Temporal bone disease classification method and system based on multi-modal medical image fusion technology

Abstract

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

Description

Technical Field

[0001] This invention relates to the field of image analysis, and more particularly to a method and system for classifying temporal bone diseases using multimodal medical image fusion technology. Background Technology

[0002] With the continuous development of medical imaging technology, multimodal imaging has become an indispensable tool in diagnosis and treatment planning. Temporal bone disorders, especially those related to hearing and balance, typically rely on a variety of imaging modalities, such as CT (computed tomography), MRI (magnetic resonance imaging), and PET (positron emission tomography). These different types of images provide information at different levels; for example, CT can clearly show bone structures, while MRI has unique advantages in displaying soft tissues and neural structures. By comprehensively utilizing these multimodal imaging techniques, temporal bone disorders can be analyzed and diagnosed more comprehensively and accurately.

[0003] However, traditional multimodal image analysis methods typically rely on manual intervention or single image processing techniques. While these methods can provide some diagnostic evidence, they often struggle to accurately differentiate and identify complex and elusive temporal bone lesions. Especially when dealing with large volumes of image data and subtle or overlapping lesion areas, existing techniques often face high rates of misdiagnosis and missed diagnosis. Furthermore, traditional image fusion methods mostly focus on simple image stitching or pixel-level alignment, lacking in-depth analysis of the intrinsic features of different image modalities, resulting in unsatisfactory disease classification results. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method and system for classifying temporal bone diseases using multimodal medical image fusion technology, thereby resolving at least one of the aforementioned technical issues.

[0005] To achieve the above objectives, this invention provides a method for classifying temporal bone diseases using multimodal medical image fusion technology, comprising the following steps: Step S1: Acquire multimodal images of the patient, perform adaptive distortion correction, and generate a standardized image set; Step S2: Perform layer-by-layer anatomical semantic segmentation and multimodal image fusion on the standardized image set to construct an image fusion framework; Step S3: Based on the image fusion framework, perform intelligent recognition of the fine structure of the temporal bone to construct a personalized temporal bone anatomical structure map; Step S4: Based on the personalized temporal bone anatomical structure diagram, perform tissue functional status analysis and digital pathological dynamic simulation to construct a digital pathological model; Step S5: Perform intelligent pathological feature classification based on the digital pathology model to obtain an intelligent classification report.

[0006] This specification provides a temporal bone disease classification system based on multimodal medical image fusion technology, used to perform the temporal bone disease classification method based on multimodal medical image fusion technology as described above, including: The image processing module is used to acquire multimodal images of patients, perform adaptive distortion correction, and generate standardized image sets. The image fusion module is used to perform layer-by-layer anatomical semantic segmentation and multimodal image fusion on standardized image sets, and to construct an image fusion framework. The structural recognition module is used to intelligently identify the fine structure of the temporal bone based on the image fusion framework and construct a personalized temporal bone anatomical structure map. The digital simulation module is used to analyze tissue functional status and perform digital pathological dynamic simulation based on personalized temporal bone anatomical structure diagrams, and to build digital pathological models. The intelligent classification module is used to perform intelligent pathological feature classification based on the digital pathology model to obtain an intelligent classification report.

[0007] The beneficial effects of this invention are as follows: By accurately acquiring temporal bone imaging data from different modalities (such as CT, MRI, endoscopic ultrasound, and pathological slide data), it can comprehensively cover various information in the temporal bone region, ensuring the inclusion of structural features at the bone, soft tissue, and cellular levels. Adaptive distortion correction solves the geometric distortion problem caused by different imaging modalities, enabling accurate alignment and fusion of images from different modalities, greatly improving image quality and data consistency. Density normalization eliminates density differences between different imaging modalities, allowing for effective comparison of these images under the same standard. Layer-by-layer anatomical semantic segmentation and multimodal image fusion achieve refined segmentation and identification of the temporal bone region. Layer-by-layer anatomical semantic segmentation clearly marks different anatomical structures (such as bone tissue, neural structures, and blood vessels) in the images, thereby extracting detailed information about different anatomical regions. This provides more accurate anatomical features for subsequent disease classification. Simultaneously, multimodal image fusion technology optimizes and fuses image data from different sources such as CT, MRI, and endoscopic ultrasound, maximizing the advantages of each modality and enhancing the information content and accuracy of the images.

[0008] By combining multimodal imaging data and layer-by-layer segmentation results, the fine anatomical structures of the temporal bone region can be deeply identified. By accurately identifying different anatomical structures, especially complex areas within the temporal bone (such as the cochlea, auditory nerve, and intraosseous vessels), more personalized temporal bone anatomical maps can be provided to physicians. These personalized maps not only reflect the patient's unique anatomical features but also identify potential pathological changes, such as abnormal bone hyperplasia and soft tissue tumors. Detailed functional assessments can be performed on specific structures within the temporal bone region. For example, by combining imaging data and histological information, the functional status of nerves and blood vessels within the temporal bone region can be analyzed, thereby inferring potential functional disorders, such as nerve compression or abnormal blood flow. Furthermore, through digital pathological dynamic simulation, the pathological changes in the temporal bone region can be reconstructed on a computer, simulating tissue responses under different pathological conditions.

[0009] Intelligent pathological feature classification based on digital pathology models enables accurate identification and classification of temporal bone diseases. Utilizing advanced algorithms such as deep learning, the intelligent classification system can automatically identify different types of diseases based on pathological features, such as benign tumors, malignant tumors, and inflammation. It provides rapid and efficient pathology analysis reports, avoiding misdiagnosis or missed diagnosis that may occur in traditional manual analysis. Furthermore, the intelligent classification report can integrate information from different modalities of imagery to provide more accurate disease classification results. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of a temporal bone disease classification method based on multimodal medical image fusion technology according to the present invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a detailed flowchart illustrating the implementation steps of step S2; Figure 4 This is a flowchart illustrating the detailed implementation steps of step S3. Detailed Implementation

[0011] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0012] This application provides a method and system for classifying temporal bone diseases using multimodal medical image fusion technology. The executing entities of the method and system for classifying temporal bone diseases using multimodal medical image fusion technology include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, and network upload devices that can be considered as general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio-image management system, an information management system, and a cloud-based data management system.

[0013] Please see Figures 1 to 4 This invention provides a method for classifying temporal bone diseases using multimodal medical image fusion technology, comprising the following steps: In the embodiments of the present invention, see Figure 1 This is a flowchart illustrating the steps of a temporal bone disease classification method using multimodal medical image fusion technology according to the present invention. In this example, the steps of the temporal bone disease classification method using multimodal medical image fusion technology include: Step S1: Acquire multimodal images of the patient, perform adaptive distortion correction, and generate a standardized image set; In this embodiment, multimodal medical imaging data of the patient is acquired, and geometric distortions caused by different modal imaging are corrected to generate a standardized image set. The acquired images include high-resolution temporal bone CT (e.g., Siemens SOMATOM Definition AS+, slice thickness 0.6 mm), MRI (GE Discovery MR750 3.0T, T1 / T2 sequence, slice thickness 1 mm), high-frequency endoscopic ultrasound images (using a 20MHz probe), and histopathological sections (HE-stained digital images, resolution 0.25 μm / pixel). First, under a unified anatomical coordinate system, all images are registered to a three-dimensional temporal bone structural model referenced by CT, and the corresponding spatial alignment matrix is ​​extracted. For nonlinear geometric distortions in modalities such as MRI and ultrasound, a method based on B-spline mesh registration and deformation field reconstruction is used for distortion detection, and then the distortion coefficient field is calculated. Subsequently, based on a deformation-based inference strategy, adaptive distortion correction was performed using structural fidelity metrics of the image modalities themselves (such as local structural similarity (SSIM) and gradient entropy) to ensure that the structural contours were highly consistent with the actual anatomical structure. After distortion correction, modal density calculations and standardization were further performed on each modal image, specifically including signal intensity normalization (Z-score normalization), tissue contrast enhancement (based on the CLAHE method), and spatial resolution resampling (unified to 0.5 mm³ voxels). Finally, a standardized multimodal image set with good structural consistency and uniform grayscale and texture features was generated, providing an accurate foundation for subsequent anatomical segmentation and fusion.

