marking; By using different colors, symbols, or annotation methods, abnormal data can be highlighted in the model, making it easier for subsequent analysis and processing. c) Acquisition and import of diagnostic information: Automatically retrieve diagnostic information related to the abnormal data markers. This information may come from the patient's medical history, previous examination reports, or the diagnostic opinions of professional doctors. If relevant diagnostic information exists, it will be imported into the human structural model and associated with abnormal data markers to provide strong support for clinical decision-making. If no relevant diagnostic information is available, a reminder will be issued in the model, prompting the imaging personnel or relevant medical staff to conduct further examinations of the area or to refine the diagnostic information. d) Comparison and analysis of abnormal data: It provides the function of selecting, viewing and comparing abnormal data from multiple examinations or different time points in the personal anatomy chart, making it easier for doctors to observe the changing trends of abnormal data; By comparing and analyzing the data, we can further assess the clinical significance of the abnormal data and provide important evidence for the early diagnosis and treatment of diseases.

8. The diagnostic information acquisition and import method in a method for identifying and marking abnormal data in a human anatomy diagram according to claim 7, characterized in that, Includes the following steps: a) Efficient abnormal data association retrieval: Using precisely labeled abnormal data in the human body structure model as core search keywords or indexes, the system can automatically and comprehensively retrieve patients' medical history records, previous examination reports, and professional doctors' diagnostic opinions. The search scope should broadly cover all diagnostic information related to the abnormal data, including but not limited to diagnostic conclusions, treatment history, disease progression, and pathological diagnoses. b) Importing and linking precise diagnostic information: If diagnostic information closely related to the abnormal data is retrieved, advanced medical information system technology should be used to ensure that this information is accurately, completely and promptly imported into the human structural model. Imported diagnostic information needs to be intuitively and closely linked with abnormal data markers so that users can simultaneously and conveniently obtain relevant diagnostic information when viewing abnormal data, providing comprehensive and intuitive support for clinical decision-making; c) Intelligent missing information reminders and supplements: If no diagnostic information directly related to the abnormal data marking area is found during the search process, the system should automatically issue a prominent and clear reminder signal in the human body structure model, such as highlighting or flashing prompts. The alert signal should include specific instructions to guide the imaging personnel or relevant medical staff to conduct further detailed examinations of the abnormal area or to supplement necessary diagnostic information, such as repeating sampling or adding other relevant examinations. The system provides an intuitive and easy-to-use interface or tools to facilitate medical staff in quickly and accurately entering new diagnostic information and ensure that this information is closely linked to abnormal data markers; d) Comprehensive image data recording and tracking: For all imaging data related to abnormal data, including but not limited to surface images, endoscopic images, X-ray imaging, ultrasound imaging, magnetic resonance imaging, and nuclear medicine imaging, a one-to-one correspondence should be established in the human structural model. The system should record and track changes in these imaging data, especially those after a definitive diagnosis has been made through pathological diagnosis or molecular testing, to provide rich and accurate data support for subsequent intelligent diagnostic recommendations. e) Construction and optimization of intelligent recommendation and diagnostic system: Based on abnormal data in human structural models, imported diagnostic information, and associated pathological diagnoses and molecular detection results, an intelligent recommendation diagnostic system is constructed and continuously optimized. The system should be able to automatically analyze, integrate, and deeply mine various types of data to provide patients with personalized, accurate, and timely diagnostic suggestions, while assisting doctors in making more efficient and accurate treatment decisions. f) Continuous model iteration and optimization: By continuously collecting patients' subsequent medical imaging data, pathological diagnoses and molecular test results, and regularly iterating and updating the model, we can ensure that the model is highly consistent with the patient's real human anatomy and physiological state. We continuously optimize the diagnostic information acquisition and import functions in the model to improve its accuracy, practicality, and timeliness, providing more precise, comprehensive, and personalized support for patient diagnosis and treatment.

