Medical intelligent analysis and auxiliary diagnosis system combined with image recognition

Abstract

The application relates to the technical field of medical image diagnosis, and discloses a medical intelligent analysis and auxiliary diagnosis system combined with image recognition. The system comprises a medical image feature extraction module which recognizes anatomical structure and lesion shape features in an image, extracts pixel distribution parameters and texture feature vectors, and generates a multi-modal image feature set; a standard feature unit construction module which maps the features to a preset diagnosis dimension field, establishes an association identifier, and generates a standardized image feature unit; a diagnosis rule library matching module which searches a rule library to match disease and diagnosis indication clauses, calculates the matching number and the logical nesting depth, and generates a rule matching complexity factor; a case association graph construction module which finds cross-case shared feature entities, counts the frequency and density, and generates an association tightness index; and a comprehensive diagnosis decision module which combines the multi-case association graph and the single-item diagnosis compliance state, calculates a disease risk score, and generates an auxiliary diagnosis decision scheme.

Description

Technical Field

[0001] This invention relates to the field of medical imaging diagnostic technology, specifically to a medical intelligent analysis and auxiliary diagnostic system that combines image recognition. Background Technology

[0002] In modern medical diagnostic processes, medical image analysis is a crucial step in disease identification and assessment, encompassing various image types such as CT, MRI, and ultrasound. The anatomical information and lesion morphology characteristics contained in these images are important references for doctors to determine the type of disease and formulate treatment plans. Currently, medical image analysis mainly relies on doctors' professional knowledge and clinical experience; however, this process has many limitations.

[0003] Medical images contain massive amounts of pixel information and complex texture features. During manual analysis, doctors often struggle to capture all key features, especially for early, small lesions or atypical lesions, where features are easily missed. Furthermore, different doctors have varying perceptions and judgment criteria for image features, potentially leading to different diagnoses even for the same case, making it difficult to ensure consistency in diagnostic results.

[0004] In traditional diagnostic processes, doctors struggle to quickly retrieve and correlate large amounts of historical cases. When encountering rare or complex cases, they cannot readily access diagnostic experience from similar cases for reference, relying solely on their limited personal clinical experience. This not only prolongs diagnostic time but also reduces the accuracy of diagnostic decisions. Furthermore, the medical diagnostic rule system is vast and complex, encompassing numerous disease types and corresponding diagnostic indications. Doctors, in the manual matching process, find it difficult to efficiently complete the statistical analysis of the number of matched clauses and the in-depth analysis of logical nesting, easily leading to incomplete or inaccurate rule application, further impacting diagnostic quality.

[0005] With the continuous growth of medical needs and the increasing number of clinical cases, doctors are facing increasing diagnostic pressure. The traditional medical image analysis and diagnosis model that relies on manual methods can no longer meet the needs of efficient and accurate clinical diagnosis. There is a need for an intelligent system that can automatically extract image features, efficiently match diagnostic rules, and establish case associations to help doctors improve their diagnostic level and efficiency. Summary of the Invention

[0006] The purpose of this invention is to provide a medical intelligent analysis and auxiliary diagnosis system that combines image recognition to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a medical intelligent analysis and assisted diagnosis system combining image recognition, the system comprising:

[0008] The medical image feature extraction module is used to identify anatomical structural features and lesion morphology features in medical images, extract image pixel distribution parameters and texture feature vectors, and generate a multimodal image feature set.

[0009] The standard feature unit construction module, based on the multimodal image feature set, maps features to preset diagnostic dimension fields, establishes the association between anatomical structures and lesion features, and generates standardized image feature units;

[0010] The diagnostic rule base matching module, based on the standardized image feature units, retrieves constraint clauses from the medical diagnostic rule base that match disease types and diagnostic indicators, calculates the number of clause matches and the logical nesting depth, and generates a rule matching complexity factor.

[0011] The case association map construction module, based on multiple standardized image feature units, searches for shared anatomical structure features and lesion distribution features across cases, counts the frequency of repeated occurrences of entities and the density of category distribution, and generates a cross-case association tightness index.

[0012] The comprehensive diagnostic decision-making module calculates the disease risk score and generates an auxiliary diagnostic decision-making scheme based on the multi-case association graph output by the cross-case association graph construction module and the single-item diagnostic conformity status judgment output by the diagnostic rule base matching module.

[0013] Preferably, the step of obtaining the multimodal image feature set is as follows:

[0014] The system segments organ regions and lesion regions in medical images, extracts gray-level histogram statistics and spatial gradient distribution parameters for each region, performs multi-scale texture filtering on the image pixel matrix, and outputs texture energy feature vectors and frequency domain response feature vectors. The system then fuses organ region features and texture feature vectors to generate a multimodal image feature set.

[0015] Preferably, the step of obtaining the standardized image feature unit is as follows:

[0016] The organ morphology features in the multimodal image feature set are mapped to the anatomical structure standard field, and the lesion texture features are mapped to the pathological attribute standard field; the consistency of the field mapping is verified and the feature vector dimension is converted; a spatial location association identifier is established between the anatomical structure standard field and the pathological attribute standard field to generate standardized image feature units.

[0017] Preferably, the step of obtaining the rule matching complexity factor is as follows:

[0018] The disease type field and diagnostic indication field in the standardized image feature unit are analyzed, and logical constraint clauses are filtered in the medical diagnostic rule base. The tree-like nesting level and the number of condition judgment nodes of the matching clauses are counted. Combined with the historical reference frequency and time range coverage of the clauses, a rule matching complexity factor is generated.

[0019] Preferably, the step for obtaining the single diagnostic conformity status determination is as follows:

[0020] The rule matching complexity factor is invoked to match the constraint clause threshold, and the anatomical structure feature value and pathological attribute feature value of the image to be diagnosed are compared item by item; the continuity of the distribution of feature values ​​within the constraint threshold range is verified, and the anatomical structure conformity status and pathological attribute conformity status are generated; the two types of conformity status are fused to generate a single diagnostic conformity status judgment.

[0021] Preferably, the step of obtaining the cross-case association density index is as follows:

[0022] The anatomical structure feature codes and lesion distribution feature codes in multiple standardized image feature units are traversed, and entities with the same code are classified and labeled with entity category labels; the frequency of recurrence and spatial distribution density of entities of the same type in cross cases are statistically analyzed to generate a cross-case association density index.

[0023] Preferably, the steps for obtaining the multi-case association map are as follows:

[0024] Based on the cross-case association density index, topological coding is performed on the anatomical structure feature nodes and lesion feature nodes in the standardized image feature unit; the feature similarity and spatial association strength between nodes are calculated; and a multi-layer atlas structure with feature nodes as vertices and association strength as edge weights is constructed.

[0025] Preferably, the steps for obtaining the disease risk score are as follows:

[0026] Extract all associated paths of lesion feature nodes from the multi-case association map; calculate the single-diagnosis conformity status judgment result and path level depth corresponding to each path; weight the feature deviation and association tightness of nodes in the path to generate a disease risk score.