[0014] Step S2: Perform layer-by-layer anatomical semantic segmentation and multimodal image fusion on the standardized image set to construct an image fusion framework; In this embodiment, a standardized image set is segmented into anatomical structures at the semantic level, and a unified image fusion framework is generated through multimodal information fusion to support fine structure recognition and subsequent pathological modeling. First, CT, MRI, and ultrasound images are segmented using structure-adapted deep neural networks. For example, CT uses a ResUNet++ structure, MRI uses a 3D Attention UNet structure, and ultrasound images are combined with an edge-aware CNN module to extract the clarity of the tympanic membrane and bone boundaries. For anatomical region segmentation, the model output includes the main structures of the temporal bone (external auditory canal, tympanic membrane, malleus, incus, stapes, facial nerve canal, labyrinthine system) and spatial envelope (temporal bone cavity, shell of bone). To achieve consistent alignment of structural semantics across different modalities, a cross-modal label consistency matrix is ​​constructed. Symmetric Mutual Information is applied to constrain the segmentation output of each modality, and shape consistency regularization terms are introduced in common multimodal regions (e.g., the junction of the tympanic membrane and ossicles). Building upon this foundation, a unified image fusion framework is constructed. A multi-scale residual fusion block is employed to fuse features corresponding to different modalities, while preserving local modal differences to enhance the perception of different structures. The fusion result is represented in three-dimensional tensor form, with each voxel containing multiple modal-weighted fusion values ​​and their structural segmentation labels, forming a three-dimensional anatomical annotation atlas. This image fusion framework not only preserves the diversity of multimodal information but also achieves semantic consistency of anatomical information, laying the foundation for personalized structural recognition.

[0015] Step S3: Based on the image fusion framework, perform intelligent recognition of the fine structure of the temporal bone to construct a personalized temporal bone anatomical structure map; In this embodiment, fine structural identification of the temporal bone is performed based on the fusion results, with particular attention to the personalized identification of microstructures (such as the ossicular chain and labyrinthine system) and variant structures (such as the course of the facial nerve and the morphology of the pyramidal region). Methodologically, a Structural Attention Enhanced Net model is employed, combined with spatial location encoding of structural labels in the fusion atlas, to achieve saliency enhancement of key structures in the feature space. A multi-scale structural context encoding module introduces neighborhood anatomical information into each structural voxel, guiding the model to perform high-precision identification in blurred edge areas (such as the junction of the tympanic cavity and shell). The structural identification results are expressed as point clouds in three-dimensional space, and a complete personalized temporal bone structural model is generated through topological repair (based on MeshNet and surface reconstruction algorithms). The model also incorporates an anatomical variation detection mechanism, performing principal component analysis (PCA) on the identified structural morphology and spatially matching it with the standard anatomical model to identify morphologically abnormal areas. For example, a deviation of the facial nerve canal from the mean path exceeding 2.5 standard deviations is marked as "abnormal deviation," and a change in the connection angle of the ossicular chain exceeding 15° is marked as "connection malformation." The final output is a personalized temporal bone anatomical structure diagram, including: structure annotation diagram, structure mesh model, structure attribute table (length, curvature, density), abnormal area highlighting, etc. The visualization results can be exported as DICOM RT structure set or STL format for 3D printing, meeting the multi-dimensional needs of clinical and research use.

[0016] Step S4: Based on the personalized temporal bone anatomical structure diagram, perform tissue functional status analysis and digital pathological dynamic simulation to construct a digital pathological model; In this embodiment, based on the constructed personalized temporal bone structure map, tissue functional status analysis is performed on key structural regions. Combining multi-source functional indicators such as elastic modulus, hemodynamic parameters, and metabolic activity, a spatiotemporal evolution model of tissue state is established, and digital pathological simulation is conducted to construct a complete digital pathological model. The functional data sources required for analysis include MR elastography (MRE), ultrasound Doppler parameters (such as PI and RI), and PET metabolic data (SUV value distribution). These functional parameters are mapped to the spatial voxels of the structural model through a functional map mapping mechanism. Subsequently, a time evolution simulation module is introduced, based on ODE (ordinary differential equation) and finite element physiological simulation (FEM) methods, to simulate dynamic pathological processes such as tissue elasticity changes and labyrinthine effusion diffusion paths after tympanic membrane perforation, predicting disease progression trends. Texture features (such as local grayscale changes, edge gradient directions, and local invariance Harris response values) are extracted from the structural map to construct a three-dimensional feature map, which is then jointly modeled with the functional map. A multi-channel graph neural network (GNN) is trained to learn the intrinsic connections between pathological structures. The final digital pathology model includes an integrated structure-function atlas, a lesion risk heatmap, tissue state trend curves, and visualized pathological simulation results. This model can serve as a virtual simulation platform for visualization training and predictive analysis in various pathological scenarios, demonstrating significant clinical application value.

[0017] Step S5: Perform intelligent pathological feature classification based on the digital pathology model to obtain an intelligent classification report.

[0018] In this embodiment, based on the constructed digital pathology model, fine-grained features of the pathological region are extracted, and an end-to-end classification neural network is constructed to achieve multi-category, hierarchical, and interpretable intelligent classification of temporal bone diseases. The extracted features include multi-scale lesion texture features, structural abnormality indicators (such as morphological deviation coefficients), and functional state mutation values ​​(such as regions with sudden drops in elastic modulus), which are uniformly mapped to a cross-modal feature space to generate a unified feature representation tensor. A multi-channel attention classification network (Multi-Channel Attention CNN) is used, combined with residual structures and Transformer encoding modules, to improve the discrimination ability of complex pathological maps. The network outputs the predicted probabilities and confidence intervals for multiple disease categories, while the interpretable module (Grad-CAM++) visualizes the model's decision-making basis, enhancing the transparency of the results. Finally, the system generates an intelligent classification report, including: 1) the main disease prediction results (e.g., "cholesteatoma-type otitis media"); 2) pathological classification and disease hierarchy path; 3) a summary of structural-functional abnormality indicators; 4) model classification confidence and suggested examination measures; and 5) a visualized structural map and highlighted anatomical locations. This classification model achieved an accuracy of 93.6% and an AUC of 0.958 on the internal test set, demonstrating clinical deployability. The intelligent classification report can directly interface with hospital PACS and electronic medical record systems for seamless diagnostic assistance.

[0019] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Acquire multimodal images of the patient, including CT, MRI, endoscopic ultrasound, and histopathological section data of the temporal bone region; A three-dimensional anatomical coordinate system for the temporal bone is defined, and geometric distortion detection is performed on the multimodal images to extract the geometric distortion coefficients of different modal images. Adaptive distortion correction is performed based on the geometric distortion coefficients to obtain a distortion-corrected image. The image modal density of the distortion-corrected image is calculated, and then density normalization is performed to generate a normalized image set.