9. A method for combining clinical information with image analysis and diagnosis and integrating it with a human structural model, characterized in that, Includes the following steps: a) Obtain and integrate medical history information with human structural models: Record the patient's current medical history in detail, including symptom characteristics, onset time and progression, and treatment process, and associate this information with the corresponding parts in the human anatomy model; Collect the patient's medical history, including underlying diseases, surgical / traumatic history and allergy history, to provide background information for image analysis, and mark the location of relevant medical history information in the model; b) Validation of the model by combining physical examination results: Analyze the patient's vital signs, such as body temperature and blood pressure, and correlate them with imaging findings. Validate the consistency between imaging findings and clinical signs in a human structural model. Assess specialist signs and ensure that imaging findings match clinical signs through precise location matching in the model, thereby improving diagnostic accuracy; c) Integration of laboratory test results with model analysis: Using data such as inflammatory markers, tumor markers, and liver and kidney function from blood tests, we can help determine the nature of lesions seen in imaging and mark the corresponding locations of abnormal indicators in human structural models. By combining the results of body fluid examinations, such as routine, biochemical, and culture results of pleural effusion or cerebrospinal fluid, the cause of the lesion can be further clarified, and relevant information can be updated in the model. d) Assessing dynamic changes in clinical manifestations and model updates: Matching the timeliness of symptoms and images ensures that the diagnosis is consistent with the disease progression, and the imaging data is updated in real time in the human structural model to reflect the disease progression (such as improvement of cough and sputum and dissipation of lung CT shadows). Compare imaging data before and after treatment to assess treatment effectiveness, adjust treatment plans in a timely manner, and record changes during the treatment process in the model; e) Consider other clinical background factors and model personalization: Analyze patients’ age, gender, occupation, lifestyle and epidemiological history to provide personalized basis for imaging diagnosis, and consider the impact of these factors on the human structural model. By integrating all clinical information, the scope of disease differentiation is narrowed, and the accuracy and specificity of diagnosis are improved. The model provides intuitive display and auxiliary analysis. f) Comprehensive analysis and diagnosis, and enhancement of model-assisted decision-making: Comprehensive Analysis and Diagnosis: Based on a comprehensive integration of clinical information (including detailed medical history, meticulous physical examination, and accurate laboratory test results) and multi-dimensional, high-resolution imaging data (including optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging, and nuclear medicine imaging), combined with a highly accurate human anatomy model, in-depth comprehensive analysis is performed. By meticulously comparing normal and abnormal tissue structures and accurately assessing the extent and severity of lesions, the risk of misdiagnosis is effectively avoided, ensuring a high degree of accuracy and reliability in diagnostic results. Enhanced Model-Assisted Decision-Making: Utilizing a high-precision human anatomy model, the model intuitively and three-dimensionally displays the lesion site and its surrounding complex anatomical structures, including crucial information such as important blood vessels and nerves, providing doctors with unprecedented comprehensive visual support. The model not only helps doctors quickly locate the lesion area but also assists them in understanding the spatial relationship between the lesion and surrounding tissues, thereby making more accurate and safer clinical decisions. Furthermore, the model supports dynamic updates and real-time data analysis, ensuring that doctors can access the latest and most comprehensive patient information at any time, further improving diagnostic efficiency and effectiveness. g) Model storage and retrieval facilitate subsequent tracking and analysis: The human anatomy model, which combines clinical information and image analysis for diagnosis, will be stored on a hospital server or a national medical server. During subsequent patient visits, the retrieval system can quickly search and retrieve previous models, and update and optimize them by combining new clinical information and imaging data, providing strong support for the patient's continued diagnosis and treatment.

10. A human structural model construction system that deeply integrates definitive diagnosis and follow-up strategies, characterized in that, Includes the following modules: I. Define the diagnostic module 1. Conduct a comprehensive inspection of submodules. Image acquisition unit: responsible for conducting comprehensive examinations of the human body using a variety of high-quality medical imaging data, including but not limited to optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging, and nuclear medicine imaging.

2. Model Building Submodule 3D Modeling Unit: Based on the above medical imaging data and combined with advanced 3D modeling technology, construct a human body structure model with basic and accurate human features; 3. Abnormal Data Matching and Identification Submodule Image comparison unit: accurately matches and marks abnormal data in the human body structure model, and identifies data that does not match the original tissues and organs through image comparison and difference analysis technology; 4. Comprehensive Analysis and Diagnosis Submodule Medical history and examination integration unit: Combining the patient's medical history records, previous examination reports and professional doctors' diagnostic opinions, the abnormal data are comprehensively analyzed and diagnosed to clarify the nature and extent of the disease; II. Review and Inspection Module 1. Review Plan Development Submodule Disease nature assessment unit: Based on a clear diagnosis, develop a targeted follow-up plan according to the nature and severity of the disease; Technology Selection Unit: The follow-up plan prioritizes the use of economical, convenient medical imaging technologies with low radiation exposure to patients, such as ultrasound imaging technologies like B-mode.

2. Review and Data Collection Submodule Regular follow-up unit: In accordance with the follow-up plan, patients are regularly followed up and new medical imaging data are collected; 3. Data Comparison and Analysis Submodule Trend Observation Unit: This unit compares and analyzes new medical imaging data with existing human anatomy models to observe disease trends, evaluate treatment effectiveness, and adjust treatment plans in a timely manner. III. Model Optimization and Update Module 1. Refined optimization and personalized customization submodule Image fusion unit: Combines new medical imaging data and review results to perform refined optimization and personalized customization of human structural models; 2. Information Update Submodule Continuous update unit: continuously updates the images and diagnostic information in the model to ensure that the model remains highly consistent with the patient's real human anatomy and physiological state; 3. Data Comparison and Viewing Submodule Trend Analysis Unit: Provides the ability to select, view, and compare abnormal data from multiple examinations or different time points within the personal structural chart, facilitating doctors' observation of disease trends and providing strong support for clinical decision-making.

