2747 results about "Lesion" patented technology

The invention provides an esophageal squamous carcinoma detection method and system based on combination of multi-modal information fusion and artificial intelligence, and relates to the technical field of detection. And inputting the fusion feature map corresponding to the multi-modal image information into a pre-trained esophageal squamous carcinoma detection model to perform focus area detection and segmentation on the fusion feature map, and based on a detection result, performing benign and malignant classification and staging prediction on a detected focus by using an integrated learning method in combination with a classification algorithm to obtain the esophageal squamous carcinoma. Outputting a classification result and a visual heat map; converting the extracted focus area information into three-dimensional point cloud data by using an improved point cloud reconstruction algorithm; performing space division and noise reduction processing on the three-dimensional point cloud data by adopting a self-adaptive octree structure; and generating a smooth lesion three-dimensional model by using an improved Poisson surface reconstruction algorithm in combination with a curved surface subdivision technology.

The present invention provides a catheter for treating occlusions in blood vessels. An exemplary catheter for treating occlusions in blood vessels comprises a tubular inner member including a base segment with a first lumen defining a fluid inlet port, and a second lumen defining a fluid outlet port. An extension segment is distal to the base segment. The extension segment has a reduced cross-section. An emitter assembly includes a first insulated wire extending through the second lumen and a second insulated wire, and a conductive sheath wrapped circumferentially around the first insulated wire, the second insulated wire, and the extension segment. A cap or balloon is sealably attached to the distal end of the catheter and surrounds the emitter assembly, said cap or balloon being fillable with conductive fluid.

The invention discloses an efficient skin disease segmentation method based on multiple scales and a mixed attention mechanism, and the method comprises the steps: firstly carrying out the preprocessing of a skin mirror image through data enhancement, and then inputting the skin mirror image into a convolutional neural network for feature extraction, thereby obtaining a skin lesion segmentation region prediction map. According to the method, the convolutional neural network is applied, multi-scale features of local information and global information can be extracted from a skin mirror image, and the feature representation capability is enhanced through feature fusion. The objective of the invention is to solve the problems of changeable lesion areas, irregular shapes and the like of skin disease images, and improve the accuracy and robustness of skin disease image segmentation.

The invention discloses a deep interpretable skin disease identification method based on small sample learning, which belongs to the field of image processing, and comprises the following steps of: respectively inputting similar or heterogeneous skin disease images into a double-branch small sample contrast learning network, segmenting a lesion area of the skin disease image to generate lesion segmentation masks, extracting shape information in the lesion segmentation masks, and identifying the shape information of the lesion segmentation masks; positioning a minimum square frame containing a lesion area, and performing clipping and zooming to obtain a lesion image; extracting the depth features of the branches corresponding to the lesion images of the two branches, calculating the contrast loss of the depth features of the double branches to optimize network parameters, and extracting multi-scale texture information at the same time; metadata containing shape information and texture information and depth features of an image are subjected to multi-modal fusion for skin disease identification, and through combination of a multi-stage deep learning network and medical diagnosis logic, features such as form and texture of a lesion area are extracted and fused step by step. The classification accuracy of skin disease identification and the interpretability of the model are improved.

The invention relates to the technical field of disease and pest identification and control, in particular to a disease and pest identification and control early warning system and method, and the system comprises a data collection module, a polarization data analysis module, an image super-resolution processing module, a feature extraction module, a disease prediction module and a decision support module. According to the method, early recognition and accurate prediction of plant diseases and insect pests are achieved by integrating capture and analysis of spectrum polarization data, image super-resolution processing and time sequence analysis, fine structural changes of plant leaves are accurately captured through the polarized light technology, fine lesion features can be revealed by deeply analyzing the polarization data, and the accuracy of the plant diseases and insect pests is improved. The local spectrum enhancement technology further refines the image quality, it is ensured that the change of the cellular level can be accurately recognized, deep learning analysis is carried out in combination with environmental data, the disease development trend can be effectively predicted, and the processing speed and accuracy are improved.

