37 results about "Clinico pathological" patented technology

The invention relates to a pathological section intelligent auxiliary differential diagnosis system based on multi-modal fusion, in particular to the field of clinical pathology, semantic unification of multi-modal data is achieved through meta-task construction and a cross-modal alignment technology, and transferable diagnostic knowledge is extracted by utilizing a meta-learning framework; the method combines a generative model and knowledge constraints to generate high-quality synthetic data, and finally fuses real and synthetic samples through a self-adaptive diagnosis mechanism, thereby remarkably improving the differential diagnosis capability of rare lesions, effectively solving the problem of model generalization in a training data scarcity scene, and improving the accuracy of model identification. And efficient and reliable intelligent auxiliary decision support is provided for clinical pathological diagnosis.

The invention provides a puncture sample detection platform based on a tumor protein targeting probe. According to the puncture sample detection platform, IR-780 is used for marking tumor specific protein in a puncture sample; the improved three-dimensional gel electrophoresis device is used for separating proteins in tissues; the gel processing platform is used for processing and analyzing the separation gel medium; and the automatic diagnosis platform is used for automatically judging benign and malignant conditions of the puncture sample according to the intensity and coherence of the fluorescence signal and circling the contour of the malignant sample. Compared with clinical pathological examination, the method has the advantages that benign and malignant puncture samples are judged by evaluating the protein content of various tumor receptors, and the problem of diagnosis difficulty caused by limited histological information is well solved. Meanwhile, the dependence on visual diagnosis of pathological analysis of the frozen section and experience of pathologists is reduced, and the accuracy and efficiency of pathological tissue diagnosis are effectively improved. The automatic diagnosis platform can eliminate a large number of benign samples and can reduce repeated work of pathologists.

The invention discloses a prognosis evaluation method and system for diffuse large B-cell lymphoma. According to the method, firstly, a gene module closely related to lipid metabolism is screened from DLBCL transcriptome data through weighted gene co-expression network analysis (WGCNA), and then eight key prognosis genes including FNDC1, IL22RA2, C15orf48, OMD, MFAP2, BC017398, CXCL6 and TNFAIP6 are identified from the module by adopting multi-step regression analysis (single factor Cox, LASSO and multi-factor Cox). A risk scoring model is constructed based on the expression levels and regression coefficients of the genes, and DLBCL patients can be divided into a high-risk group and a low-risk group with significant survival differences. The risk score and the clinical pathological factors are further integrated to construct a column graph, and individualized survival probability prediction can be achieved. The invention further provides a corresponding prognosis evaluation system, electronic equipment and a storage medium. An independent data set verifies that the prognosis model has excellent prediction performance and clinical practical value.

The invention discloses a medical synthetic data analysis method and device based on causal reasoning and a medium. The method comprises the following steps: generating medical synthetic data through a generator created according to a real clinical data set; analyzing the real clinical data set and the medical synthetic data to obtain a first causal diagram and a second causal diagram, and comparing the two diagrams to identify an abnormal causal edge corresponding to the medical synthetic data; determining a path corresponding to the abnormal causal edge in the first causal graph, extracting risk factors and corresponding results in the path, and respectively calculating an average processing effect and an effect difference value of the risk factors on the results in the real clinical data set and the medical synthetic data; and determining an analysis result of the medical synthetic data according to the effect difference value. According to the method, the medical synthetic data quality can be deeply analyzed, the fidelity of the synthetic data to a real-world clinical pathology causal mechanism is verified, the specific link of causal distortion is accurately positioned, and clear and executable guidance is provided for the correction data generation process.

The invention provides an innovative model based on the correlation between the PLEKHA4 gene expression level and the low-grade glioma radiotherapy sensitivity and a prediction method. According to the model, a multi-factor Logistic regression analysis framework is constructed, and the PLEKHA4 gene expression level and key clinical pathological parameters are organically combined, so that a radiotherapy sensitivity prediction model is established. The method comprises a series of steps of sample collection, gene expression detection, clinical information collection, model calculation, result interpretation and the like, and can realize accurate prediction of radiotherapy response of low-grade glioma patients. Compared with the prior art, the method has the remarkable advantages of simplicity and convenience in operation, high prediction accuracy, high clinical transformability and the like. The method has great potential in the aspect of guiding individualized radiotherapy scheme formulation, can significantly improve the treatment effect, and has important value for medical application. Besides, the model can be optimized through further clinical verification, so that clinical practice can be better served, and the life quality and prognosis effect of patients are improved.

