Intelligent image diagnosis and analysis system

Abstract

The invention discloses an intelligent image diagnosis and analysis system. The intelligent image diagnosis and analysis system comprises the following steps: step 1, acquiring and preprocessing image data; step 2, carrying out multi-modal image fusion; 3, patient feature self-adaption is carried out; 4, intelligent analysis and labeling are carried out; 5, automatic stroking and three-dimensional modeling are carried out; 6, optimizing the deep learning model; step 7, intelligent recommendation and feedback; and step 8, visualized report and cloud platform analysis are carried out. Through effective fusion and analysis of multi-modal image data and in combination with an individual feature adaptive mechanism, the system can provide more comprehensive pathological information, so that the recognition rate and accuracy of lesions are remarkably improved, and through analysis and intelligent recommendation functions of historical medical records of patients, the system can be applied to the medical treatment of the patients. The system can provide personalized examination suggestions and subsequent treatment schemes for each patient, and the personalized medical strategy improves the satisfaction degree of the patient.

Description

Technical Field

[0001] The present invention relates to the technical field of imaging diagnosis, and particularly to an intelligent imaging diagnosis and analysis system. Background Art

[0002] The background art mainly relates to the existing technologies and development trends in the field of medical imaging diagnosis. With the continuous progress of medical imaging technologies and the wide application of various imaging modalities such as CT, MRI, and ultrasound, the quantity of medical imaging data has increased sharply. Although these data contain rich pathological information, due to the complexity and diversity of images, traditional manual reading methods can no longer meet clinical needs efficiently and accurately. Therefore, intelligent imaging diagnosis systems have emerged, aiming to automatically identify and analyze images through deep learning and image processing algorithms to help doctors improve the diagnosis efficiency and accuracy.

[0003] However, there are still significant deficiencies in the existing technologies, such as:

[0004] In the existing technologies: When existing intelligent imaging diagnosis systems process images from different devices (such as CT, MRI, ultrasound, etc.), they often face the problem of data silos, resulting in ineffective integration of different modality information, which will affect doctors' comprehensive understanding and accurate diagnosis of diseases. At the same time, when the existing technologies automatically process images, they often lack sufficient consideration of individual patient differences, resulting in the accuracy of analysis results being affected. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent imaging diagnosis and analysis system to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: 1. An intelligent imaging diagnosis and analysis system, including the following steps:

[0007] Step 1: Image data acquisition and preprocessing;

[0008] Step 2: Multi-modal image fusion;

[0009] Step 3: Patient feature adaptation;

[0010] Step 4: Intelligent analysis and annotation;

[0011] Step 5: Automatic edge tracing and 3D modeling;

[0012] Step 6: Deep learning model optimization;

[0013] Step 7: Intelligent recommendation and feedback;

[0014] Step 8: Visualization report and cloud platform analysis.

[0015] Preferably, Step 1: Image data acquisition and preprocessing specifically includes the following steps:

[0016] 1. Image data collection and preprocessing:

[0017] 1.1 Use an image acquisition device to take medical images of the patient;

[0018] 1.2 Transmit the image data to the image database of the system through a secure network to ensure data encryption;

[0019] 1.3 Apply a noise removal algorithm to denoise the image;

[0020] 1.4 Use an artifact removal technique to remove artifacts in the image;

[0021] 1.5 Apply contrast-limited adaptive histogram equalization to enhance image details;

[0022] 1.6 Standardize the processed image into a unified format.

[0023] Preferably, Step 2: Multi-modal image fusion specifically includes the following steps:

[0024] 2. Multi-modal image fusion:

[0025] 2.1 After the image processing module, extract the feature points of different modal images;

[0026] 2.2 Apply an image registration algorithm to align different modal images;

[0027] 2.3 Perform weighted superposition on the registered images to generate a fused image.

[0028] Preferably, Step 3: Patient feature adaptation specifically includes the following steps:

[0029] 3. Patient feature adaptive adjustment:

[0030] 3.1 Collect the patient's historical medical records and image features;

[0031] 3.2 Analyze the historical data through machine learning algorithms to determine the optimal set of image processing parameters;

[0032] 3.3 Automatically apply these parameter sets to reprocess the current image data.