[0020] In this embodiment, the acquired data includes four main image types: computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound imaging (EUS), and histopathological slides. CT images are used to display the high-density bony structures of the temporal bone, MRI images primarily show soft tissue details and lesion areas, endoscopic ultrasound provides high-resolution images for real-time acquisition of local tissue features, and histopathological slide data serves as the "gold standard" for image comparison and verification, and for establishing lesion classification labels. During the experiment, CT scans were performed using a 64-slice spiral CT scanner with acquisition parameters set to 120 kVp, 300 mA, 1 mm slice thickness, and 0.5 mm spacing. MRI was performed using a 3.0T MRI system, with simultaneous T1-weighted and T2-weighted sequences acquired, with parameters such as TR / TE = 500 ms / 15 ms (T1) and 3000 ms / 85 ms (T2), and a slice thickness of 1 mm. EUS used a high-frequency probe (10 MHz) for multi-angle scanning, and image enhancement modes were employed to improve the contrast of soft tissue boundaries. Histopathological sections were paraffin-embedded, stained with hematoxylin and eosin (HE), and then high-resolution images (0.25 μm / pixel) were obtained using a 20x digital slide scanner (e.g., Aperio ScanScope). All image data were uniformly numbered and standardized for storage to ensure consistency and traceability across modalities. The acquisition process followed ethical approval and informed consent procedures to ensure data legality and medical ethical compliance.

[0021] To achieve spatial alignment and precise registration among multimodal images, a unified three-dimensional coordinate system needs to be established in the temporal bone anatomical region, and the geometric distortion of each modality during imaging needs to be detected. First, key anatomical structures of the temporal bone, including the external auditory canal, petrous apex, tympanic antrum, and semicircular canals, are extracted from CT images as coordinate anchor points. With the petrous apex as the origin, the X-axis is defined pointing towards the external auditory canal, the Y-axis is perpendicular to the base of the temporal lobe and pointing upwards, and the Z-axis is orthogonal to X and Y pointing backwards, forming a right-handed coordinate system. This coordinate system is used to unify the positional reference system of images under different modalities, facilitating subsequent comparative analysis. Geometric distortion detection is performed on MRI, EUS, and tissue section data. Specifically, non-uniform gradient magnetic fields in MRI may cause image stretching or compression. An error quantification method based on the phantom model is used, comparing the measured positions of known structures in MRI with their actual positions in CT, and calculating the MRI distortion coefficient matrix D_MRI(x,y,z). The nonlinear distortion caused by probe pressure and media inhomogeneity in EUS is calibrated using a simulated tissue model and a B-spline grid to obtain the EUS distortion function D_EUS(u,v). For pathological sections, considering the deformations caused by tissue stretching, compression, and rotation during section preparation, a deformation grid model and image registration residual evaluation method are used to obtain the distortion coefficients D_Hist(u,v). The distortion coefficients of each mode are recorded as transformation matrices or non-rigid deformation fields to support subsequent correction.

[0022] An adaptive distortion correction algorithm is used to geometrically correct each modality of image, aligning it spatially with the CT reference standard. This process is divided into two categories: rigid correction and non-rigid correction. For MRI and EUS images, the distortion is mainly a local nonlinear change; therefore, a non-rigid registration algorithm based on a B-spline model is used, combined with a multi-scale optimization strategy, to achieve a high degree of freedom in correcting distorted areas in local regions. Mutual information is used as a similarity metric during registration, and the displacement of control points is optimized to minimize the information entropy difference between images. For histopathological sections, a deformation field reconstruction method based on a thin-plate spline (TPS) model is used. Manually marked anatomical landmarks in the tissue sections are paired with the CT reference image to construct a non-rigid registration model, correcting macroscopic deformations caused by tissue stretching / compression. In terms of experimental parameter settings, the B-spline grid spacing was 10×10 pixels, the gradient descent optimization iterations were set to 500 steps, and the registration accuracy was evaluated by quantifying the target overlap (Dice coefficient) and mean distance error (TRE), with the target being Dice > 0.85 and TRE < 2 mm. All corrected images were remapped to a unified 3D coordinate system, preserving the original intensity values ​​and maintaining consistent spatial topology. This step effectively improved the structural correspondence of multimodal images, laying the foundation for subsequent density calculation and modal fusion.

[0023] The calculation of image modal density aims to capture the distribution characteristics of signal intensity within regions of images across different modalities and achieve cross-modal quantitative unification. First, for each distortion-corrected image, the temporal bone region is segmented and extracted at the voxel level. Semi-automatic segmentation tools (such as ITK-SNAP) are used to label structures such as the temporal bone, inner ear, and tympanic cavity. Within the segmented region, signal intensity histograms of voxels are extracted, and statistical features such as mean, standard deviation, kurtosis, and skewness are calculated to construct the modal density model for each modality. The density of CT images is expressed in Hounsfield units (HU), ranging from approximately [0, 3000]. MRI and EUS, however, lack fixed physical units, resulting in significant signal variations; therefore, standardization is necessary for unification. Specifically, the Z-score standardization method is used, which processes the signal intensity value of each voxel in each modality image. Where I represents the original pixel value, μ represents the average density of this modality in the temporal bone region, and σ represents the standard deviation. The processed signal values ​​follow a normal distribution with a mean of 0 and a standard deviation of 1. For tissue slice images, color deconvolution is used to extract tissue components stained with hematoxylin and eosin (HE), and their intensities are normalized by mapping them to the range of 0–1. Finally, all modal images are processed into a normalized multimodal imaging dataset with a unified signal density standard, which can be used for subsequent modality fusion, deep feature extraction, and classification model training. Density normalization significantly reduces the differences in signal characteristics between modalities, improves the feasibility and accuracy of multimodal information fusion, and ensures the robust recognition of different modal features by subsequent deep learning models.

[0024] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Perform layer-by-layer anatomical semantic segmentation on the standardized image set to generate layered anatomical features; Calculate the spatial resolution difference and imaging parameters of the patient's multimodal images; Based on the spatial resolution difference value and imaging parameters, the standardized image set is registered to obtain a resolution-consistent image. Based on the hierarchical anatomical structural features, multimodal image fusion is performed on resolution-uniformed images to construct an image fusion framework.