11. A method for combining ultrasound recording with a human structural model, characterized by comprising the following steps:

1. Model retrieval: Before performing an ultrasound examination, a high-precision human anatomy model of the target individual is retrieved from the hospital server or the national medical server. Model localization: Pre-determine the target site or area to be examined by ultrasound in the model; 2. Ultrasound data acquisition and preliminary matching Data Acquisition: Ultrasonic data from the target area is acquired in real time using ultrasonic reflection imaging technology, ensuring data clarity and accuracy. During acquisition, the position, angle, and frequency of the ultrasonic probe are adjusted according to the target area to obtain the best imaging results. Preliminary matching: Using medical image processing software or tools, the real-time acquired ultrasound data is initially matched with the corresponding positions in the human body structure model. Approximate spatial alignment is achieved using image scaling, rotation, and translation operations.

3. Real-time matching and data fusion Refined matching: Building upon the initial matching, advanced image registration technology is used to further optimize the matching results by calculating the similarity or difference between the ultrasound data and the model. This ensures that every detail in the ultrasound data precisely matches the actual human anatomy. Data fusion: Optimized and matched ultrasound data are gradually fused into the human body structural model. The fusion effect is observed in real time using visualization tools or software to ensure the accurate placement of data within the model.

4. Gradual Infilling and Model Update Gradual filling: As the ultrasound examination progresses, newly acquired ultrasound data is continuously added to the human structural model. Data filling of the entire target area is completed gradually, either layered or regionally. Model Update: During the data population process, the model is updated and optimized as necessary. Based on the ultrasound examination results, the structural details in the model are adjusted to ensure that the model maintains a high degree of consistency with the actual human anatomy.

5. Model viewing and dynamic data analysis Real-time viewing: Provides the ability to view filled ultrasound data in real time within a personal anatomy diagram. Users can intuitively understand the ultrasound imaging information of the target area at any time through the anatomy diagram; Dynamic Analysis: Supports users in dynamically analyzing ultrasound data after filling. By comparing ultrasound data at different time points or with different number of examinations, the changing trend of lesions can be observed, providing strong support for clinical decision-making.

6. Result saving and subsequent applications Results saving: Save the human body structure model filled with ultrasound data as a whole or layered file for easy viewing, analysis and quick retrieval and reference later; Subsequent applications: During subsequent patient visits, the model can be quickly retrieved and accessed through the search system, and updated and optimized by combining new clinical information and imaging data, providing strong support for the patient's ongoing diagnosis and treatment.

12. A method for applying a human structural model that combines anomaly detection, anomaly comparison, and disease probability assessment, characterized in that, Includes the following steps: a) Anomaly detection and preliminary assessment: Using high-precision medical imaging data from human structural models, image processing and comparison techniques are employed to discover and identify abnormal data that do not conform to the structure of normal tissues and organs. A preliminary assessment of the identified abnormal data is conducted, including the type, location, size, morphology, and possible clinical significance of the abnormality. b) Anomaly Comparison and Analysis: The abnormal data discovered now is compared with the patient's previous medical imaging data to analyze the changing trends and possible development of the abnormal data; combined with the patient's medical history, previous examination reports and the diagnostic opinions of professional doctors, the abnormal data is analyzed and interpreted in depth. c) Disease probability assessment: Based on the characteristics of abnormal data, patients' clinical information, and medical imaging data, advanced medical data analysis algorithms are used to assess the probability of disease occurrence and possible disease types. Taking into account individual factors such as the patient's age, gender, and lifestyle habits, the probability of disease is further revised and adjusted; d) Identification needs and selection of auxiliary imaging methods: Based on the disease probability assessment results and clinical needs, determine whether additional imaging methods are needed for further differentiation; Taking into account the probability of identification, cost, radiation risk and clinical value, select appropriate auxiliary imaging methods, such as CT, MRI, PET-CT, etc. e) Notification of new findings and diagnoses: For any new abnormalities or new disease probabilities discovered through abnormal comparison and disease probability assessment, the original patient should be notified promptly, and the clinical significance and potential impact of the new findings should be explained in detail. At the same time, relevant medical staff will be notified so that they can make necessary revisions and adjustments to the patient's diagnosis and treatment plan based on the new findings; f) Continuous tracking and model updates: Patients are continuously tracked, new medical imaging data is collected regularly, and updated into the human structural model; By combining new clinical information and imaging data, the human anatomy model is iteratively optimized to ensure that the model is highly consistent with the patient's real human anatomy and physiological state.