The invention provides an agricultural pest early warning method and system based on big data, and the method comprises the steps: firstly obtaining a crop leaf image set of a target farmland region from farmland image big data, and carrying out the leaf region segmentation of the crop leaf image set, so as to distinguish a healthy region from a potential lesion region; performing disease feature extraction on the potential lesion area image to generate a key disease feature set, performing abnormal state detection on the key disease feature set by using a pre-trained disease and pest recognition model, determining a disease and pest type and predicting a diffusion trend of the disease and pest type; and finally, based on the disease and insect pest type identification and the diffusion trend prediction data, generating a disease and insect pest early warning instruction containing geographic positioning information, and sending the instruction to a farmland management system to trigger prevention and control response operation, thereby realizing accurate monitoring and early warning of crop diseases and insect pests.

The invention discloses a coronary angiography image blood vessel segmentation system based on multi-scale feature fusion. According to the invention, through the innovative design of the adaptive morphological sensing module, the system can dynamically analyze the anatomical structure characteristics of the blood vessel: the differentiable morphological operation layer converts the corrosion expansion operation into a learnable feature extraction process, so that the network can autonomously identify the gradient change and boundary trend of the blood vessel wall; the dynamic nuclear adaptation mechanism adjusts the scale and direction of morphological operation in real time according to the local blood vessel diameter and curvature characteristics, interference of surrounding tissues in a main blood vessel area can be inhibited, and continuous expression can be enhanced for capillary branches. Through deep fusion of dissection driving and data driving, the topological structure integrity of a segmentation result at a blood vessel bifurcation point and a narrow lesion area is remarkably improved, and the common problems of blood vessel fracture and misconnection in a traditional method are effectively avoided.

A system to provide endovascular support for patients. The system includes a processor configured to receive, from a medical imaging device, a pre-treatment vessel roadmap and medical images acquired in response to an endovascular therapy performed on a patient, and obtain endovascular treatment parameters, including image-based endovascular treatment parameters based on features of the medical images. Such endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient. The processor is further configured to predict tissue characteristics of a first lesion based on the endovascular treatment parameters and generate a map of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

The invention relates to the technical field of sound wave measurement, in particular to an ultrasonic image diagnosis system and method.According to the ultrasonic image diagnosis system and method, matching between reflection characteristics and tissue characteristics is made to have the self-adaptive learning ability through a deep neural network, the targeted recognition effect is improved in the aspect of signal classification, and based on the judgment result of the reflection characteristics, the diagnosis accuracy is improved. When boundary partitioning is carried out on a tissue area, the edge structure is judged by using the combination of three parameters of gray range, gradient direction and texture continuity, contour fuzziness caused by single index judgment is avoided, and the boundary partitioning accuracy is improved by extracting a gray distribution center, an amplitude change track and an edge area continuous change sequence. And the region positioning result is input into a support vector machine, accurate recognition of lesion properties is realized according to boundary classification comparison of morphological structure quantitative features and historical benign and malignant feature data, secondary verification of the structural form is performed after the image is formed, and the diagnosis integrity and the judgment confidence are effectively improved.

The invention relates to the technical field of region segmentation, in particular to a kidney ultrasonic image lesion region segmentation method. The method comprises the following steps: acquiring a tracking pixel sequence of feature points of a kidney region of an image to be analyzed, acquiring a cyst possible value according to the similarity of gray variation trends of each feature point and adjacent feature points and the length of the tracking pixel sequence, and further selecting cyst feature points in the kidney region; classifying the cyst feature points according to the distance between the pixel points at the same position in the tracking pixel sequence of any two cyst feature points and the similarity between the gray difference and the gray change trend; and according to the distance between each cyst feature point and the other cyst feature points of the category to which the cyst feature point belongs and the cyst possible value, obtaining a region growth threshold value, and taking the cyst feature points as seed points for region growth to obtain an actual cyst region. According to the scheme, the kidney cyst division precision is effectively improved.