[0033] Preferably, Step 4: Intelligent analysis and annotation specifically includes the following steps:

[0034] 4. Automatic annotation and color difference quantization:

[0035] 4.1 Use a deep learning model to automatically annotate the image and identify potential lesion areas;

[0036] 4.2 In the model annotation module, perform color difference quantization and optimize the positive feedback mechanism of the annotation model; 4.3 Generate an evaluation report of the annotation results for subsequent review.

[0037] Preferably, step 5: Automatic stroke tracing and 3D modeling specifically includes the following steps:

[0038] 5 Automatic stroke tracing and 3D modeling:

[0039] 5.1 Transfer the marked area data to the automatic stroke tracing and modeling module and apply the edge detection algorithm;

[0040] 5.2 Generate a 2D contour map of this part based on the detection results;

[0041] 5.3 Use the 3D reconstruction algorithm to convert the 2D contour into a 3D model.

[0042] Preferably, step 6: Deep learning model optimization specifically includes the following steps:

[0043] 6 Disease type recognition:

[0044] 6.1 Transfer the 3D model data to the deep learning model optimization module;

[0045] 6.2 Conduct morphological feature analysis and use the pre-trained deep learning network for disease discrimination; 6.3 Generate a disease analysis report based on the discrimination results for doctors' reference.

[0046] Preferably, step 7: Intelligent recommendation and feedback specifically includes the following steps:

[0047] 7. Personalized suggestion generation:

[0048] 7.1 Automatically generate personalized subsequent examination suggestions based on the disease discrimination results;

[0049] 7.2 Allow doctors to input feedback information to update the model recommendation algorithm;

[0050] 7.3 Integrate the suggestions into the visualization report for doctors to review.

[0051] Preferably, step 8: Visualization report and cloud platform analysis specifically includes the following steps: 8. Visualization report generation and cloud data analysis platform:

[0052] 8.1 Collect all diagnostic information and transfer it to the visualization report generation module;

[0053] 8.2 Design a dynamic interactive report template that includes images, 3D models, and recommended examinations;

[0054] 8.3 Generate the final report and export it in PDF format for doctors and patients to use;

[0055] 8.4 Upload the processed case data to the cloud platform for secure storage and analysis;

[0056] 8.5 Regularly mine the cloud platform data to identify new disease patterns and trends;

[0057] 8.6 Feed the big data analysis results back into the system to further optimize the diagnostic model.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] 1. Through the effective fusion and analysis of multi-modal imaging data and the individual feature adaptive mechanism, the system can provide more comprehensive pathological information, thus significantly improving the recognition rate and accuracy of lesions and reducing the possibility of misdiagnosis and missed diagnosis;

[0060] 2. The intelligent imaging analysis system can automatically process and annotate images, reducing the workload of doctors and shortening the diagnosis time. This enables doctors to focus more on clinical decision-making and patient communication, achieving efficient medical services;

[0061] 3. Through the analysis of patients' historical medical records and the intelligent recommendation function, the system can provide personalized examination suggestions and follow-up treatment plans for each patient. This personalized medical strategy improves patient satisfaction and promotes the implementation of more precise treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] Please refer to Figure 1 , the present invention provides a technical solution:

[0065] An intelligent imaging diagnosis and analysis system, including the following steps:

[0066] Step 1: Image data acquisition and preprocessing;

[0067] Step 2: Multi-modal image fusion;

[0068] Step 3: Patient Feature Adaptation;

[0069] Step 4: Intelligent Analysis and Annotation;

[0070] Step 5: Automatic Contouring and 3D Modeling;

[0071] Step 6: Deep Learning Model Optimization;

[0072] Step 7: Intelligent Recommendation and Feedback;

[0073] Step 8: Visualization Report and Cloud Platform Analysis.

[0074] The said Step 1: Image Data Acquisition and Preprocessing specifically includes the following steps:

[0075] 1. Image Data Collection and Preprocessing:

[0076] 1.1 Use an image acquisition device to take medical images of the patient;

[0077] 1.2 Transmit the image data to the image database of the system through a secure network to ensure data encryption;

[0078] 1.3 Apply a noise removal algorithm to denoise the image;

[0079] 1.4 Use an artifact removal technique to remove artifacts in the image;

[0080] 1.5 Apply contrast-limited adaptive histogram equalization to enhance image details;

[0081] 1.6 Standardize the processed image into a unified format.