[0025] In this embodiment, high-level anatomical semantic information is extracted from standardized multimodal images to provide structural prior features for subsequent fusion and classification. Due to the complex structure of the temporal bone region, including multiple minute anatomical structures such as the cochlea, semicircular canals, tympanic cavity, auditory nerve, facial nerve canal, and mastoid air cells, a layer-by-layer segmentation approach is required, combined with a deep semantic understanding model for high-precision anatomical structure layering. A multi-channel deep convolutional neural network model with a U-Net architecture is used. The input is a 3D slice from a standardized image set, with each channel corresponding to an image modality (CT, MRI, EUS). The model shares parameters across anatomical structural regions and includes a Softmax semantic segmentation layer at the output to classify each voxel into multiple categories. The training data comes from a manually annotated gold standard dataset of temporal bone structures. Ground truth annotation is performed using the medical image registration tool ITK and a manual annotation platform (such as 3D Slicer), classifying at least eight tissue structures, including the cochlea, semicircular canals, ossicles, and facial nerve. During training, a hybrid loss function of Dice and cross-entropy is used for optimization to enhance the sensitivity to small structures. The learning rate was set to 1e-4, the batch size to 4, and the number of iterations to 500, using the Adam optimizer. After training, the model achieved an average Dice coefficient of over 0.87 on the test set, indicating high accuracy in anatomical structure segmentation. Each layer of the output anatomical structure map was encoded into a structural feature matrix and organized into a structural atlas according to anatomical levels, used to guide spatial constraints and feature matching in subsequent image fusion. In the process of multimodal image fusion, different imaging devices produce significant differences in spatial resolution due to different acquisition physical mechanisms. Without processing, this can lead to blurring and distortion in image registration and fusion. Therefore, this step first systematically analyzes the image resolution of each modality and calculates the resolution difference index between it and the reference modality. The spatial resolution of each modality is described by its voxel size, usually expressed in mm / voxel. By reading DICOM image metadata, the three-dimensional spatial resolution information of each modality is extracted. For example, the voxel size in CT is typically 0.5 × 0.5 × 1.0 mm³, in MRI it is 0.8 × 0.8 × 1.5 mm³, and in EUS it can reach 0.2 × 0.2 × 0.5 mm³, while the resolution of histopathological images is extremely high, often measured in μm (e.g., 0.25 μm / pixel). Furthermore, imaging parameters such as magnetic field strength, acquisition matrix (matrix size), FOV (field of view), slice thickness, and slice interval need to be collected to accurately calculate the actual spatial differences. Based on these parameters, a function R(x,y,z) is constructed to measure the intermodal resolution difference, defined as the ratio vector of the difference between the target modal resolution and the reference modal resolution. Modalities with excessively large differences will undergo interpolation or downsampling in subsequent steps to achieve uniformity in image spatial hierarchy.In addition, it is necessary to evaluate the ambiguity (assessed by edge strength index) and noise level (assessed by signal-to-noise ratio SNR or mean square error MSE) in each modal imaging process to provide parameter basis for setting modal weights for the fusion algorithm.

[0026] After completing the resolution difference analysis, this step performs resolution unification processing on all modal images to obtain an image set with a consistent spatial scale, avoiding structural mismatch and fusion errors caused by differences in image slice thickness and scanning intervals. To achieve this goal, image resampling is employed, interpolating based on the voxel size of each modality and the target resolution (generally CT is selected as the benchmark). The processing flow is divided into two categories: if the target modality resolution is lower than the reference modality (e.g., MRI vs. EUS vs. CT), upsampling is performed, using cubic spline interpolation to maintain image smoothness; if the target modality resolution is higher than the reference modality (e.g., pathological images), downsampling is required, using a combination of Gaussian smoothing and average pooling to reduce information loss. All images are ultimately unified to a spatial scale of 0.5 × 0.5 × 0.5 mm³, ensuring pixel consistency in structural correspondences. During registration, it is also necessary to ensure that the geometric structure of the image remains distorted after interpolation. Therefore, a local gradient-preserving constraint is introduced as an optimization condition. By comparing the changes in the edge images before and after processing, the edge fidelity error is controlled to be within 5%. In addition, the resolution uniformity effect is evaluated by calculating the structural similarity index (SSIM) and mean grayscale error (MAE) of the images. The final image set is highly uniform in spatial structure, layer thickness, and image dimension, providing a precise spatial basis for multimodal fusion.

[0027] After completing the resolution homogenization process, this step, based on the previously extracted layered anatomical features, performs multi-level, multi-channel fusion processing on images of different modalities to construct a structure-driven image fusion framework. The core idea of ​​this process is to leverage the signal advantages of each modality in specific anatomical structures and fuse their information to form a globally optimal image representation. The fusion process is divided into two stages: the first stage is structure-guided modal weighting, and the second stage is cross-modal feature mapping. In the first stage, fusion weights are set according to the signal-to-noise ratio (SNR) and tissue recognition ability of each modality in different anatomical structures. For example, CT has a weight of 0.6 in bony structures, but only 0.2 in soft tissues; MRI has a dominant weight (0.7) in neural structures; and EUS has a higher weight (0.5) in fine tissue interface regions. The weight settings are dynamically adjusted based on previous image feature statistics and experimental experience. The second stage adopts a transform domain-based fusion method, such as a combined strategy of non-subsampled contour wave transform (NSCT) and guided filter. Each modality of image is decomposed into low-frequency and high-frequency components. In the low-frequency component, the maximum weighted average (MWAM) method is used to fuse structural information, while in the high-frequency component, the local energy + region consistency criterion is used to fuse edge and texture details. All fusion operations are performed under the constraints of a layered anatomical atlas, ensuring logical consistency and spatial coordination of the fusion results at the boundaries of different structures. The fused image is a single 3D image, but it simultaneously contains the advantageous information of each modality, taking into account the structural clarity of CT, the tissue contrast of MRI, the boundary clarity of EUS, and the cellular features of pathological images. This image will serve as the core input for subsequent deep learning classification models, providing a high-dimensional and structurally rich input representation for the intelligent diagnosis of temporal bone diseases.

[0028] In this embodiment, reference Figure 4 The diagram below illustrates the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Intelligent recognition of the fine structure of the temporal bone is performed based on the image fusion framework, and the microscopic morphological features of the tympanic membrane, ossicular chain, inner ear labyrinth and facial nerve canal are extracted. The microscopic morphological features are used to identify potential pathological abnormalities, and abnormal pathological areas are marked. The degree of variation in abnormal pathological areas is assessed to obtain a degree of variation assessment value; Based on the variability assessment value and abnormal pathological areas, a personalized anatomical template model was created for the image fusion framework to construct a personalized temporal bone anatomical structure map.

[0029] In this embodiment, based on high-resolution images with completed multimodal fusion, automatic identification and feature extraction of key anatomical structures of the temporal bone are achieved in three-dimensional space, with particular attention to the fine morphology of microscopic structures such as the tympanic membrane, the ossicular chain (malleus, incus, stapes), the inner ear labyrinth (cochlea, vestibule, semicircular canals), and the facial nerve canal. Because these structures are small in size, spatially close, and often overlap, accurate identification requires the use of deep neural networks and multi-scale structural modeling techniques. A 3D segmentation network based on a multi-scale attention mechanism (such as 3D-ResUNet + AttentionModule) is used to extract spatial semantic information layer by layer from the fused images. The network input is a three-dimensional block image of 0.5 mm³ voxel size (Patch size: 64×64×64). The model introduces an anatomical attention guidance module in the intermediate layer to guide the network in learning the spatial dependencies between structures within the region. A multi-level label supervision strategy was employed to semantically segment target regions such as the tympanic membrane, ossicles, inner ear, and facial nerve canal. Parameters such as the boundary, centerline, and morphological indices (length, curvature, surface area) of each structure were recorded as structural features. To ensure recognition accuracy, a Dice + Focal Loss joint loss function was used to weight smaller structures during model training. The training run consisted of 600 epochs with a batch size of 2, and a five-fold cross-validation strategy was used. Experimental evaluation showed that on the test set, the Dice score for tympanic membrane recognition was 0.89, for the ossicular chain 0.86, for the labyrinthine structures 0.84, and for the facial nerve canal 0.81. The recognition results were output as a structure voxel label map and its corresponding morphological feature database, supporting subsequent pathological analysis and personalized modeling. Using statistical models and intelligent detection algorithms, the extracted morphological features were analyzed for potential pathological anomalies, abnormal regions were identified, and visual labeling was performed. This process combines prior knowledge of imaging with a data-driven feature variation model to determine abnormalities, focusing on the following types of abnormalities: tympanic membrane perforation or thinning, ossicular chain interruption or abnormal calcification, inner ear labyrinth malformation (such as Mondini malformation), and facial nerve canal dilation or displacement.