[0027] Preferably, the step of obtaining the auxiliary diagnostic decision-making scheme is as follows:

[0028] The risk level of lesion feature nodes is classified according to the disease risk score; the anatomical structure association path of high-risk level nodes is marked in the cross-case association map; and a decision scheme containing the location of high-risk lesions and the identification of associated anatomical structures is generated.

[0029] Preferably, the system further includes a diagnostic correction module:

[0030] Based on the aforementioned assisted diagnostic decision-making scheme, the deviation parameters between the disease risk score and the medical diagnostic rule base are detected; the deviation parameters are fed back to the medical image feature extraction module for feature re-extraction; and the pathological attribute feature fields in the standardized image feature unit are iteratively updated.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] The medical image feature extraction module can automatically identify anatomical structural features and lesion morphology features in medical images, extract image pixel distribution parameters and texture feature vectors, and generate a multimodal image feature set. This process eliminates the reliance on manual analysis by doctors, comprehensively captures key features in images, and avoids feature omissions caused by the limitations of manual observation. Especially for early, small lesions or lesions with complex morphologies, it can accurately extract their feature information, providing a comprehensive and reliable feature basis for subsequent diagnosis.

[0033] The standard feature unit construction module, based on a multimodal image feature set, maps features to preset diagnostic dimension fields and establishes association markers between anatomical structures and lesion features, generating standardized image feature units. This module achieves standardized processing of image features, eliminating differences in feature perception among different doctors, enabling image features to be organized and presented according to a unified diagnostic dimension. This facilitates subsequent diagnostic rule matching and case association analysis, promotes the standardization of the diagnostic process, and reduces diagnostic biases caused by inconsistent feature representations.

[0034] The diagnostic rule base matching module, based on standardized image feature units, retrieves medical diagnostic rule base data and matches constraint clauses between disease types and diagnostic indicators. It calculates the number of matched clauses and the depth of logical nesting, generating a rule matching complexity factor. This function can efficiently perform matching analysis on a vast diagnostic rule system. Compared to manual matching, it significantly improves the efficiency and comprehensiveness of rule matching, quickly filtering out disease types and diagnostic indicators that match the characteristics of the current case. This helps doctors clearly understand the degree of fit between the case and the diagnostic rules, providing a clear rule basis for diagnostic decisions and avoiding diagnostic errors caused by incomplete manual matching.

[0035] The case association map construction module, based on multiple standardized imaging feature units, identifies shared anatomical structural features and lesion distribution characteristics across cases. It statistically analyzes the recurrence frequency and category distribution density of these entities and generates a cross-case association density index. This module overcomes the limitations of isolated case analysis in traditional diagnosis, enabling the association of current cases with a large number of historical cases. By analyzing the frequency and distribution density of shared feature entities, it uncovers the relationships between similar cases, providing doctors with rich reference information on similar cases. This is particularly helpful when dealing with rare or complex cases, allowing doctors to draw upon historical diagnostic experience and broaden their diagnostic approaches.

[0036] The comprehensive diagnostic decision-making module, based on multi-case association maps and combined with individual diagnostic conformity assessments, calculates disease risk scores and generates auxiliary diagnostic decision-making schemes. This module integrates diagnostic rule matching results with case association analysis results, comprehensively evaluating diseases from multiple dimensions. The generated disease risk score intuitively reflects the likelihood of disease occurrence, while the auxiliary diagnostic decision-making scheme provides doctors with specific diagnostic reference directions, helping them make more scientific diagnostic decisions based on comprehensive information. This also reduces the diagnostic workload for doctors and improves overall diagnostic efficiency and quality. Attached Figure Description

[0037] Figure 1 This is a timing diagram of the medical intelligent analysis and auxiliary diagnosis system combining image recognition described in this invention;

[0038] Figure 2 Flowchart for obtaining multimodal image feature sets;

[0039] Figure 3 A flowchart for constructing standardized image feature units;

[0040] Figure 4 A flowchart generated for determining the compliance status of a single diagnostic item. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1 This invention provides a medical intelligent analysis and assisted diagnosis system combining image recognition. The system includes: a medical image feature extraction module, a standard feature unit construction module, a diagnostic rule base matching module, a case association graph construction module, and a comprehensive diagnostic decision module. Specific implementation methods are as follows:

[0043] The medical image feature extraction module uses image segmentation and feature extraction techniques to identify anatomical structural features and lesion morphology features from medical images, extracting pixel distribution parameters and texture feature vectors to generate a multimodal image feature set. The standard feature unit construction module maps multimodal image features to preset diagnostic dimension fields, establishing association markers between anatomical structures and lesion features, and generating standardized image feature units. The diagnostic rule base matching module searches the medical diagnostic rule base based on the standardized image feature units, matching constraint clauses between disease types and diagnostic indicators, calculating the number of matching clauses and the logical nesting depth, and generating a rule matching complexity factor. The case association graph construction module analyzes shared anatomical structural features and lesion distribution features across cases, statistically analyzing the frequency of entity recurrence and category distribution density to generate a cross-case association tightness index. The comprehensive diagnostic decision module combines the multi-case association graph with single-diagnosis conformity status judgments to calculate disease risk scores and generate auxiliary diagnostic decision-making schemes.

[0044] Example 1: See Figure 2 The core function of the medical image feature extraction module is to extract a multimodal image feature set from the input medical images. The input formats of medical images include, but are not limited to, standard medical image formats such as DICOM, NIfTI, or JPEG. The system first preprocesses the input images, including grayscale normalization and spatial normalization. Grayscale normalization linearly maps pixel values ​​to the range of 0-255, eliminating brightness differences caused by acquisition from different devices. Spatial normalization uses affine transformation to align the image to a standard anatomical space. The reference template is the internationally recognized MNI152 brain atlas or the standard template for the human abdomen; the specific choice depends on the anatomical location of the image.

[0045] The preprocessed images are input into a deep learning segmentation network for organ and lesion region division. The segmentation network adopts a modified U-Net architecture, with its encoder consisting of 5 convolutional blocks. Each convolutional block contains two 3×3 convolution operations, a batch normalization layer, and a ReLU activation function, followed by a 2×2 max-pooling layer for downsampling. The decoder symmetrically employs 5 transposed convolutional blocks, each containing upsampling, feature concatenation, and two 3×3 convolution operations. Skip connections concatenate the feature maps of each layer of the encoder with the corresponding layers of the decoder, preserving multi-scale spatial information. The network output is a binary segmentation mask, marking the pixel-level locations of organs and lesions.

[0046] Based on the segmentation results, the system extracts grayscale statistical features from organ regions and lesion regions respectively. For each region, four statistical measures of the grayscale histogram are calculated: mean reflects the overall brightness level, variance characterizes the degree of pixel value fluctuation, skewness describes the asymmetry of the histogram distribution, and kurtosis measures the sharpness of the distribution. Spatial gradient features are calculated using the Sobel operator, obtaining the gradient magnitudes in the horizontal and vertical directions respectively, and statistically analyzing the joint distribution histogram of the gradient magnitudes to generate a 16-dimensional gradient feature vector.