[0082] In the image data acquisition stage, first use high-resolution medical imaging devices (such as CT, MRI, ultrasound) to take images of the patient. Ensure that all devices are calibrated before acquisition to ensure image quality. The image data is transmitted to the image database of the system through a secure network to ensure that the data is encrypted during transmission to protect patient privacy.

[0083] In the data preprocessing stage, first, noise removal is performed on the collected image data using median filtering or Gaussian filtering algorithms to reduce random noise in the images. Then, specialized artifact removal techniques are used to remove artifacts caused by factors such as metal implants to ensure the authenticity and readability of the images. Next, the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied to enhance the contrast and details of the images, making the lesion areas more obvious. Finally, the processed images are standardized into a unified format (such as DICOM) for subsequent processing and analysis. The implementation of step one ensures the quality and accuracy of the image data, laying a solid foundation for subsequent analysis and diagnosis. Through efficient preprocessing and data standardization, not only can noise and artifacts be reduced, but also the visualization effect of important lesion areas can be enhanced, thereby improving the efficiency and accuracy of the entire image diagnosis system.

[0084] Step 2: Multi-modal image fusion, specifically including the following steps:

[0085] 2. Multi-modal image fusion:

[0086] 2.1 After the image processing module, extract the feature points of different modal images;

[0087] 2.2 Apply image registration algorithms to align different modal images;

[0088] 2.3 Perform weighted superposition on the registered images to generate a fused image.

[0089] In the multi-modal image fusion stage, first, extract different modal image data from the image database, such as CT, MRI, and ultrasound images. These images usually have different resolutions and imaging characteristics, so registration processing is required. Use feature point extraction algorithms (such as SIFT or ORB) to identify the overlapping areas between different images to determine their corresponding relationships. Next, apply image registration algorithms, such as rigid or non-rigid registration methods, to ensure accurate alignment of different modal images. After registration, adopt weighted superposition technology to fuse multi-modal images, usually using transparency adjustment to balance the information of different modal images. The fused image can retain the advantages of each modality, making the manifestation of the lesion area more obvious and detailed. Finally, save the fused image in a unified format for subsequent analysis and automatic annotation processing.

[0090] By effectively fusing the information of multiple different modal images, a more comprehensive and detailed perspective is provided, enhancing the ability to identify lesions. This multi-modal fusion technology enables doctors to obtain more comprehensive diagnostic evidence, thereby improving the accuracy and reliability of image diagnosis and providing more valuable support for subsequent analysis and treatment plans.

[0091] Step 3: Patient feature adaptation, which specifically includes the following steps:

[0092] 3. Adaptive adjustment of patient features:

[0093] 3.1 Collect the patient's historical medical records and imaging features;

[0094] 3.2 Analyze the historical data through machine learning algorithms to determine the optimal set of image processing parameters;

[0095] 3.3 Automatically apply these parameter sets to reprocess the current image data.

[0096] In the stage of adaptive adjustment of patient features, first collect the patient's historical medical records, including past imaging data, medical history, treatment plans, and relevant clinical information. These data will be used to establish a personalized image processing model to optimize image analysis according to the specific situation of the patient. Next, use machine learning algorithms to analyze the historical data to identify which image processing parameters (such as contrast, brightness, color saturation, etc.) can effectively improve the image quality of a specific patient. This process involves statistical analysis of a large number of historical cases to find the best parameter settings. Once the optimal set of image processing parameters is determined, the system will automatically apply these parameters to reprocess the image data of the current patient to ensure that the image can maximize the display of lesion features and improve the accuracy of diagnosis. By introducing the patient's personalized medical information and optimizing the image processing process, image analysis can more accurately reflect the unique pathological characteristics of each patient. This adaptive method not only improves the image quality and readability but also enhances the intelligence level of the entire diagnostic system, ensuring that doctors can rely on more accurate information for clinical decision-making, thereby improving the diagnosis and treatment effect of patients.