[0030] A statistical shape model (SSM) of normal anatomical structures was established. Using images of a healthy control group as a benchmark, 30 samples without temporal bone lesions were collected, and morphological parameters (such as volume, thickness, angle, and curvature) of each structure were extracted to construct a Gaussian distribution model in a multidimensional feature space. Subsequently, Z-scores were applied to the structural features of the patients to be analyzed. If the Z-value of any parameter exceeded ±2, a morphological abnormality was considered to exist. Furthermore, in machine learning-assisted discrimination, the Isolation Forest algorithm was introduced to assist in identification through local density anomalies among structural features, improving the detection rate of atypical cases. For identified abnormal structural regions, the system automatically generated three-dimensional visualization labels and color-coded them in the fused images (e.g., red marks indicate high-risk abnormalities). In the experiment, the sensitivity of abnormal structure identification reached 91.4%, and the specificity was 88.7%, with tympanic membrane perforation showing the highest identification rate. Through this step, preliminary intelligent diagnosis from the structural to the pathological level can be achieved, laying the foundation for variability assessment and individualized modeling. A three-dimensional structural comparison analysis and morphological deviation scoring mechanism were employed. For each identified aberrant structure, the system first rigidly aligns it with a standard anatomical model, then calculates its Shape Deviation Score. This score includes three categories of indicators: first, geometric offset, assessed by Hausdorff distance and mean surface distance (MSD), reflecting differences in the position of anatomical boundaries; second, volume change rate, representing the proportion of structural shrinkage or expansion (relative to the standard volume); and third, density change coefficient, normalized by calculating the average grayscale change (ΔHU or ΔT2 intensity) of a local region and the noise ratio. Each indicator is normalized to between 0 and 1, and a weighted average (geometric offset weight 0.4, volume change weight 0.3, density change weight 0.3) is used to form a comprehensive variation degree evaluation (VDE). For multiple structural variations, a weighted summation is used to obtain the integrated variation value. In experiments, the evaluation model demonstrated good grading ability for different pathological subtypes (such as otosclerosis, congenital ear malformation, and facial nerve palsy), automatically classifying lesions into mild, moderate, and severe levels.

[0031] Based on fused images and recognition results, voxel reconstruction and isosurface generation algorithms (such as Marching Cubes) are used to perform 3D modeling of the fine structure of the temporal bone. Secondly, deviations in variable regions (such as surface displacement vectors and abnormal volume proportions) are mapped to the 3D model using color gradients or mesh deformation to visualize lesions. Thirdly, structural labeling and variation annotation modules are introduced to construct an interactive digital model, including anatomical hierarchical navigation, structural attribute display, and lesion summaries. The personalized template output format is compatible with medical image standards (such as DICOM-RT Structure Set or STL format) and can be imported into clinical image systems (such as PACS) or 3D printing platforms. The system also provides a comparison function, allowing individual templates to be compared with healthy standard models or population models (Atlas) to assess their feature dispersion within the population. Personalized modeling takes no more than 5 minutes, with an average reconstruction accuracy error of less than 0.6 mm. It also serves as an individual prior model in multimodal classification algorithms, participating in feature enhancement and classification assistance, significantly improving the accuracy and interpretability of subsequent disease classification tasks.

[0032] In this embodiment, step S4 includes the following steps: Based on personalized temporal bone anatomical structure diagrams, tissue functional status analysis is performed to extract tissue status information, including tissue elastic modulus, hemodynamic parameters, and metabolic activity. Real-time status changes and evolution of organizational status information are analyzed to generate a real-time profile of the organizational status. The personalized temporal bone anatomical structure map is decomposed into structural map features, and tissue texture features, edge gradient information, and local invariance coefficients are extracted and fitted to obtain the structural map feature set. Digital pathology dynamic simulation is performed based on real-time tissue state profiling and structural diagram feature sets to construct a digital pathology model.

[0033] In this embodiment, structural and functional data are fused, and based on a personalized three-dimensional temporal bone anatomical structure map, the biomechanical and physiological functional status of local tissues is quantitatively analyzed to obtain important parameters reflecting disease activity. Three core tissue state indicators are elastic modulus, hemodynamic indices, and metabolic activity, which together constitute the tissue state information set. The acquisition of elastic modulus mainly relies on elastography data (such as magnetic resonance elastography (MRE) or acoustic radiation force pulse imaging (ARFI). By applying low-frequency vibrations to the target tissue in the fused image, the shear wave propagation velocity is collected, and tissue stiffness is inferred from this. Taking MRE as an example, acquisition is performed on a 3.0T MRI system with a driving frequency set to 60 Hz and a reconstructed image resolution of 1.5 mm³. An inversion algorithm is used to calculate the shear modulus of each voxel, thereby obtaining the elasticity map of the temporal bone soft tissue region and identifying pathological manifestations such as middle ear mucosal fibrosis and facial nerve perineurosis. Hemodynamic parameters are obtained through DCE-MRI (Dynamic Enhanced MRI) or CT perfusion scans. Perfusion rate, blood volume, and mean transit time (MTT) are extracted from the region of interest to assess whether cochlear blood supply is restricted and whether microvascular dilation exists in inflammatory areas. Acquisition parameters include an injection dose of 0.1 mmol / kg contrast agent at a rate of 3 ml / s, a sequence acquisition interval of 1.5 s, and a total of 60 frames acquired. Parameter calculations are performed using the Fick principle. Metabolic activity assessment relies on PET-CT images (or fMRI metabolic-related sequences). Standard uptake values ​​(SUVs) are used to map the glucose metabolism levels of different anatomical regions, identifying hypermetabolic lesions such as inflammatory granulation tissue and tumor-active areas. Projecting these functional parameters back into personalized structural maps allows for precise analysis and localization of the biological functional status of each anatomical unit.

[0034] Based on multi-temporal imaging sequences (such as multi-timepoint DCE-MRI or sequential PET imaging), the changes of tissue state indicators (such as elastic modulus, perfusion rate, and metabolic rate) over time are curve-fitted and trend-modeled. Local weighted regression (LOESS) is used to smoothly fit the state time series of each tissue unit and construct its first derivative mapping to reflect the rate of change. Curvature analysis is used to predict future trends. Furthermore, a dynamic modeling framework based on graph convolutional networks (GCN) is introduced, treating each anatomical structure node in the personalized structural map as a graph node and the time state sequence as a node attribute. Dynamic evolution modeling is performed under structural connectivity constraints to capture inter-regional state linkages and abnormal propagation paths. For example, decreased elasticity in the tympanic membrane region accompanied by increased blood flow in the tympanic cavity may predict the progression of otitis media. Finally, all temporal information is fused using a four-dimensional mapping (x, y, z, t) to generate a color functional dynamics map, where color represents state type, brightness represents the magnitude of change, and edges represent the rate of spatial expansion. Experimental results show that this real-time profile can predict changes in ear disease symptoms 1-2 weeks in advance, helping doctors to judge the response to treatment or the progression of the disease in clinical practice.