[0047] The Gabor filter bank is configured with four directional parameters (0°, 45°, 90°, 135°) and six scale parameters (wavelengths increasing by a factor of 2 from 2 to 32 pixels) to filter the image. The energy value of each filtered result is calculated, which is the sum of the squares of the pixel values ​​in the filtered image, ultimately yielding a 24-dimensional texture energy feature. Frequency domain analysis is achieved through a Fast Fourier Transform (FFT), which transforms the image into a frequency space and divides it into three sub-bands: low-frequency, mid-frequency, and high-frequency. The proportion of energy in each sub-band to the total energy is calculated, forming a 3-dimensional frequency domain feature.

[0048] Volume is calculated by statistically segmenting the number of foreground pixels in the mask and converting it to actual cubic millimeters based on the spatial resolution of the image. Surface complexity is estimated using a three-dimensional fractal dimension algorithm, and the self-similarity characteristics of the mask surface at different observation scales are calculated using box counting. In addition to volume, the morphological characteristics of the lesion include sphericity, edge irregularity, and internal heterogeneity. Sphericity is calculated as the ratio of the lesion surface area to the surface area of ​​an ideal sphere of the same volume. Edge irregularity is measured using a convex hull defect algorithm to determine the degree of deviation between the actual contour and the convex hull contour. Internal heterogeneity is obtained by calculating the coefficient of variation of the grayscale values ​​of the lesion region.

[0049] Numerical features were standardized using Z-scores, while categorical features were encoded using one-hot encoding. Dimensionality reduction was performed before feature concatenation, with principal component analysis retaining 95% of the original information, compressing high-dimensional features into 64-dimensional vectors. The dimensionality-reduced feature vectors were then combined with spatial coordinate information (organ centroid location and lesion centroid location) to form the final multimodal image feature set.

[0050] The segmentation network output undergoes morphological post-processing, employing opening operations to eliminate minor noise and closing operations to fill holes. A reasonableness check is implemented during the feature calculation stage; for example, if the organ volume exceeds the range of common anatomical knowledge, a manual review process is triggered. Redundancy detection is performed before feature fusion, and feature pairs with correlation coefficients exceeding a threshold are discarded. The entire process runs in a GPU-accelerated environment, with a single feature extraction time controlled within 3 seconds, meeting clinical real-time requirements.

[0051] The output of the medical image feature extraction module is a structured feature set, containing the following data groups: organ anatomical features (volume, location, morphological parameters), lesion pathological features (size, shape, texture parameters), and multimodal fusion feature vectors. This data is transmitted to subsequent processing modules via a standard interface using the Protocol Buffers serialization protocol, ensuring efficient data transmission and cross-platform compatibility. The module design supports horizontal expansion, allowing the integration of new feature extraction algorithms through a plug-in mechanism, such as adding fiber-tracking features from diffusion tensor imaging or time-intensity curve features from dynamically enhanced images. The processing workflow is compatible with low-quality image input, using adaptive filtering techniques to handle motion artifacts and noise interference. For small sample data, a transfer learning strategy is employed; the pre-trained model is initially trained on a public dataset and then fine-tuned using a small amount of labeled data from the target hospital. Regarding privacy protection, all feature extraction operations are performed on a local server; the original image data does not leave the hospital. Anonymization is implemented during feature set transmission, removing all meta-information related to patient identity.

[0052] Computationally intensive tasks, such as neural network inference, are deployed on NVIDIA Tesla series GPUs, while memory-intensive operations, such as large matrix calculations, run on Intel Xeon processors. The storage system employs a tiered design, with hot data stored on NVMe solid-state drives and cold data archived to a distributed file system. This architecture balances computational performance and cost-effectiveness, and can run stably in a typical hospital computing environment.

[0053] Core functionalities are encapsulated as independent services, exposed via RESTful APIs. These services are deployed in Docker containers, supporting rapid expansion and rolling upgrades. Configuration management employs a declarative approach, with all parameters defined via YAML files, supporting dynamic loading and hot updates. A logging system records detailed operation logs, meeting the audit requirements of medical information systems. Test coverage exceeds 90%, with unit tests simulating various boundary conditions, including extreme image sizes, abnormal pixel values, and missing metadata. When acquiring images from the PACS system, the system automatically parses acquisition parameters from DICOM tags, such as scan sequence parameters and contrast agent information. When the output feature set is written to the hospital data center, it automatically generates observational data resources conforming to the FHIR standard, seamlessly integrating with the electronic medical record system. The system provides visualization tools for radiologists to review feature extraction results, supporting segmentation mask overlay display and feature value distribution histogram display to assist in manual quality control decisions.

[0054] A feature algorithm registry is established to record the calculation method and version evolution of each feature. When medical guidelines are updated, leading to changes in feature definitions, the system supports parallel calculation and result comparison of multiple feature versions. Feedback is regularly collected from clinical use, and the feature selection strategy is gradually optimized through online learning, eliminating features with low discriminative power and adding new imaging biomarkers. Version releases adopt a blue-green deployment strategy, allowing for instant switching between old and new versions to ensure continuity of clinical services.

[0055] The medical image feature extraction module is designed with full consideration of the characteristics of different image modalities. CT images focus on the distribution characteristics of Henlein unit values, MRI images focus on the signal intensity relationship between multiple sequences, ultrasound images analyze speckle noise patterns, and PET-CT integrates metabolic and anatomical features. For specialized applications, such as adding emphysema index features to lung CT, adding white matter high-signal volume measurement to brain MRI, and adding calcification cluster analysis to mammography, this specialized customization is achieved through configuration strategy files without modifying the core code.

[0056] Input data anomalies include corrupted image files, missing sequence images, and incomplete metadata. The system defends against these anomalies through checksum checks, sequence integrity verification, and mandatory field checks. Processing anomalies, such as GPU memory overflow or insufficient disk space, automatically degrade to CPU mode or clean up temporary files. Business logic anomalies, such as segmentation failures or feature calculation timeouts, are handled by the system after recording detailed error context, skipping the current case without affecting the overall workflow. All anomalies are handled according to severity, and critical errors are immediately reported to the system administrator.

[0057] Neural network inference is accelerated using TensorRT, feature computation is parallelized using SIMD instructions, and disk I / O latency is reduced through memory caching. High-frequency operations such as convolution operations utilize the highly optimized BLAS library, and feature dimensionality reduction uses the stochastic PCA algorithm to handle large-scale data. System resource usage is dynamically controlled, limiting computing resources for non-critical tasks during peak business periods to ensure the responsiveness of core functions.

[0058] Example 2: See Figure 3The standard feature unit construction module receives a multimodal image feature set from the medical image feature extraction module. This set includes organ anatomical feature groups, lesion pathological feature groups, and fused feature vectors. The spatial coordinate information in the organ anatomical feature groups undergoes a coordinate system transformation, mapping it from the original image coordinate system to the standard anatomical spatial coordinate system. The transformation process employs a 12-DOF affine transformation, calculating the transformation matrix using a feature point matching algorithm. Feature points are selected from anatomical landmarks of the organ, such as the bifurcation of the portal vein in the liver or the carina of the trachea in the lungs. The transformed coordinates are encoded according to standard anatomical structure fields, with the encoding system referencing international anatomical terminology standards. Each organ is assigned a unique identifier, and its three-dimensional spatial position, volume, and orientation parameters are recorded.