[0097] Step 4: Intelligent analysis and annotation, which specifically includes the following steps:

[0098] 4. Automatic annotation and color difference quantification:

[0099] 4.1 Use a deep learning model to automatically annotate the image and identify potential lesion areas;

[0100] 4.2 In the model annotation module, perform color difference quantification and optimize the positive feedback mechanism of the annotation model;

[0101] 4.3 Generate an evaluation report of the annotation results for subsequent review.

[0102] In the stage of automatic annotation and analysis based on deep learning, first, a deep neural network model is constructed, usually adopting the architecture of convolutional neural network (CNN) to be able to process complex image data. Then, the model is trained using the labeled training set, and the image data in the training set should contain different types of lesions and their corresponding labels to ensure that the model can learn effective features. After training, the cross-validation method is used to evaluate the performance of the model, and the hyperparameters are adjusted to optimize the accuracy and recall rate. The verified model is then applied to new image data for automatic annotation, automatically identifying and marking the lesion areas in the images. In addition, combining the concept of multi-task learning, the model can also perform image segmentation and classification simultaneously to provide more comprehensive analysis results. Finally, the results of annotation and analysis are generated into a report for clinicians to refer to and assist their decision-making.

[0103] The automatic annotation and analysis of image data are realized through deep learning technology, greatly improving the efficiency and accuracy of image interpretation. This automatic process not only reduces the workload of doctors but also decreases the possibility of human errors, providing more reliable support for clinical diagnosis. This intelligent way of image analysis promotes the progress of medical imaging and ultimately helps to improve the treatment effect of patients and the quality of medical services.

[0104] Step 5: Automatic edge tracing and 3D modeling, specifically includes the following steps:

[0105] 5 Automatic edge tracing and 3D modeling:

[0106] 5.1 Transfer the data of the annotated area into the automatic edge tracing and modeling module and apply the edge detection algorithm;

[0107] 5.2 Generate a 2D contour map of this part based on the detection results;

[0108] 5.3 Use the 3D reconstruction algorithm to convert the 2D contour into a 3D model.

[0109] In the automatic stroke tracing and 3D modeling stage, first, the annotated image data is transmitted to the automatic stroke tracing and modeling module. Then, edge detection algorithms such as the Canny algorithm are applied to extract the edge information of the annotated area. By searching for areas with sharp changes in image grayscale, this algorithm can effectively identify the contours of lesions. Once the edge detection is completed, the system generates a 2D contour map of this part based on the detection results. This process ensures the accuracy of the contours, enabling more accurate subsequent 3D modeling. Subsequently, using 3D reconstruction algorithms such as Marching Cubes, the obtained 2D contour map is converted into a complete 3D model. By analyzing the isosurfaces of the 3D volume data, this algorithm can efficiently generate a 3D surface, thus presenting the morphological characteristics of the lesion area. After completing the 3D modeling, the system can visualize the model for doctors to conduct more in-depth analysis and evaluation. Through automatic stroke tracing and 3D modeling, accurate visualization of the lesion area is achieved. This process not only enhances the depth and three-dimensional sense of image analysis, enabling doctors to more comprehensively understand the nature and scope of the lesion, but also provides an important reference basis for subsequent surgical planning and treatment decisions, thereby significantly enhancing the accuracy and effectiveness of medical services.

[0110] Step 6: Deep learning model optimization, specifically including the following steps:

[0111] 6 Disease type identification:

[0112] 6.1 Transmit the 3D model data to the deep learning model optimization module;

[0113] 6.2 Conduct morphological feature analysis and use a pre-trained deep learning network for disease discrimination;

[0114] 6.3 Generate a disease analysis report based on the discrimination results for doctors' reference.