[0035] First, texture analysis was performed, using the Gray-Level Co-occurrence Matrix (GLCM) and Wavelet Transform to extract texture features from various anatomical regions of the temporal bone, including indicators such as energy, entropy, contrast, and homogeneity. GLCM calculations used directional angles (0°, 45°, 90°, 135°) with a pixel distance of 2, and the sampling range covered the entire structural surface. Texture features reflect the consistency and orderliness of the tissue's microstructure, which is of great value in identifying pathological calcification, hyperplasia, or cystic changes. Second, edge gradient information was extracted. The Sobel operator and Laplacian filtering were used to enhance the edges of each structure in the fused image, and a complete edge gradient map was constructed using Canny edge detection. The rate of change of structural edges was used as an important feature to assess structural integrity, such as the risk of stapes footplate fracture or the irregularity of edges in the tympanic membrane perforation area. The third category is Local Invariant Descriptors, including SIFT, SURF, and ORB feature point descriptors, used to maintain the stability of local structures under spatial rotation, scale changes, or modal differences. Under multimodal conditions, ORB features exhibit stable performance and high efficiency, extracting an average of no less than 300 feature points per structural region. These features are then subjected to dimensionality reduction processing using Principal Component Analysis (PCA), ultimately forming a structural graph feature vector of approximately 128 dimensions for each structure. This vector constitutes the overall structural feature set, serving as the core input features in digital pathology modeling.

[0036] The model is constructed using an integrated Multimodal Graph Neural Network (MM-GNN) framework. Node inputs are structural graph feature vectors, and edge attributes are jointly defined by anatomical adjacency and functional covariance. The state evolution module incorporates a temporal embedding mechanism to dynamically update the elasticity, blood flow, and metabolic state of each tissue unit, simulating the pathological trajectory. The simulation process sets the initial state as the actual pathological state in the current fused image. Pathological driving parameters include an inflammatory index (increased metabolism + decreased elasticity) and a structural weakening index (increased texture entropy + weakened edge gradient). Through a discrete evolution mechanism with a time step of Δt = 3 days, the model calculates the joint update of the structural-functional state in each iteration, outputting a predicted future state map for each structure. Simulation results include a lesion area expansion trend map, a structural and functional degradation prediction map, and a multi-timepoint image reconstruction sequence. In experiments, during a disease course simulation test on 20 patients with otitis media, the model's prediction accuracy reached 88.5%, and the average lesion boundary fitting error was less than 1.2 mm, indicating its usability in clinical auxiliary prediction.

[0037] In this embodiment, the specific steps of step S5 are as follows: Local receptive field identification is performed on digital pathology models to extract local detailed texture information; Multi-scale feature pyramids are constructed from local detail texture information to obtain multi-scale attention features; Cross-modal space mapping alignment is performed based on multi-scale attention features to generate a unified feature space representation; Based on the unified feature space representation, end-to-end feature training is performed on the digital pathology model to construct an intelligent feature classification model; The patient information to be classified is input into the intelligent feature classification model to perform intelligent pathological feature classification and obtain an intelligent classification report.

[0038] In this embodiment, a deep perception analysis is performed on the constructed digital pathology model. The local receptive field mechanism is used to identify microscopic changes in pathological texture, with particular emphasis on refining the local texture features of structures such as the edge of tympanic membrane perforation, ossicular wear areas, inner ear calcifications, and abnormal neural tube pathways. The local receptive field design is based on the fundamental principles of convolutional neural networks (CNNs), capturing detailed changes at different spatial scales through multi-layered convolutional kernels and sliding window operations. The input is a high-resolution sub-map (patch) of each structural region in the digital pathology model, with a patch size of 64×64×64 voxels. Multi-scale sliding convolutions are performed with kernel sizes ranging from 3×3×3 to 7×7×7, a stride of 1, and zero-padding to maintain spatial dimensions. A three-dimensional feature response map is constructed using a multi-channel input mechanism (each channel representing a type of tissue state, such as metabolic rate and elastic modulus), extracting local texture features at each location, such as cell density, boundary irregularities, and local gray-level heterogeneity. To enhance the model's focus on local regions, a spatial attention module is introduced. This module adjusts the receptive field using location-sensitive weights, guiding the model to prioritize lesion edges and high-risk areas. During training, label-guided region sampling is employed, focusing on training samples from suspected lesion areas to improve the ability to identify fine-grained pathological features. This step provides fundamental, highly discriminative local information for subsequent feature pyramid construction and cross-modal alignment.

[0039] A multi-scale feature pyramid (FPN) is constructed, and a multi-scale attention mechanism is introduced to obtain global-local joint feature representations under a hierarchical structure. This method can effectively capture morphological, density, and texture feature changes at different scales among complex temporal bone tissues, and is particularly suitable for identifying morphological variations and edge evolution features in lesion areas. During the construction process, the local texture feature map extracted in the previous stage is divided into five scales (L1~L5), each corresponding to 1 / 1, 1 / 2, 1 / 4, 1 / 8, and 1 / 16 of the original image resolution, respectively. Downsampling and upsampling are achieved through max pooling and deconvolution operations. In each scale layer, a channel attention module and a spatial attention module are introduced to work together to enhance the response to key features while suppressing redundant or background interference information. The feature fusion part adopts a lateral connection mechanism to adaptively weight and fuse low-level detail information with high-level semantic information to form a multi-scale attention feature map. Furthermore, a hierarchical supervision mechanism is introduced during the training phase, with independent loss backpropagation (deep supervision) performed on each scale layer to enhance the model's ability to learn features of different granularities. In the experiments, a multi-scale pyramid network was constructed using the FPN+SE (Squeeze-and-Excitation) structure, achieving an average feature recognition accuracy improvement of approximately 8.3% in the pre-training task of temporal bone lesion classification. The multi-scale attention features output by this module will serve as the core input for subsequent spatial alignment and unified feature mapping.

[0040] A cross-modal feature mapping framework based on contrastive learning is adopted, constructing a modality contrastive loss to minimize the distance between feature vectors from different modalities but belonging to the same structural region in high-dimensional space, while maintaining sufficient discriminative power for vectors from different structural regions. Positive / negative pairs are used to guide the model to learn modality-independent feature representations. In terms of spatial mapping structure, a two-stream network design is introduced, consisting of a shared-weight encoder and a modality-specific adapter. The former extracts general structural features, while the latter adjusts modality-specific perturbations, achieving distribution alignment after feature fusion. To improve spatial consistency, a geometric alignment module based on a Thin-PlateSpline (TPS) deformation kernel is also embedded to ensure structural overlap and morphological correspondence between multiple modalities in the spatial coordinate system. During training, a contrastive loss function with a margin of 1.0 is set, and the AdamW optimizer is used for 200 training epochs with a learning rate of 1e-4. Batch normalization and Dropout (p=0.5) strategies are employed to prevent overfitting. This unified representation not only eliminates the information silos between modalities, but also provides a basis for modal collaborative input for feature classification models.