[0059] For lesions with known pathological types, the system directly calls a predefined mapping rule base. This rule base is constructed according to the WHO disease classification standards and contains typical feature descriptions of various lesions. For example, the spiculation of pulmonary nodules is mapped to the edge irregularity field, and the enhancement pattern in enhanced CT scans is mapped to the internal blood supply feature field. For lesions of unknown types, a multi-classifier voting mechanism determines the mapping relationship. The classifier group includes three algorithms: support vector machine, random forest, and gradient boosting tree. Each classifier is trained on an independent training set, and the mapping is only executed when the voting result reaches a preset confidence threshold.

[0060] The network input is a 64-dimensional fused feature vector, which is progressively transformed into a 256-dimensional standard space through three fully connected hidden layers. The number of neurons in the hidden layers are 128, 192, and 256, respectively. Each layer is followed by batch normalization and dropout layers to prevent overfitting. The network training uses a contrastive loss function, ensuring that feature vectors of similar cases are close in distance in the standard space, while those of dissimilar cases are far apart. The training data comes from labeled case sets from multiple medical centers, covering common diseases and rare cases, ensuring the generalization ability of the transformation network.

[0061] Spatial associations are established by calculating the distance from the centroid of the lesion to the surface of the nearest organ. The distance is calculated using three-dimensional Euclidean distance and normalized to a percentage of the organ's radius. Functional associations are derived from a medical knowledge graph, where nodes represent anatomical structures and edges represent physiological functional connections. For example, the association between hepatocellular carcinoma lesions and the portal vein considers not only spatial distance but also the functional labeling of the portal vein's blood supply relationship. Association identifiers are stored using a weighted directed graph structure, with edge weights reflecting the strength of the association.

[0062] The diagnostic rule base is stored in a graph database, where nodes represent diagnostic entries and edges represent logical constraints. The retrieval process extracts key feature values ​​from standardized units as query conditions and performs multi-hop traversal within the graph database. During matching, the applicability score of each rule is dynamically calculated, weighted by three factors: feature conformity, rule authority, and clinical timeliness. Rule authority is set according to the issuing institution's level, and clinical timeliness is calculated based on the distance between the rule's validity period and the current date.

[0063] The calculation of the rule matching complexity factor comprehensively considers the complexity of the logical structure and the diversity of clinical practice. The logical nesting depth is obtained by analyzing the conditional decision tree of the rules, statistically analyzing its maximum branch depth and the number of leaf nodes. Conditional decision nodes are assigned different weights based on their type, with numerical range judgments receiving lower weights and combinational logic judgments receiving higher weights. Historical citation frequency is calculated using a time decay model, with recent citation records receiving greater weight. Timeliness coverage checks whether the rules cover timeliness factors such as the collection time of the current case and the patient's age. Finally, the complexity factor is fused into a scalar value using a multilayer perceptron.

[0064] Anatomical structural feature coding employs a hierarchical coding scheme: the first three digits represent the body system, the middle five digits identify the specific organ, and the last four digits record morphological variations. Lesion distribution feature coding includes pathological type codes and spatial distribution pattern codes. Fuzzy matching algorithms are used for code matching, tolerating a certain degree of parameter differences. The classification process for entities of the same type maintains a dynamically updated feature prototype library. The feature code of a new case is compared with existing patterns in the prototype library for similarity; if the similarity exceeds a threshold, it is classified into an existing category; otherwise, a new category is created.

[0065] The frequency of entity recurrence is statistically analyzed using a sliding time window method, with a configurable window size (defaulting to the most recent 1000 cases). Spatial distribution density is calculated using three-dimensional kernel density estimation, with adaptive bandwidth adjustment. The cross-case association density index integrates frequency statistics and density analysis results, incorporating a weighting factor from the source hospital, indicating a greater contribution from cases from authoritative medical centers. The index structure uses an inverted index to accelerate queries, with feature codes serving as keys and the list of associated cases as values.

[0066] The feature mapping process employs a dual-verification mechanism, with key field mapping results subject to sampling review by senior radiologists. The dimensionality transformation network utilizes an online learning mechanism, automatically adding newly labeled cases to the training set to trigger incremental model updates. Rule matching incorporates an interpretable component, visually displaying matching paths and weight allocations, and supporting clinical expert review. Case association analysis employs differential privacy technology, adding compliance noise before statistical results are published to prevent patient privacy leaks. Feature mapping and dimensionality transformation are deployed on a GPU server cluster, utilizing CUDA to accelerate matrix operations. Rule matching and case association analysis run on high-memory servers, using high-speed SSDs to store intermediate results. Network communication employs RDMA technology to reduce data transmission latency, and the distributed computing framework automatically balances node load. The storage system uses a hybrid architecture: hot data is stored in an all-flash array, lukewarm data is stored on hard disk drives, and cold data is archived in a tape library.

[0067] The input feature set undergoes a completeness check, triggering an automatic completion process if key fields are missing. Mapping results record version information, supporting historical tracing and rollback. The output of the dimensionality transformation network monitors distribution shifts, automatically triggering retraining upon anomaly detection. The rule matching process records detailed logs, including the matching path and score composition for each rule. Case association analysis undergoes sampling verification, with periodic manual review of the association results for their reasonableness.

[0068] The module interface design follows the medical information system integration specifications. Input and output adopt the HL7FHIR standard, with feature units represented as Observation resources and association graphs represented as List resources. Service interfaces support synchronous and asynchronous call modes; in asynchronous mode, task identifiers are returned for subsequent queries. Access control implements the RBAC model, with different roles having differentiated data access permissions. Audit logs record all critical operations, complying with HIPAA compliance requirements.

[0069] When feature mapping fails, the system automatically downgrades to a conservative strategy, performing only basic field mapping. Rule matching timeouts trigger a timeout handling process, returning completed intermediate results. Case association analysis experiences memory overflow, automatically switching to batch processing mode. All error events are categorized and handled hierarchically; non-critical errors are logged and the process continues, while critical errors trigger alarms and suspend related services.

[0070] The feature mapping rule base is updated monthly to keep pace with the latest medical classification standards. The dimensionality transformation network undergoes A / B testing, with the new model version running in shadow mode to verify its effectiveness. Impact analysis is performed on rule base changes to assess their impact on historical case diagnoses. Case association algorithm upgrades utilize blue-green deployment, with new and old versions running in parallel for result comparison. A version rollback mechanism ensures rapid recovery capabilities in emergency situations.

[0071] Feature mapping employs a caching mechanism, reusing cached results from high-frequency mapping patterns. The dimensionality transformation network utilizes quantization techniques, converting the FP32 model to INT8 to improve inference speed. Rule matching incorporates a pre-filtering step to quickly eliminate obviously irrelevant rules. Case association analysis uses an approximate algorithm to improve processing throughput with acceptable accuracy loss. Resource scheduling implements dynamic priority adjustment, automatically allocating more computing resources to emergency cases.