The invention discloses an MRI medical image segmentation method and system based on deep learning, and the method comprises the steps: obtaining a mammary gland MRI image, carrying out the standardization processing of the mammary gland MRI image, and removing a non-region of interest to obtain a target MRI image; performing detail enhancement on the suspected lesion area to reconstruct an image to be preprocessed; performing noise reduction processing, contrast enhancement and edge enhancement preprocessing on the to-be-preprocessed image to generate three versions of input images, and inputting the three versions of input images into a pre-trained deep learning model as three channels to complete breast tumor identification; and carrying out image segmentation on the breast tumor region, and classifying segmented breast tumors. According to the MRI medical image segmentation method and system based on deep learning provided by the invention, accurate and efficient processing of the mammary gland MRI image is realized, efficient identification, accurate segmentation and accurate classification of the mammary gland tumor are realized, the accuracy and robustness of mammary gland tumor detection can be improved, and efficient and reliable auxiliary diagnosis is provided for clinic.

The invention discloses a bone joint lesion area automatic labeling method and system based on image processing, and belongs to the field of bone joint lesion area labeling. According to the invention, a bone joint reflection signal is obtained by combining the polarization-state-adjustable laser light source with polarization-sensitive optical coherence tomography, and a three-dimensional birefringence distribution diagram is generated through matrix analysis. And after non-local mean filtering is carried out on the distribution map, a lesion edge contour is extracted based on a dynamic gradient amplitude, and a binary mask is generated. Mask pixels are converted into network nodes containing space coordinates, gradient directions and birefringence, and a topological network is constructed based on the Euclidean distance of adjacent pixels. The weight is calculated through the node intersection density and the birefringence, and a graph attention network is utilized to aggregate multi-layer features to extract lesion topological features. The features are mapped to a three-dimensional space to construct a lesion probability distribution field, an interactive labeling result is generated through contour surface extraction and transparency mixed rendering, and the accuracy of labeling the complex lesion area of the bone joint can be improved.

A colorectal lesion multi-modal classification method based on pathological attention and multi-instance learning constructs an efficient automatic diagnosis model by fusing visual features and a textual prototype defined by pathology experts. The method comprises the following steps: collecting histopathological image data, segmenting the histopathological image data into standardized image blocks, and extracting visual features by using a pre-training feature extraction network after color standardization and noise processing; a multi-instance learning framework and a pathological attention mechanism are combined, feature space distribution of a text prototype is adjusted in a self-adaptive mode through a dynamic prototype optimization module, and optimization targets of visual clustering and cross-modal semantic alignment are balanced by adopting a gradient perception double-loss dynamic weighting strategy; and after the model is trained in stages, the generalization performance is verified in an external data set. According to the method, the classification precision is remarkably improved, the method can adapt to dyeing difference and tissue heterogeneity without pixel-level labeling, the accuracy rate in cross-center verification is superior to that of an existing reference model, and the efficiency of pathological diagnosis is greatly improved.

In accordance with the present disclosure there is provided an angioplasty catheter system and method for using such angioplasty catheter system that facilitates controllably delivering focalized pressure to complex lesions. The angioplasty catheter comprises an elongated member having a proximal end, a distal end, and at least one lumen extending at least partially through the elongated member, and an inflatable member proximally affixed to the elongated member adjacent to the distal end and in fluid communication with the at least one lumen, the inflatable member having a radius R and including at least two lobes (32,34), the at least two lobes separated from each other by one or more waist portion (44) defined by two legs (91,92) and a waist portion length (37) such that upon pressurization, radial stress around the waist portion is directed away from the vessel wall and focalized into the lesion at an angle about perpendicular to the legs (91,92) of the upper and lower base of the waist portion (44) creating variable directional forces from to (70,73) to (70′, 73′) resulting in the preferential formation of lesion fractures at the waist of the inflatable member.

In the humanoid robot operation system provided by the embodiment of the invention, the mechanical arm is assembled on the trunk and is in communication connection with the host, the trunk is assembled on the moving mechanism, the trunk is internally provided with the host accommodating space so as to assemble the host in the host accommodating space, and the man-machine interface is assembled on the trunk and is in communication connection with the host. A generation type operation large model is deployed on the host, and the operation large model executes the following steps that basic medical record information, preoperative examination results and focus image data of a patient are received through a man-machine interface to construct a three-dimensional physiological structure model; performing feature analysis on the three-dimensional physiological structure model to mark a target surgical site and formulate a mechanical arm path according to the target surgical site; establishing a mapping relation between the path of the mechanical arm and the position of the patient to generate an action track sequence of the mechanical arm; according to the action track sequence, the moving mechanism is driven to move towards the position of the patient to reach the designated point position, and the mechanical arm is controlled to drive the surgical instrument to act so as to perform an operation on the target surgical site.