[0041] This model enables automatic identification and accurate classification of temporal bone pathological features. It integrates three key modules: feature encoding, feature fusion, and classification prediction. Employing a combined architecture of a Fully Convolutional Network (FCN) and a Transformer structure, it achieves deep semantic understanding of structural features and multi-class prediction of pathological labels. In terms of model structure, the bottom layer uses 3D-ResNet to extract spatial structural features, while the middle layer uses a Transformer encoder to handle long-distance dependencies and model inter-structural contextual information. The top classifier layer provides multi-label softmax output, supporting concurrent classification of various pathological types, including otosclerosis, congenital ossicular malformation, cholesteatoma, middle ear effusion, and facial nerve canal stenosis. End-to-end training employs a combination of cross-entropy loss and focal loss to address class imbalance. Training data is divided into training and validation sets in an 8:2 ratio, with 300 training epochs, a batch size of 4, and the RAdam optimizer. The initial learning rate is 1e-3, and the dynamic decay coefficient is 0.95. Data augmentation techniques included random rotation, mirroring, modal perturbation, and boundary blurring to enhance the model's generalization ability to clinical variations. Model evaluation metrics included accuracy, F1 score, AUC (area under the curve), and recall. On the independent test set (N=120 cases), the model achieved an average accuracy of 92.6% and an AUC of 0.943. This model possesses end-to-end inference capabilities, requires no manual intervention, and can be directly used in automated diagnostic processes for clinical data.

[0042] The input data includes a fused image set processed with resolution uniformity, structural map reconstruction, and pathological state annotation. Image standardization and feature encoding are performed through the model preprocessing module, followed by pathological feature classification by the model. Classification results are output per anatomical region, including pathological type, lesion probability score (e.g., cholesteatoma: 87% confidence), highlighted key lesion areas, and model discrimination criteria (e.g., structural bias + overlap of metabolically active regions). The classification report is presented in a richly illustrated format, combining 3D anatomical diagrams, lesion heatmaps, and text annotations, including the following core contents: 1) Structural identification results; 2) Pathological type determination; 3) Description of structural abnormalities in each lesion region; 4) Classification confidence and reference thresholds; 5) Suggested clinical examination / treatment directions. The report generation time is less than 30 seconds, with real-time feedback capability.

[0043] In this embodiment, the specific steps for inputting the patient information to be classified into the intelligent feature classification model, performing intelligent pathological feature classification, and obtaining an intelligent classification report are as follows: The patient information to be classified is input into the intelligent feature classification model, the temporal bone disease spectrum is analyzed hierarchically, and intelligent pathological feature classification is performed to obtain the intelligent pathological classification result. Uncertainty quantification modeling of intelligent pathology classification results is performed to construct a dynamic adjustment mechanism for confidence intervals; The confidence interval adjustment mechanism was sampled and validated, and the statistical significance test results were obtained. Based on the statistical significance test results, keyity cross-validation is performed, and the intelligent pathology classification results are adaptively adjusted to obtain an intelligent classification report.

[0044] In this embodiment, the fused images of the actual patients to be classified are input into the intelligent feature classification model. This model not only outputs disease classification results but also performs hierarchical analysis of the disease spectrum, providing more in-depth pathological discrimination criteria. The so-called hierarchical analysis of the disease spectrum refers to embedding pathological types into the hierarchical structure of temporal bone-related diseases, simulating the systematic association of diseases at the levels of etiology, pathogenesis, lesion location, and tissue manifestation, and generating a disease hierarchy prediction atlas. The input data is a unified feature representation after structural graph reconstruction, feature extraction, and multimodal alignment. The model first performs category identification on global features, and then performs fine-grained classification cascades based on the internal disease ontology tree. For example, the model first classifies it as "conductive hearing loss disease," then refines it to the "middle ear lesion" direction, and further analyzes it into specific subtypes such as "cholesteatoma" or "otosclerosis." This process uses a hierarchical multi-label classification network, combined with the disease relationship structure defined in a priori medical ontology (such as UMLS), to automatically complete the classification at each level. The model output includes: 1) the most likely disease name and category; 2) the phylogenetic tree path (the reasoning path from the highest level to the lowest node); 3) the confidence score for each level of classification; and 4) a heatmap of the main lesion structures (ClassActivation Map). Taking a patient with facial nerve canal malformation as an example, the system output path is: "Neuro-hearing impairment" → "Facial nerve canal lesion" → "Congenital facial nerve canal stenosis," with a final confidence score of 92.1%. This hierarchical discriminative structure greatly improves the system's ability to interpret complex conditions, enhancing clinical readability and reasoning transparency.

[0045] The method employed is Monte Carlo Dropout, a technique within the Bayesian Deep Learning framework. During the model inference phase, the Dropout mechanism is retained, performing multiple forward propagations (typically T=100 times) under the same input conditions, collecting the classification probability distribution for each iteration. By analyzing the mean and variance of these distributions, a confidence interval (CI) for each class can be derived, and a quantitative representation of the uncertainty of the classification results can be constructed accordingly. Furthermore, an adaptive confidence estimation strategy is introduced, adjusting the confidence limits based on the distribution shape of the classification results. If the predicted distribution is concentrated (small variance), the confidence interval narrows; if the predicted distribution is dispersed, the confidence interval widens, indicating a potential risk. This mechanism dynamically responds to the model's uncertainty regarding different cases and the fusion of features from different modalities. For example, in one case of tympanic membrane perforation, the model output was "Acute suppurative otitis media, confidence level 82%, CI = [75.3%, 89.6%]"; another case was "Inner ear labyrinth abnormality," but the CI was [42.1%, 78.5%], indicating a high degree of uncertainty in the judgment, requiring further confirmation by a doctor using other examinations. This mechanism enhances the transparency and clinical safety of AI classification results, providing important quantitative evidence for subsequent statistics and validation.

[0046] The classification results were systematically evaluated through sampling tests and statistical modeling to confirm whether the confidence interval coverage met the expected confidence level (e.g., 95% coverage corresponds to a 95% confidence interval). This process incorporated Monte Carlo sampling and hypothesis testing strategies to ensure the statistical reliability of the model's uncertainty representation. N=500 case samples were randomly sampled from the test set, and the classification model was run 100 times for each sample to obtain the confidence distribution and predicted labels. In the statistical analysis phase, the actual coverage probability (CP) of each category within the 95% confidence interval was examined to see if it significantly deviated from the theoretical confidence level. If the actual CP was significantly lower than the theoretical value, it indicated that the model was overconfident; if the CP was too high, it indicated that the model was too conservative. For hypothesis testing, a binomial test or bootstrap resampling method was used to evaluate the confidence interval coverage for each disease category. Taking cholesteatoma lesions as an example, with a theoretical confidence level of 95%, the proportion of correct classifications falling within the predicted confidence interval (CI) in the actual sampling results was 93.2%, with a difference of 1.8%. The p-value of the Bootstrap test was 0.076, which did not reach the significant difference threshold (α=0.05), indicating that the confidence mechanism possesses statistical stability. Furthermore, a correlation regression model between classification confidence intervals and actual misclassification rates was established to verify its discriminative reference value. Experimental results showed that a high confidence interval width significantly predicted the risk of misclassification (r² = 0.71, p<0.001). Through this sampling validation process, the system can screen out classification results with high reliability while marking unstable judgments, improving the reliability and clinical interpretability of the classification results.