[0072] Data transmission is encrypted end-to-end using the TLS 1.3 protocol. Stored data is encrypted at the field level, with sensitive data protected separately. Access logs are designed to prevent tampering, and digests are stored using blockchain technology. System vulnerabilities are scanned regularly, and critical patches are applied within 24 hours. Disaster recovery measures ensure data recovery in extreme circumstances, with backup strategies including daily incremental backups and weekly full backups.

[0073] Example 3: See Figure 4 The diagnostic rule base matching module and the case association map construction module work together to complete the conversion process from standardized image feature units to single-item diagnostic conformity determination and multi-case association maps. The core of this implementation method lies in establishing a precise matching mechanism between medical diagnostic rules and image features, and mining potential clinical patterns through cross-case feature association.

[0074] After standardized image feature units are input into the diagnostic rule base matching module, feature parsing is performed first. The disease type field extracted from the units is semantically expanded using medical ontology; for example, "pulmonary nodule" is expanded to subcategories such as "pulmonary ground-glass opacity" and "solid nodule." Diagnostic indicator fields are decomposed into numerical and categorical indicators. Numerical indicators include lesion diameter and CT value range, while categorical indicators include edge morphology and enhancement features. The rule base is stored using a graph structure, where nodes represent diagnostic entries, edges represent logical relationships, and edge attributes include constraints and confidence scores. Rule matching employs a bidirectional search strategy, both searching for applicable rules from features and backtracking for required features from diagnostic entries.

[0075] For each matching rule clause, analyze its logical structure depth D. r The number of condition judgment nodes N r D r N represents the maximum path length of the rule tree. r Count the total number of all decision nodes. Introduce a time decay factor w. t Adjusting the impact of historical citation frequency t c t represents the current time. r λ is the most recent reference time of the rule, and λ is the decay coefficient. Time-limited coverage C v Calculate the proportion of cases covered by the rule clauses that have time-to-death parameters. Final complexity factor F. cThrough weighted fusion calculation:

[0076]

[0077] Where: α, β, γ, δ are weighting coefficients, D max and N max The maximum value in the rule base is used for normalization.

[0078] The conformity test of anatomical structural feature values ​​is divided into spatial conformity and morphological conformity. Spatial conformity calculates the organ position offset and compares it with standard anatomical atlases to generate a deviation score. Morphological conformity analyzes volume ratios and shape parameters, employing a dynamic threshold adjustment strategy to automatically adjust the judgment threshold based on the patient's body shape parameters. The test of pathological attribute feature values ​​focuses on feature combination patterns, such as whether the combined distribution of calcification morphology and enhancement degree matches typical case patterns. The two conformity states are fused through a decision tree, which employs cost-sensitive learning during training to reduce the weight of false negative results.

[0079] The case association map construction module processes multiple standardized imaging feature units to build a cross-case feature network. Anatomical structure feature nodes are rapidly located for similar structures using a 3D hash algorithm; the hash key is generated by combining organ type encoding and spatial grid coordinates. The similarity calculation for lesion feature nodes integrates phenotypic features and spatial distribution. Phenotypic similarity uses a modified Jaccard index to consider differences in feature importance. Spatial distribution similarity is calculated to determine the correlation of spatial distribution patterns in lesion clusters, quantifying distribution differences through a point-process model.

[0080] The multi-layered graph structure is constructed using a hypergraph model. Vertices represent feature entities, and hyperedges connect related sets of entities. The weight W of each hyperedge is... h Determined by the strength of the association between entities:

[0081]

[0082] Wherein: S i Let R represent the i-th similarity component. i For clinical relevance weights, d i This serves as a feature dimension adjustment factor. The map's hierarchical structure is based on the medical knowledge system: the basic layer represents anatomical structural associations, the intermediate layer represents pathophysiological associations, and the upper layer represents clinical diagnostic associations.

[0083] Rule-based matching incorporates fuzzy logic to handle uncertain features and sets confidence thresholds to filter low-quality matches. Case association employs an incremental update mechanism, calculating only the affected subgraphs when a new case arrives. Memory management utilizes an object pool model, keeping frequently accessed feature nodes resident in memory. The parallel computing framework distributes feature similarity calculation tasks across multiple worker nodes.

[0084] Rule matching results undergo cross-validation, and a random sample of cases is reviewed by an expert system. Redundancy checks are performed during feature node creation, and highly similar feature entities are merged. Consistency checks are performed during graph updates to ensure that newly added edges do not violate medical common sense. Detailed operation logs are recorded throughout the process, supporting result traceability and problem troubleshooting.

[0085] The input interface receives a standardized feature unit stream, supporting both batch import and real-time push modes. The output interface provides a graph query service, supporting feature-based similar case retrieval and association path discovery. The management interface allows clinical experts to adjust weight parameters and threshold settings to adapt to different application scenarios.

[0086] Rule matching timeout triggers degradation processing, returning partial matching results and marking the integrity status. When feature node conflicts occur, a coordination process is initiated to resolve the conflict based on creation timestamps and source authority. When graph storage fails, a backup storage node is automatically switched to ensure data persistence. When resources are insufficient, a load balancing strategy is triggered, redirecting tasks to idle nodes.

[0087] The rule index employs a multi-level caching structure, with frequently accessed rules residing in memory. Feature similarity calculation utilizes an approximation algorithm to improve throughput with acceptable accuracy loss. Graph traversal optimizes access patterns by preloading densely associated subgraphs into the cache. Resource scheduling implements dynamic priority adjustment, prioritizing computing resources for urgent case requests.

[0088] Feature data storage employs field-level encryption, with sensitive features such as tumor location protected separately. Access control utilizes attribute-based encryption for fine-grained management of data usage permissions. Audit logs are protected for integrity and employ digital signatures to prevent tampering. Data transmission uses a dedicated medical VPN tunnel to ensure secure communication.

[0089] Integration with electronic medical record systems retrieves patient history information to aid rule matching. Interface with image archiving systems supports access to original images for feature review. Linkage with reporting systems automatically generates descriptive text for key findings in the atlas. Mobile devices support visual browsing of the atlas, allowing physicians to explore feature relationships through touch controls. The rule base implements version control, performing impact assessments before major changes. Atlas structure upgrades employ a gradual migration approach, with parallel operation of old and new formats ensuring a smooth transition. Performance monitoring collects processing metrics in real time, automatically triggering optimization processes upon identifying bottlenecks. Error pattern analysis establishes a knowledge base for addressing typical problems, enhancing the system's self-healing capabilities. The basic data layer contains raw matching results and atlas structures for algorithm analysis. The business view layer organizes feature relationships according to specialty needs, such as focusing on metastasis pathways in oncology. The interactive display layer optimizes visualization effects, supporting dynamic filtering and focus switching. This layered output meets different granularity needs from research analysis to clinical decision-making.