[0115] In the deep learning model optimization stage, first, the generated 3D model data is input into the deep learning model optimization module. This module preprocesses the 3D model to ensure that the data format conforms to the input requirements, and at the same time extracts relevant morphological features, such as key parameters like volume, surface area, and shape. Next, a pre-trained deep learning network is used for disease discrimination. This process includes using models such as convolutional neural networks (CNNs) to analyze and classify the extracted morphological features. Through training, the model can identify different types of lesions and output corresponding classification results and probability distributions. Finally, based on the discrimination results of the deep learning model, the system generates a disease analysis report, which contains the disease type, possible clinical significance, and recommended follow-up examinations or treatment plans. This information will be provided for doctors' reference to assist their clinical decision-making. Through deep learning technology, accurate identification and analysis of disease types have been achieved, significantly improving the efficiency and accuracy of diagnosis. This process not only provides doctors with an objective data-based basis but also promotes the formulation of personalized medical plans, thus optimizing the treatment path for patients and ultimately improving the quality and effectiveness of overall medical services.

[0116] Step 7: Intelligent recommendation and feedback, specifically includes the following steps:

[0117] 7. Personalized recommendation generation:

[0118] 7.1 Automatically generate personalized follow-up examination recommendations based on the disease discrimination results;

[0119] 7.2 Allow doctors to input feedback information to update the model recommendation algorithm;

[0120] 7.3 Integrate the recommendations into the visualization report for doctors to review.

[0121] In the intelligent recommendation and feedback stage, the system first automatically generates personalized follow-up examination suggestions based on the disease diagnosis results. These suggestions will be based on the current diagnostic information, standard treatment guidelines, and relevant literature to ensure the scientific nature and pertinence of the recommended examinations and treatment plans. Then, the system provides a feedback input interface for doctors, allowing them to evaluate the generated suggestions and input feedback information. This feedback will be used to update the model recommendation algorithm, thereby optimizing the future recommendation process and enhancing the accuracy and practicality of the intelligent system. Finally, the personalized suggestions are integrated into the visual report to ensure that doctors can conveniently review this information when making diagnostic and treatment decisions. The report not only includes disease analysis and suggestions but also presents them in an intuitive way, enabling doctors to quickly obtain the required information. Through the intelligent recommendation and doctor feedback mechanism, personalized follow-up examination suggestions and continuous model optimization are achieved. This process enhances the pertinence and flexibility of medical decision-making, improves doctors' work efficiency, and provides more comprehensive medical services for patients. Through continuous feedback and adjustment, the system can continuously adapt to clinical practice and improve the overall medical quality.

[0122] Step 8 mentioned above: Visual report and cloud platform analysis specifically includes the following steps:

[0123] 8. Visual report generation and cloud data analysis platform:

[0124] 8.1 Collect all diagnostic information and transmit it to the visual report generation module;

[0125] 8.2 Design a dynamic interactive report template, including images, 3D models, and recommended examinations;

[0126] 8.3 Generate the final report and export it in PDF format for doctors and patients to use;

[0127] 8.4 Upload the processed case data to the cloud platform for secure storage and analysis;

[0128] 8.5 Regularly mine the cloud platform data to identify new disease patterns and trends;

[0129] 8.6 Feed back the big data analysis results into the system to further optimize the diagnostic model.

[0130] In the visualization report generation stage, all relevant diagnostic information is first collected, including imaging data, 3D models, disease analysis results, and personalized suggestions, and then transmitted to the visualization report generation module. This module will organize and format the information to ensure that all content is clear and easy to understand. Next, a dynamic interactive report template is designed, which should include elements such as imaging, 3D model display, and recommended examinations, enabling doctors and patients to better understand the report content through interactive operations. This template not only enhances readability but also improves the information transmission effect. Finally, the system generates the report and exports it in PDF format for easy printing and sharing by doctors and patients. In terms of the cloud data analysis platform, the processed case data is securely uploaded to the cloud platform for storage and subsequent analysis. The data in the cloud platform is regularly mined to identify new disease patterns and trends, and potential clinical values are discovered with the help of big data technology. Finally, the results obtained from the cloud data analysis are fed back into the system to further optimize the diagnostic model to improve future diagnostic accuracy and efficiency.

[0131] By generating visualization reports and using the cloud platform for data analysis, the effective transmission and storage of information are achieved, and the scientific and intelligent clinical decision-making is promoted. This process not only provides intuitive and easy-to-understand diagnostic information for doctors and patients but also drives medical research and innovation through big data analysis, ultimately improving the overall medical quality and patient experience.