The invention discloses a street tree safety risk feature detection method, system and device, and a storage medium. The method comprises the steps of obtaining image information of a lesion area of a trunk on a street tree; preprocessing the image information to obtain a training sample; based on the training sample, training an improved YOLOv8 network model to obtain a trained pest and disease identification model; wherein the improved YOLOv8 network model comprises a selective multi-scale convolution kernel attention mechanism module and an average pooling module; and detecting a trunk image to be recognized by adopting the pest and disease recognition model to obtain a detection result. A pest and disease identification model obtained by training the training sample and the improved YOLOv8 network model can be used for identifying large-batch and various types of tree trunk pest and disease images, and a high-precision identification result is quickly provided, so that the identification time is saved, the troubleshooting cost is reduced, and a more comprehensive reference basis is provided for urban greening management and decision making.

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

The invention relates to the technical field of image enhancement, in particular to a method for detecting an intestinal lesion area of a child based on machine vision. Carrying out threshold segmentation on the grayscale image to obtain a reference point; clustering the reference points and partitioning the grayscale image according to the shape of a cluster; acquiring edge points and a to-be-optimized area according to the gradient of the pixel points in the block area; obtaining a target point according to the gray difference characteristics of the pixel points and the edge points of the to-be-optimized region; obtaining an abnormal point according to the gray difference characteristics of the target point and other target points in the neighborhood and the gray difference characteristics of the neighborhood of the edge points; and obtaining a gray adjustment range value according to the gray difference characteristics of the neighborhoods of the abnormal points and the edge points in the to-be-optimized region. According to the method, a final gray value is obtained according to a gray adjustment range value and a gray discrete feature of an edge point neighborhood; histogram equalization is carried out according to the final gray value, an intestinal tract detection enhanced image is obtained, and the image enhancement accuracy is improved.

The invention provides a real-time breast duct inner wall lesion recognition and positioning method and system based on optical fiber imaging, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining a breast duct inner wall image sequence, extracting a displacement vector field, decomposing the displacement vector field into a dominant frequency and residual components, analyzing the dominant frequency phase to obtain a tissue motion period, an abnormal displacement area is screened from the residual error to establish a tissue anomaly map, an area descriptor is constructed through local wavelet coefficient analysis, a lesion core area is determined through density clustering, and finally the range and the expansion direction of a lesion area are determined through a multi-scale radial basis function and isoline analysis. According to the invention, real-time accurate identification and positioning of the lesion of the inner wall of the breast duct can be realized.

The present invention relates to medical devices and methods for treating a lesion such as a vascular stenosis using non-thermal irreversible electroporation (NTIRE). Embodiments of the present invention provide a balloon catheter type NTIRE device for treating a target lesion comprising a plurality of electrodes positioned along the balloon that are electrically independent from each other so as to be individually selectable in order to more precisely treat an asymmetrical lesion in which the lesion extends only partially around the vessel.

The invention relates to the technical field of medical image processing, and discloses a CyclGan-based multi-modal medical image elastic registration system, which comprises a multi-modal medical image data preprocessing module, a CyclGan-based elastic registration module, a CyclGan-based elastic registration module and a CyclGan-based elastic registration module, a module for training the CyclGan model on the basis of training strategies of medical image data of different modalities; a module for performing elastic transformation and resampling on the preprocessed medical image based on an output grid of the model; and a post-processing module is used for post-processing the deformed medical image. Through the above operations, automatic elastic registration between medical images of different modalities and fusion of medical images of different modalities after registration and alignment can be realized, a doctor is helped to obtain more comprehensive information and more accurately position a lesion area, and thus the accuracy and efficiency of diagnosis are improved.