[0090] Boundary feature values ​​are represented probabilistically, such as by adding confidence intervals to "possibly match" states. Conflict rule matching initiates an arbitration process, invoking a multi-expert voting mechanism. Fuzzy association paths are labeled with uncertainty markers to alert users to their judgments. This design enables the system to reasonably handle the inherent uncertainties in the medical field.

[0091] Containerized deployment supports a hybrid cloud architecture, with core services running on local servers and elastic tasks extended to the cloud. GPU resources are dynamically allocated, prioritizing critical computing during peak hours. Storage strategies differentiate between hot, warm, and cold data, optimizing the balance between storage costs and access efficiency. Energy management employs intelligent scheduling, executing resource-intensive background tasks during off-peak hours.

[0092] The module's evaluation mechanism is based on practical clinical value. Regular physician feedback is collected to assess the clinical relevance of matching results. Atlas analysis results are compared with follow-up data to validate the predictive value of association patterns. System performance monitoring focuses on clinical scenario indicators, such as the average processing time for emergency cases.

[0093] Example 4: The comprehensive diagnostic decision-making module analyzes the topological characteristics of lesion feature nodes in the cross-case association network and combines this with the degree of conformity to clinical diagnostic rules to form a structured decision support output. The module input includes two main data sources: a multi-layer association graph output by the case association graph construction module, and a set of single-item diagnostic conformity status judgments generated by the diagnostic rule base matching module. The multi-case association graph is stored in a graph database, containing three vertex types and four edge types. The graph query interface supports multi-condition retrieval, such as filtering association paths by lesion type or searching for high-frequency co-occurrence features by anatomical region.

[0094] The system first filters all nodes containing lesion features from the graph and assigns initial weights based on feature type and clinical importance. Weight allocation references risk stratification criteria in clinical guidelines; for example, features related to malignant tumors have higher weights than those related to benign lesions. The association path extraction employs a restricted depth-first search algorithm, starting from the lesion node and expanding the search along various edges, with a maximum path depth of 5 layers to avoid overgeneralization. See Table 1, which visualizes the decision-making basis during the scoring process, assisting clinicians in understanding the system's reasoning logic.

[0095] Table 1: Risk Score Factor Analysis Table for Typical Lesion Feature Nodes.

[0096]

[0097] The comprehensive scoring phase integrates three key indicators: path-level indicators reflecting the depth of association, diagnostic conformity indicators representing the degree of matching with clinical rules, and feature deviation indicators quantifying the difference from typical cases. The scoring algorithm employs a dynamic weighting strategy, automatically adjusting the contribution ratio of each indicator based on the feature type. For example, for malignant tumor-related features, the weight of diagnostic conformity is set higher; for chronic disease features, more attention is paid to the consistency of evolution patterns across cases. The final risk score is converted into a five-level grading standard, from very low risk to very high risk, with each level corresponding to a different intensity of clinical treatment recommendations.

[0098] The system predefines standard report templates for various lesions, including essential descriptive elements. Personalization is dynamically generated based on the specific case's atlas characteristics, such as annotating unique anatomical variations or rare phenotypic combinations. The output is a structured document containing text descriptions, risk level labels, and a visual atlas summary. The localization and labeling of high-risk lesions combines spatial analysis and semantic annotation. All anatomical structures connected to high-risk nodes are extracted from the atlas, spatial projection relationships are calculated, and key coordinates are annotated on the 3D model. Semantic annotation uses standardized medical terminology; anatomical structure names conform to international anatomical nomenclature, and pathological descriptions refer to WHO classification standards. The localization results are registered with the original images to generate annotated 2D sections and 3D reconstructed views.

[0099] The risk scoring process employs dual-channel verification. After the main algorithm generates the initial score, an auxiliary verification module performs a rationality check based on simplified rules. Decision generation incorporates natural language generation technology, converting structured data into descriptive text conforming to clinical reporting standards. Visualization utilizes a medical-grade rendering engine to ensure the accuracy of anatomical annotations and image quality meet diagnostic requirements. Input data undergoes integrity verification, checking for the presence of essential fields. Intermediate results are recorded during the scoring calculation process, supporting step-by-step traceability during manual review. Logical consistency checks are performed before outputting the decision plan, ensuring that the textual description matches the atlas analysis results. Cases are periodically sampled for review by an expert committee, and feedback is collected for algorithm optimization.

[0100] The input interface accepts standardized atlas data formats and conforms to state data structures. The output interface offers multiple formats, including structured data APIs, PDF report documents, and DICOM-SR structured reports. The management interface allows configuration of risk grading thresholds and report template styles to adapt to the working habits of different medical institutions. When atlas data is abnormal, a degradation processing mode is initiated, using a locally cached basic association model. When score calculation times out, an estimated range is returned with a confidence level. When protocol generation fails, a simplified list of key findings is provided to ensure the availability of basic information. All error events are tiered according to urgency, prioritizing the continuity of treatment for high-risk cases. Atlas queries implement index optimization, establishing fast retrieval channels for frequently accessed paths. Score calculation uses tiered caching, reusing intermediate results from similar feature nodes. Protocol generation implements parallel pipeline processing, with text, charts, and annotations generated simultaneously. Resource scheduling implements a clinical priority strategy, automatically allocating the highest computing resource quota to emergency cases.

[0101] Patient identification information is automatically anonymized after the protocol is generated, and the report retains only necessary medical identifiers. Data transmission employs end-to-end encryption, and stored data is subject to field-level access control. Audit logs record the complete operation trajectory, supporting post-event accountability. Integration with the hospital information system supports automatic triggering of decision analysis, initiating processing immediately upon completion of examinations. Integration with the PACS system supports writing back annotation results for retrieval by the imaging workstation. Linkage with electronic medical records allows embedding decision protocols into progress notes. Mobile adaptation supports real-time report viewing and interactive exploration of correlation maps. The scoring model is version-controlled, undergoing retrospective validation before major updates. A dynamic update mechanism is established for the decision template library, automatically contributing template fragments from newly diagnosed special cases. Performance monitoring tracks key indicators in real time, such as average report generation time and emergency response speed. Error pattern analysis establishes a typical case library to optimize the system's anomaly handling capabilities.

[0102] After analysis tables similar to Table 1 are automatically generated, their fields are reviewed and verified for reasonableness by medical experts. Table versions are linked to algorithm versions to ensure reproducible results. Storage strategies differentiate between frequently accessed and archived data, balancing access efficiency and storage costs. Access control is based on role-based access control, with access to sensitive tables restricted.

[0103] Different specialties can customize risk level definitions; for example, oncology can adjust the malignancy risk threshold. Decision-making templates support specialty-specific extended fields; for instance, cardiovascular specialists can add hemodynamic parameter descriptions. Atlas visualization supports specialty preference settings; for example, neurology specialists can focus on white matter fiber bundle display modes. This design maintains the consistency of the core algorithm while meeting the needs for in-depth specialization.