[0132] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent image diagnosis and analysis system, characterized in that: The steps include: Step 1: Image data acquisition and preprocessing; Step 2: Multimodal image fusion; Step 3: Patient characteristics adaptation; Step 4: Intelligent analysis and annotation; Step 5: Automatic tracing and 3D modeling; Step 6: Deep learning model optimization; Step 7: Intelligent recommendation and feedback; Step 8: Visualization report and cloud platform analysis.

2. The intelligent image diagnosis and analysis system according to claim 1, characterized in that: The step 1: image data acquisition and preprocessing, specifically includes the following steps:

1. Image data collection and preprocessing: 1.1 Use image acquisition equipment to take medical images of patients; 1.2 Transmit the image data to the system's image database through a secure network to ensure data encryption; 1.3 Apply noise removal algorithm to denoise the image; 1.4 Use artifact removal technology to remove artifacts in the image; 1.5 Apply contrast limiting adaptive histogram equalization to enhance image details; 1.6 Standardize the processed images into a unified format.

3. The intelligent image diagnosis and analysis system according to claim 2, characterized in that: The step 2: multimodal image fusion, specifically includes the following steps:

2. Multimodal image fusion: 2.1 After the image processing module, feature points of different modal images are extracted; 2.2 Apply image registration algorithm to align images of different modalities; 2.3 Perform weighted superposition on the registered images to generate a fused image.

4. The intelligent image diagnosis and analysis system according to claim 3, characterized in that: Step 3: Patient characteristics adaptation, The specific steps include:

3. Adaptive adjustment of patient characteristics: 3.1 Collect the patient's historical medical records and imaging characteristics; 3.2 Analyze historical data through machine learning algorithms to determine the optimal set of image processing parameters; 3.3 Automatically apply these parameter sets to reprocess the current image data.

5. The intelligent image diagnosis and analysis system according to claim 1, characterized in that: The step 4: intelligent analysis and labeling, specifically includes the following steps:

4. Automatic labeling and color difference quantification: 4.1 Use deep learning models to automatically annotate images and identify potential lesion areas; 4.2 In the model annotation module, color difference quantification is performed and the positive feedback mechanism of the annotation model is optimized; 4.3 Generate an evaluation report of the annotation results for subsequent review.

6. The intelligent image diagnosis and analysis system according to claim 1, characterized in that: The step 5: automatic outlining and three-dimensional modeling, specifically includes the following steps:

5. Automatic Stroke and 3D Modeling: 5.1 The marked area data is transferred to the automatic stroke modeling module and the edge detection algorithm is applied; 5.2 Generate a two-dimensional contour map of the part based on the detection results; 5.3 Use 3D reconstruction algorithm to convert the 2D contour into a 3D model.

7. The intelligent image diagnosis and analysis system according to claim 1, characterized in that: The step 6: deep learning model optimization specifically includes the following steps:

6. Identification of disease types: 6.1 The 3D model data is transferred to the deep learning model optimization module; 6.2 Perform morphological feature analysis and use pre-trained deep learning networks to identify symptoms; 6.3 Generate a symptom analysis report based on the identification results for the doctor’s reference.

8. The intelligent image diagnosis and analysis system according to claim 1, characterized in that: The step 7: intelligent recommendation and feedback, specifically includes the following steps:

7. Personalized suggestion generation: 7.1 Automatically generate personalized follow-up examination suggestions based on the disease identification results; 7.2 Allow doctors to input feedback information and update the model recommendation algorithm; 7.3 Integrate recommendations into a visual report for physician review.

9. The intelligent image diagnosis and analysis system according to claim 1, characterized in that: The step 8: visualization report and cloud platform analysis specifically includes the following steps:

8. Visual report generation and cloud data analysis platform: 8.1 Collect all diagnostic information and pass it to the visual report generation module; 8.2 Design dynamic interactive report templates that include images, 3D models, and recommended inspections; 8.3 Generate the final report and export it to PDF format for doctors and patients to use; 8.4 Upload the processed case data to the cloud platform for secure storage and analysis; 8.5 Regularly mine cloud platform data to identify new disease patterns and trends; 8.6 Feed the big data analysis results back into the system to further optimize the diagnostic model.

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