The invention provides an image segmentation labeling method and system based on machine learning, and relates to the technical field of computer vision, and the method comprises the steps: dynamically selecting three detection points in a key region of a segmentation boundary based on the boundary form of an initial segmentation result, and constructing a dynamic triangular mesh unit; calculating a geometric characteristic value according to the vertex coordinates of the dynamic triangular mesh unit, wherein the geometric characteristic value comprises a proportional relation between the area and the side length of the triangular mesh unit; comparing the geometric characteristic value with a preset threshold range, and if the geometric characteristic value exceeds the threshold range, generating a boundary coordinate correction parameter; and adjusting boundary coordinates of the lung region segmentation result according to the correction parameter, and generating a corrected lung region segmentation result. Through end-to-end operation from image acquisition to lesion marking, the workload of doctors is reduced, and based on the segmentation result, the lesion marking unit can accurately position the lesion position.

The invention discloses a medical ultrasonic image intelligent processing method and system, and relates to the technical field of medical image.According to the main scheme, normal feature coordinates of different organs and tissues are calculated by obtaining normal ultrasonic image data, to-be-processed image data and historical medical record information of a patient; extracting coordinates of a to-be-detected image and comparing the coordinates with normal coordinates for preliminary judgment; performing secondary judgment by combining ovality and circularity; setting a lesion gray threshold to judge whether a lesion exists or not; calculating the size of a lesion area when the lesion exists; according to the method and the system, the comprehensive blood flow evaluation index is calculated in combination with color Doppler blood flow imaging data, the disease evaluation value is calculated according to known medical record data and the like, the disease level is judged, the diagnosis report is automatically generated, the accuracy and the efficiency of medical ultrasonic diagnosis are improved, and powerful support is provided for clinical diagnosis.

The invention relates to a colorectal lesion typing method based on a KAN network and a variable attention mechanism, and belongs to the technical field of medical image computer processing. According to the method, colorectal lesions are classified; a KAN-ResNet backbone network typing model is established and initialized; setting an optimizer and a loss function; model training; storing and outputting the model; and parting the model. According to the method, a ResNet backbone network and a KAN mechanism are combined to capture complex modes and interdependence relations in colorectal images. The KAN-ResNet model aims at improving the classification performance of a fine-grained data set, so that the accuracy of distinguishing subtle changes of colorectal lesions is improved. Layered features are effectively captured by using a deep residual learning framework of a residual network ResNet, a variable attention mechanism and complexity of adapting to lesions are combined, so that the model can focus on related areas, accurate features can still be captured although deformation exists, and adaptability and accuracy in the aspect of colorectal lesion classification can be improved.

The invention discloses an oral tooth lesion AI auxiliary diagnosis system, which belongs to the field of artificial intelligence and comprises an image acquisition module, a three-dimensional modeling module, a lesion marking module, a dual-channel feature extraction module, a cross-modal diagnosis reasoning module, a lesion evolution trend prediction module and a dynamic risk level generation module. The image acquisition module utilizes multi-frequency structured light and a polarization camera to cooperatively acquire oral images; the three-dimensional modeling module is used for reconstructing an upper and lower jaw three-dimensional structure based on edge constraint splicing point clouds and registering images to form double-view fusion data; the lesion labeling module fuses expert labeling and weak supervision pseudo labels to generate joint labels; the dual-channel module extracts skeleton and texture features; the reasoning module realizes cross-modal semantic coupling through an image-text co-occurrence graph; the evolution prediction module models a lesion change path based on the time reversal causal network; and the risk module outputs a five-level risk and re-injects the embedded vector to strengthen prediction. The beneficial effects are that diagnosis intelligence and clinical decision support level are obviously improved.

The invention relates to the technical field of picture generation, in particular to a picture generation method based on multi-scale features. The method comprises the following steps: firstly, collecting medical images, patient data and lesion stage information under different equipment and acquisition parameters, after screening preprocessing, constructing and training a multi-scale VQ-VAE model, introducing an attention mechanism, and adopting adaptive codebook updating and multi-codebook fusion quantification; then, a hierarchical autoregression model based on a Transform decoder is constructed and trained, and lesion stage information is fused into the hierarchical autoregression model; and finally, inputting specific lesion stage information, generating a multi-scale discrete index sequence through a hierarchical autoregression model, converting the multi-scale discrete index sequence into a codeword vector through a multi-scale VQ-VAE model, and finally generating a simulated medical image. According to the scheme, the multi-scale VQ-VAE model is utilized to encode the input image into the multi-scale discrete feature representation, and meanwhile, the autoregression model is applied to the discrete hidden space of the VQ-VAE, so that the distribution of the discrete representation sequence can be effectively modeled, and the medical image with higher quality and more realistic sense can be generated.