[0104] The structured report uses a segmented, collapsible display, supporting quick location of key information. The visual map offers multi-scale browsing, allowing free zooming from a global overview to detailed features. Risk levels use a combination of color coding and icons to enhance visual clarity. Interactive features allow clinicians to add annotations and correction suggestions, forming a closed loop of human-computer collaboration.

[0105] For cases marked as emergency, a rapid analysis channel is activated, prioritizing key feature extraction and core correlation path analysis. Emergency report generation simplifies unnecessary fields and highlights urgent treatment recommendations. The status notification system pushes processing progress updates in real time, allowing clinical teams to monitor waiting times. Cases with boundary risk scoring automatically trigger additional explanations, listing key factors supporting and opposing the score. Conflicting correlation paths are marked with warning signs to alert clinicians to make informed decisions. Low-confidence results limit the automatic generation of treatment recommendations, instead providing reference information for human decision-making. This design allows the system to reasonably express the uncertainty of judgments, avoiding the misleading effect of overconfidence. Containerized deployment allows for dynamic scaling of computing nodes to cope with fluctuations in examination volume. An in-memory database caches frequently accessed atlas data, reducing disk I / O latency. Task queues implement priority management to ensure resource allocation matches clinical urgency. Energy monitoring optimizes computational load distribution, reducing energy consumption pressure during peak periods.

[0106] Regularly collect physician feedback to evaluate the clinical helpfulness of decision-making plans. Analyze report adoption rates and revision frequency to assess the acceptability of system recommendations. Track subsequent treatment pathways for high-risk cases to verify the predictive accuracy of risk scores. This clinical effectiveness-oriented evaluation mechanism ensures that system development always focuses on real medical needs.

[0107] Example 5: The diagnostic correction module constitutes a closed-loop mechanism for system self-optimization. By continuously monitoring the matching deviation between the auxiliary diagnostic decision-making scheme and the medical diagnostic rule base, it drives iterative improvement of feature extraction and analysis. This module establishes a feedback path from decision output to front-end feature extraction, realizing the dynamic evolution of the system's diagnostic capabilities.

[0108] The disease risk score in the assisted diagnostic decision-making scheme is compared with the expected range of the medical diagnostic rule base to calculate multidimensional bias parameters. Bias types include numerical bias and logistic bias. Numerical bias is such as the risk score exceeding the confidence interval suggested by the rule base, while logistic bias is such as the anatomical association path in the decision-making scheme not conforming to the typical propagation pattern in the rule base. The bias parameters are calculated using a weighted difference model, considering differences in clinical importance, with the bias weight of key diagnostic indicators significantly higher than that of secondary indicators.

[0109] The system generates a deviation analysis report, annotating suspicious feature fields and deviation directions, and transmits it to the medical image feature extraction module. The re-extraction process employs an adversarial generative network to enhance feature robustness. The generator network receives the original image and deviation report as input, learns a feature compensation pattern through a four-layer convolutional structure, with kernel sizes of 7×7, 5×5, 3×3, and 3×3, and outputs a feature-enhanced image representation. The discriminator network contains a three-layer fully connected structure to determine the consistency of the generated features with the standard features. Adversarial training employs an alternating optimization strategy until the generated features pass the discriminator's test.

[0110] The system maintains a dynamic model library of feature mapping functions. When re-extracting features, the corresponding mapping function is called for incremental training. The training data consists of historical labeled cases and newly discovered biased cases, and the sampling strategy ensures sufficient representation of difficult samples. The optimization algorithm uses stochastic gradient descent with a driving term, with an initial learning rate of 0.001, decaying by 10% every 10 iterations. An early stopping strategy is implemented during training; updates are terminated when the validation set loss fails to decrease for three consecutive iterations, and the maximum number of iterations is limited to 100.

[0111] The system establishes a versioned processing channel, allowing parallel processing of old and new feature fields and recording the differences in results. The diagnostic rule base matching module receives the updated feature units and re-executes rule matching and complexity calculation. The case association graph construction module incrementally indexes the updated features and updates the association strength of affected nodes. The comprehensive diagnostic decision module regenerates the risk score and decision plan based on the new data, forming the revised output.

[0112] Deviation detection employs a multi-threshold triggering mechanism, setting different sensitivity thresholds for different processing strategies. Feature re-extraction utilizes region focusing, locating key anatomical regions based on deviation reports for refined re-extraction. Online learning incorporates flexible weighting and solidification technology to preserve knowledge representations from important historical cases. Version management operates in shadow mode; new version processing results do not immediately impact clinical output but must pass quality gate checks.

[0113] The deviation detection results are subject to sampling verification, with clinical experts confirming the validity of the deviations. Re-extracted features undergo distribution stability testing to prevent feature space drift. The online learning process monitors model degradation; if the updated model's performance on the baseline case set deteriorates beyond a threshold, it is automatically rolled back. The revised decision plan is compared in a structured manner with the original plan, and all changes are marked for clinical review.

[0114] The input interface receives decision schemes and rule base metadata. The output interface provides bias analysis reports and feature update notifications. The management interface allows configuration of bias thresholds and learning rate parameters. The debug interface outputs detailed optimization trajectory logs, including feature changes and model update magnitudes for each iteration.

[0115] When bias analysis fails, a conservative strategy is activated, re-extracting only core feature fields. When adversarial training fails to converge, a traditional feature enhancement method is switched to. When online learning experiences memory overflow, the batch size is automatically reduced. In case of version conflicts, both old and new results are retained for manual review. All error events are subject to tiered alerts, and system administrators are immediately notified of core process interruptions.

[0116] Deviation detection employs incremental computation, analyzing only newly generated decision-making schemes. Feature re-extraction utilizes region-selective processing, focusing on deviation-related anatomical regions. Online learning implements parameter freezing, maintaining fixed weights for stable feature mapping layers. Version comparison uses differential encoding technology for efficient storage of result change records. Deviation analysis reports undergo content anonymization, removing patient-related information. The re-extraction process is completed within a secure computing environment, ensuring the original images do not leave the isolation zone. Online learning parameter updates are transmitted with encryption to prevent model leakage. Audit logs record all data access behaviors, complying with medical data governance standards. Integration with hospital quality control systems automatically generates quality improvement cases from deviation events. Linkage with teaching systems transforms typical amendment examples into training materials. Integration with research platforms allows anonymized corrected data to support clinical research. Mobile devices support real-time feature update notifications, facilitating physician tracking of system optimization progress.

[0117] The bias detection model is updated quarterly to incorporate newly discovered misclassification patterns. The adversarial network architecture is regularly optimized based on hardware advancements, such as adapting to new AI acceleration chips. The online learning mechanism undergoes rolling evaluation, eliminating inefficient optimization algorithms. The version management system is continuously enhanced to support more complex branch management strategies. Specific bias thresholds can be set for different specialties, such as reducing the tolerance for bias in malignant features in oncology. Feature re-extraction intensity is adjusted according to specialty needs, such as increasing the extraction accuracy of white matter lesions in neurology. The online learning frequency supports specialty customization, with higher update frequencies configured for highly variable diseases. This flexibility allows the system to adapt to differences in diagnostic criteria across different specialties.