13. A method for optimizing the establishment of a standard database of abnormal tissue structures, the core objective of which is to aggregate and enrich abnormal and diagnosed data in human structural models, and simultaneously, to continuously improve the accuracy of imaging in diagnosing abnormal structures by deeply learning the abnormal manifestations of various medical imaging data corresponding to the confirmed diagnoses. This method is detailed in the following steps:

1. Data collection and organization Multi-source data acquisition: Systematic collection: Extensive collection of abnormal human body structure data from various medical institutions, research laboratories and medical imaging centers, covering high-quality medical imaging data such as optical imaging (surface images, endoscopic images), X-ray imaging (X-ray plain film, CT), ultrasound imaging (B-mode ultrasound, color Doppler ultrasound), magnetic resonance imaging (MRI, fMRI) and nuclear medicine imaging (PET, SPECT). Diagnostic coverage: Ensure that the collected data comprehensively covers morphological, functional, and metabolic abnormalities caused by various diseases that have been clearly diagnosed by pathological diagnosis, molecular testing, or other methods, and guarantee the comprehensiveness, high resolution, and accuracy of the data; Data organization and classification: Fine classification: Based on dimensions such as disease type, abnormal morphology, functional impact, and metabolic changes, the collected abnormal structure data is finely classified and organized to support subsequent efficient retrieval and in-depth analysis; Preprocessing enhancement: By using preprocessing techniques such as denoising and standardization, data quality and consistency are improved to ensure the accurate application of data in the model; 2. Abnormal structure identification, annotation, and diagnostic verification Image processing and comparison: Comprehensive comparison: Utilizing advanced image processing and comparison technologies, abnormal structural data is comprehensively compared with normal human body structural models to accurately identify abnormal data that does not match normal tissues and organs. Precise localization and annotation: Precisely locate abnormal data and annotate in detail the type, location, size, morphology, and possible clinical significance of the abnormality; Validation of diagnostic data: Rigorous verification: Rigorous collection and verification of diagnostic data from medical personnel to ensure that all abnormal structural data in the database originates from confirmed cases; Intuitive association: Clearly marking the results of pathological diagnoses, molecular tests, or other diagnostic methods in the human structural model and forming an intuitive association with the corresponding medical imaging data; 3. Database construction and management Database design: High-efficiency architecture: A highly optimized database architecture, including tables, fields, and indexes, is designed to ensure efficient data storage and fast retrieval; Advanced system: Advanced database management systems, such as relational databases or NoSQL databases, are used to handle the demands of large data volumes and high-concurrency access; Data import and storage: Complete import: Import the organized, identified, labeled and confirmed abnormal structure data into the database to ensure data integrity, consistency and security; Encrypted backup: Encrypt and back up data to ensure its security and reliability during storage and transmission; 4. Data retrieval, analysis, and learning Highly efficient search mechanism: Multi-dimensional retrieval: Establishes a retrieval mechanism based on keywords, disease types, abnormal features, and other dimensions to achieve rapid and accurate data retrieval; Visual interface: Provides an intuitive and easy-to-use visual retrieval interface, facilitating efficient browsing and querying of abnormal structured data; Data analysis, mining, and learning: In-depth analysis: Utilizing data mining and machine learning techniques, we conduct in-depth analysis of abnormal structure data to discover potential disease associations, abnormal patterns, and development trends; Image learning: Specifically, it learns the abnormal manifestations of various medical imaging data (including optical imaging, X-ray imaging, ultrasound imaging, magnetic resonance imaging and nuclear medicine imaging) corresponding to the diagnosis, and continuously improves the diagnostic probability of imaging in the identification of specific abnormal structures. Decision support: Provides data analysis reports and visualizations to support doctors' decision-making and promote the in-depth development of medical research; 5. Continuous updates and optimizations Continuous data collection: Long-term cooperation: Establish long-term and stable cooperative relationships with medical institutions, research laboratories, etc., to continuously collect the latest abnormal structure data and their diagnostic information, and ensure the timeliness and comprehensiveness of the database; Database iterative optimization: Performance Improvement: Based on user feedback, data analysis results, and advancements in medical imaging, we continuously optimize the database architecture, retrieval mechanism, and data analysis algorithms to improve database performance and user experience. Model updates: The model is iteratively updated regularly to ensure that the abnormal structural data in the model are highly consistent with the real human anatomy and physiological state, providing solid support for the diagnosis of medical imaging.