[0047] Further, a "criticality cross-validation" strategy is introduced. This involves combining the confidence score of the classification output, the degree of structural anomaly, and changes in functional indicators to perform logical consistency verification and adaptive adjustment on the preliminary classification results, ultimately generating an intelligent classification report with diagnostic suggestions and visual representations. Criticality refers to the core position of a classification result in the overall judgment logic chain. For example, a lesion like "facial nerve stenosis" appearing repeatedly and with significant weight in multiple classification paths is considered a high-criticality node. Based on the model's judgment results, the system performs feature-level cross-validation by combining anomaly intensity scores (such as edge change rate and density deviation) and functional state anomalies (such as blood flow anomaly scores and metabolic increment values) in the structural diagram. If there are cases where the classification label confidence is low but the key features are highly abnormal (e.g., CI=65%, but structural anomaly score>0.85), the system triggers an adaptive classification adjustment mechanism. This mechanism recalculates the category probability distribution through confidence weight reconstruction and joint structural-functional scoring, thus correcting the classification output. This process employs a Bayesian weighted fusion strategy, using structural criticality as a priori to improve the interpretability of the model's decisions. The final intelligent classification report includes: 1) main classification results and phylogenetic path; 2) classification confidence intervals and uncertainty indicators; 3) summary of key structure and function scores; 4) adaptive adjustment explanation and comparison with the original classification; and 5) clinical suggestions and visual highlights. This report supports structured output (PDF, HTML) and DICOM-SR integration, facilitating physician viewing, printing, and archiving.

[0048] In this embodiment, a temporal bone disease classification system based on multimodal medical image fusion technology is provided, for performing the temporal bone disease classification method based on multimodal medical image fusion technology as described above, including: The image processing module is used to acquire multimodal images of patients, perform adaptive distortion correction, and generate standardized image sets. The image fusion module is used to perform layer-by-layer anatomical semantic segmentation and multimodal image fusion on standardized image sets, and to construct an image fusion framework. The structural recognition module is used to intelligently identify the fine structure of the temporal bone based on the image fusion framework and construct a personalized temporal bone anatomical structure map. The digital simulation module is used to analyze tissue functional status and perform digital pathological dynamic simulation based on personalized temporal bone anatomical structure diagrams, and to build digital pathological models. The intelligent classification module is used to perform intelligent pathological feature classification based on the digital pathology model to obtain an intelligent classification report.

[0049] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0050] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for classifying temporal bone diseases using multimodal medical image fusion technology, characterized in that, Includes the following steps: Step S1: Acquire multimodal images of the patient, perform adaptive distortion correction, and generate a standardized image set; Step S2: Perform layer-by-layer anatomical semantic segmentation and multimodal image fusion on the standardized image set to construct an image fusion framework; Step S3: Based on the image fusion framework, perform intelligent recognition of the fine structure of the temporal bone to construct a personalized temporal bone anatomical structure map; Step S4: Based on the personalized temporal bone anatomical structure diagram, perform tissue functional status analysis and digital pathological dynamic simulation to construct a digital pathological model; Step S5: Perform intelligent pathological feature classification based on the digital pathology model to obtain an intelligent classification report.

2. The temporal bone disease classification method based on multimodal medical image fusion technology according to claim 1, characterized in that, The specific steps of step S1 are as follows: Acquire multimodal images of the patient, including CT, MRI, endoscopic ultrasound, and histopathological section data of the temporal bone region; A three-dimensional anatomical coordinate system for the temporal bone is defined, and geometric distortion detection is performed on the multimodal images to extract the geometric distortion coefficients of different modal images. Adaptive distortion correction is performed based on the geometric distortion coefficients to obtain a distortion-corrected image. The image modal density of the distortion-corrected image is calculated, and then density normalization is performed to generate a normalized image set.

3. The temporal bone disease classification method based on multimodal medical image fusion technology according to claim 1, characterized in that, The specific steps of step S2 are as follows: Perform layer-by-layer anatomical semantic segmentation on the standardized image set to generate layered anatomical features; Calculate the spatial resolution difference and imaging parameters of the patient's multimodal images; Based on the spatial resolution difference value and imaging parameters, the standardized image set is registered to obtain a resolution-consistent image. Based on the hierarchical anatomical structural features, multimodal image fusion is performed on resolution-uniformed images to construct an image fusion framework.

4. The temporal bone disease classification method based on multimodal medical image fusion technology according to claim 1, characterized in that, Step S3 is as follows: Intelligent recognition of the fine structure of the temporal bone is performed based on the image fusion framework, and the microscopic morphological features of the tympanic membrane, ossicular chain, inner ear labyrinth and facial nerve canal are extracted. The microscopic morphological features are used to identify potential pathological abnormalities, and abnormal pathological areas are marked. The degree of variation in abnormal pathological areas is assessed to obtain a degree of variation assessment value; Based on the variability assessment value and abnormal pathological areas, a personalized anatomical template model was created for the image fusion framework to construct a personalized temporal bone anatomical structure map.

5. The temporal bone disease classification method based on multimodal medical image fusion technology according to claim 1, characterized in that, The specific steps of step S4 are as follows: Based on personalized temporal bone anatomical structure diagrams, tissue functional status analysis is performed to extract tissue status information, including tissue elastic modulus, hemodynamic parameters, and metabolic activity. Real-time status changes and evolution of organizational status information are analyzed to generate a real-time profile of the organizational status. The personalized temporal bone anatomical structure map is decomposed into structural map features, and tissue texture features, edge gradient information, and local invariance coefficients are extracted and fitted to obtain the structural map feature set. Digital pathology dynamic simulation is performed based on real-time tissue state profiling and structural diagram feature sets to construct a digital pathology model.

6. The temporal bone disease classification method based on multimodal medical image fusion technology according to claim 1, characterized in that, The specific steps of step S5 are as follows: Local receptive field identification is performed on digital pathology models to extract local detailed texture information; Multi-scale feature pyramids are constructed from local detail texture information to obtain multi-scale attention features; Cross-modal space mapping alignment is performed based on multi-scale attention features to generate a unified feature space representation; Based on the unified feature space representation, end-to-end feature training is performed on the digital pathology model to construct an intelligent feature classification model; The patient information to be classified is input into the intelligent feature classification model to perform intelligent pathological feature classification and obtain an intelligent classification report.

7. The temporal bone disease classification method based on multimodal medical image fusion technology according to claim 6, characterized in that, The specific steps for inputting patient information to be classified into the intelligent feature classification model, performing intelligent pathological feature classification, and obtaining an intelligent classification report are as follows: The patient information to be classified is input into the intelligent feature classification model, the temporal bone disease spectrum is analyzed hierarchically, and intelligent pathological feature classification is performed to obtain the intelligent pathological classification result. Uncertainty quantification modeling of intelligent pathology classification results is performed to construct a dynamic adjustment mechanism for confidence intervals; The confidence interval adjustment mechanism was sampled and validated, and the statistical significance test results were obtained. Based on the statistical significance test results, keyity cross-validation is performed, and the intelligent pathology classification results are adaptively adjusted to obtain an intelligent classification report.

8. A temporal bone disease classification system based on multimodal medical image fusion technology, characterized in that, A method for classifying temporal bone diseases using the multimodal medical image fusion technology as described in claim 1, comprising: The image processing module is used to acquire multimodal images of patients, perform adaptive distortion correction, and generate standardized image sets. The image fusion module is used to perform layer-by-layer anatomical semantic segmentation and multimodal image fusion on standardized image sets, and to construct an image fusion framework. The structural recognition module is used to intelligently identify the fine structure of the temporal bone based on the image fusion framework and construct a personalized temporal bone anatomical structure map. The digital simulation module is used to analyze tissue functional status and perform digital pathological dynamic simulation based on personalized temporal bone anatomical structure diagrams, and to build digital pathological models. The intelligent classification module is used to perform intelligent pathological feature classification based on the digital pathology model to obtain an intelligent classification report.

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