[0118] The system provides bias interpretation tools, visually demonstrating the impact of feature changes on decision-making. The physician feedback interface supports the evaluation of the accuracy of annotated correction results. Controversial cases initiate multi-expert consultation processes, with collective decision-making results automatically used for system optimization. This collaborative model fosters a positive interaction between medical intelligence and artificial intelligence.

[0119] Bias detection is completed within 5 minutes of decision generation. Feature re-extraction takes no more than 3 minutes. Online learning uses asynchronous background updates, without affecting the real-time diagnostic process. A fast track is activated for urgent cases, prioritizing key corrective steps. Changes in diagnostic consistency before and after correction are tracked, and the improvement effect of feature updates on specific diseases is analyzed. The adoption rate of corrective results by physicians is statistically analyzed to assess the clinical acceptance of system optimization. Misdiagnosis rate trends are monitored to verify the long-term improvement in diagnostic accuracy of the closed-loop system. This clinically value-oriented evaluation mechanism ensures that system optimization always serves the core goal of improving medical quality.

[0120] Deviation detection is deployed on conventional servers, feature re-extraction runs on GPU clusters, and online learning is allocated dedicated computing nodes. Task scheduling implements dynamic prioritization, with correction requests for urgent cases being processed first. The storage system is designed with a dedicated data lake for the iterative process, retaining a complete optimization history for retrospective analysis. Energy management implements load-aware scheduling to avoid high-energy-consuming operations during peak periods.

[0121] A checkpoint mechanism is established for the remediation process, allowing recovery to the most recent stable state after unexpected interruptions. Core components are deployed in an active-active manner, automatically switching to a backup node in case of a single point of failure. Optimization parameters and model snapshots are backed up regularly to support rapid reconstruction in disaster scenarios. This design ensures that the remediation function remains available under various abnormal conditions.

[0122] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0123] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A medical intelligent analysis and auxiliary diagnosis system combining image recognition, characterized in that, The system includes: The medical image feature extraction module is used to identify anatomical structural features and lesion morphology features in medical images, extract image pixel distribution parameters and texture feature vectors, and generate a multimodal image feature set. The standard feature unit construction module, based on the multimodal image feature set, maps features to preset diagnostic dimension fields, establishes the association between anatomical structures and lesion features, and generates standardized image feature units; The diagnostic rule base matching module, based on the standardized image feature units, retrieves constraint clauses from the medical diagnostic rule base that match disease types and diagnostic indicators, calculates the number of clause matches and the logical nesting depth, and generates a rule matching complexity factor. The case association map construction module, based on multiple standardized image feature units, searches for shared anatomical structure features and lesion distribution features across cases, counts the frequency of repeated occurrences of entities and the density of category distribution, and generates a cross-case association tightness index. The comprehensive diagnostic decision-making module calculates the disease risk score and generates an auxiliary diagnostic decision-making scheme based on the multi-case association graph output by the cross-case association graph construction module and the single-item diagnostic conformity status judgment output by the diagnostic rule base matching module.

2. The medical intelligent analysis and auxiliary diagnosis system combining image recognition according to claim 1, characterized in that, The steps for obtaining the multimodal image feature set are as follows: The system segments organ regions and lesion regions in medical images, extracts gray-level histogram statistics and spatial gradient distribution parameters for each region, performs multi-scale texture filtering on the image pixel matrix, and outputs texture energy feature vectors and frequency domain response feature vectors. The system then fuses organ region features and texture feature vectors to generate a multimodal image feature set.

3. The medical intelligent analysis and auxiliary diagnosis system combining image recognition according to claim 2, characterized in that, The steps for obtaining the standardized image feature units are as follows: The organ morphology features in the multimodal image feature set are mapped to the anatomical structure standard field, and the lesion texture features are mapped to the pathological attribute standard field; the consistency of the field mapping is verified and the feature vector dimension is converted; a spatial location association identifier is established between the anatomical structure standard field and the pathological attribute standard field to generate standardized image feature units.

4. The medical intelligent analysis and auxiliary diagnosis system combining image recognition according to claim 3, characterized in that, The steps for obtaining the rule matching complexity factor are as follows: The disease type field and diagnostic indication field in the standardized image feature unit are analyzed, and logical constraint clauses are filtered in the medical diagnostic rule base. The tree-like nesting level and the number of condition judgment nodes of the matching clauses are counted. Combined with the historical reference frequency and time range coverage of the clauses, a rule matching complexity factor is generated.

5. The medical intelligent analysis and auxiliary diagnosis system combining image recognition according to claim 4, characterized in that, The steps for obtaining the single-item diagnostic compliance status determination are as follows: The rule matching complexity factor is invoked to match the constraint clause threshold, and the anatomical structure feature value and pathological attribute feature value of the image to be diagnosed are compared item by item; the continuity of the distribution of feature values ​​within the constraint threshold range is verified, and the anatomical structure conformity status and pathological attribute conformity status are generated; the two types of conformity status are fused to generate a single diagnostic conformity status judgment.

6. The medical intelligent analysis and auxiliary diagnosis system combining image recognition according to claim 5, characterized in that, The steps for obtaining the cross-case association density index are as follows: The anatomical structure feature codes and lesion distribution feature codes in multiple standardized image feature units are traversed, and entities with the same code are classified and labeled with entity category labels; the frequency of recurrence and spatial distribution density of entities of the same type in cross cases are statistically analyzed to generate a cross-case association density index.

7. The medical intelligent analysis and auxiliary diagnosis system combining image recognition according to claim 6, characterized in that, The steps for obtaining the multi-case association map are as follows: Based on the cross-case association density index, topological coding is performed on the anatomical structure feature nodes and lesion feature nodes in the standardized image feature unit; the feature similarity and spatial association strength between nodes are calculated; and a multi-layer atlas structure with feature nodes as vertices and association strength as edge weights is constructed.

8. The medical intelligent analysis and auxiliary diagnosis system combining image recognition according to claim 7, characterized in that, The steps for obtaining the disease risk score are as follows: Extract all associated paths of lesion feature nodes from the multi-case association map; calculate the single-diagnosis conformity status judgment result and path level depth corresponding to each path; weight the feature deviation and association tightness of nodes in the path to generate a disease risk score.

9. The medical intelligent analysis and auxiliary diagnosis system combining image recognition according to claim 8, characterized in that, The steps for obtaining the auxiliary diagnostic decision-making scheme are as follows: The risk level of lesion feature nodes is determined based on the disease risk score. Based on the anatomical structure association paths of the high-risk nodes in the cross-case association map, a decision scheme containing the location of high-risk lesions and the identification of associated anatomical structures is generated.

10. The medical intelligent analysis and auxiliary diagnosis system combining image recognition according to claim 9, characterized in that, The system also includes a diagnostic correction module: Based on the aforementioned assisted diagnostic decision-making scheme, the deviation parameters between the disease risk score and the medical diagnostic rule base are detected; the deviation parameters are fed back to the medical image feature extraction module for feature re-extraction; and the pathological attribute feature fields in the standardized image feature unit are iteratively updated.

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