The invention discloses a diabetic foot assessment method based on wound image recognition. The method comprises the following steps: processing a foot infrared thermal imaging image to generate a temperature anomaly graph; fusing the foot wound image and the temperature anomaly graph to generate a fused image; constructing a foot blood flow analysis model, extracting a blood flow influence factor, and correcting the fused image through the blood flow influence factor; and constructing a joint assessment model, taking the corrected fusion image and historical data of the diabetic foot patient as input of the joint assessment model, predicting a risk assessment level of the diabetic foot, and outputting a disease assessment result of the diabetic foot patient according to the risk assessment level. According to the method, multi-modal medical image fusion analysis is realized, the accuracy of wound area positioning and pathological state expression is improved, the accuracy of lesion area recognition is improved, the illness worsening risk can be predicted in advance, clinical decision can be assisted, the risk of serious complications such as amputation of a patient is reduced, and the prognosis and life quality of the patient are improved.

Systems and methods for post-PCI coronary analysis. An example method includes accessing a plurality of angiographic images depicting at least one vessel of a patient's heart, the angiographic images including two or more post-percutaneous coronary intervention (post-PCI) images and one or more diagnostic images, wherein the post-PCI images depict a PCI location in which a PCI is included to revascularize a lesion, and wherein the diagnostic images depict the lesion; receiving, via a user interface presenting the post-PCI images, user input specifying positions of the PCI location in the post-PCI images; determining an index indicative of vascular function based on the angiographic images, wherein the index is derived based on a three-dimensional model of the portion of the patient's heart, and wherein individual portions of the diagnostic images depicting the lesion are masked during generation of three-dimensional model; and presenting, via the user interface, summary information associated with PCI, the summary information including at least the determined index.

The invention provides a multi-modal large language model for generating a hepatocellular carcinoma key pathological diagnosis report, a framework main body is a visual coding module, and a multi-modal feature alignment module, a multi-head low-rank attention mechanism, an enhanced medical MoE mechanism and a structured output decoding layer are also introduced. The visual coding module is constructed on the basis of a Swin Transform architecture, visual pre-training is completed on hepatocellular carcinoma MRI data, and after a task specific classification head is stripped, a trunk feature extraction network is reserved to serve as an image modal representation encoder. The multi-modal feature alignment module guides the model to learn a cross-modal semantic mapping relation between a hepatocellular carcinoma MRI image and a key pathological diagnosis report language, image modal input is a visual feature sequence, and text modal output is a structured description text; and the structured output decoding layer generates six types of liver cancer focus attributes. According to the method, the pre-operative multi-parameter and multi-stage enhanced MRI image is utilized, and the open-source large model is finely adjusted to generate a matched liver cancer postoperative pathology report.

The invention relates to the technical field of data visualization, in particular to a multi-modal fusion anorectal data visualization analysis method and system, and the method comprises the following steps: synchronously collecting an anorectal image and a pressure signal, aligning a timestamp, carrying out the preprocessing, outputting a standardized image and a coded physiological signal, and extracting edge and texture features. After LSTM coding, splicing with image features, and fusing into a joint feature map; generating a multi-scale heat map by the feature pyramid network, and coarsely positioning a lesion; after boundary optimization, U-Net recovers details, and a pixel-level lesion probability graph is output; superposing the Jet color gradation to the original image in a semitransparent manner, and marking a pressure abnormal time period; and performing weighted scoring to generate a fourth-level clinical suggestion. According to the anorectal disease diagnosis method, through multi-modal space-time alignment, multi-scale feature fusion, boundary optimization and a quantitative scoring system, the problems of data splitting, insufficient precision and low efficiency of a traditional method are solved, a high-precision and high-robustness intelligent diagnosis tool is provided for anorectal diseases, and the scientificity and efficiency of clinical decision making are remarkably improved.