The application provides a virtual restaining method of pathological section images, comprising the following steps: obtaining a histopathological image of a pathological section; constructing a virtual restaining network of the histopathological image based on a diffusion model; training the virtual restaining network, and taking the trained virtual restaining network as a virtual restaining model of a pathological image; segmenting the histopathological image into a plurality of segmented images; virtually restaining the segmented images through the virtual restaining model of the pathological image to obtain restained segmented images; and splicing the restained segmented images to obtain a restained histopathological image. The application can directly generate an IHC image from a scanned HE staining image, avoids time, money, manpower and material resources spent in the IHC section manufacturing process, makes clinical pathological diagnosis more convenient, and promotes the development of the medical and health industry.

This invention relates to the field of medical technology, specifically to a method for preoperative risk assessment and prediction in liver transplant patients with hepatocellular carcinoma, comprising the following steps: Sample collection: selecting plasma samples and corresponding clinicopathological information from liver transplant recipients of hepatocellular carcinoma, and clarifying the inclusion and exclusion criteria for samples; Plasma cell-free DNA extraction and whole-genome sequencing: extracting and quality-controlling cell-free DNA from the plasma samples collected in step S1, constructing a sequencing library, and performing low-coverage whole-genome sequencing. This invention utilizes plasma-extracted cfDNA for whole-genome sequencing, combined with clinical testing information, to construct a preoperative risk assessment and prediction model for postoperative recurrence in liver transplant recipients of hepatocellular carcinoma based on non-invasive testing. This model can be used to predict the probability of recurrence-free survival before liver transplantation. The model derivation cohort integrates clinical records and circulating tumor DNA data for preoperative recurrence risk prediction.

The present application relates to the technical field of animal model construction, and specifically discloses a method for constructing an atherosclerotic plaque thrombosis model. ‑ / ‑ The method comprises the following steps: 1) selecting an ApoE The method provided by the present application is simple in operation, high in repeatability, and can induce atherosclerotic plaque thrombosis under the stimulation of a low-dose LPS, and can obtain a thrombosis rate comparable to that of the prior art, and can be closer to the formation of atherosclerotic thrombosis related to clinical endotoxemia, and better simulate the clinical pathological process.

The invention provides a children nephroblastoma machine learning survival model developed based on a center queue, and belongs to the technical field of nephroblastoma prediction and evaluation. And analyzing and evaluating prognosis risk factors of the nephroblastoma, establishing an interpretable machine learning survival model based on the central queue, and finally, converting the model into a clinically friendly online prediction platform to promote the transformation of a prognosis evaluation tool from scientific research to clinical practice, so as to improve the prognosis evaluation efficiency. The method aims at providing dynamic risk assessment and individualized treatment decision support for clinicians, and finally the survival income of relapse / refractory child patients is improved.

The invention relates to the technical field of medical image processing and artificial intelligence, and particularly discloses an intestinal cancer pathological image screening method applied to fluorescence immunohistochemistry. The method comprises the following steps: carrying out multi-channel acquisition and pretreatment on a fluorescent pathological section; according to the method, the image screening model based on the convolutional neural network is constructed and trained, automatic screening and grading of pathological images are realized through the trained deep learning model, mass fluorescence immunohistochemical intestinal cancer pathological images can be processed in batches, manual evaluation one by one is not needed, the screening period is greatly shortened, and the screening efficiency is improved. The problems that traditional manual screening is low in efficiency and difficult to meet high-throughput image analysis requirements are solved. According to the automatic process, the workload of pathologists is reduced, fatigue errors caused by long-time manual screening are avoided, efficient and stable data screening support is provided for subsequent accurate quantitative analysis and AI model training, and efficient promotion of clinical pathological research and diagnosis is assisted.

The invention relates to an immunohistochemical detection reagent and kit for bladder cancer neuroendocrine subtype diagnosis and prognosis based on a PHF6 index. The reagent comprises a PHF6 primary antibody, a PHF6 secondary antibody, an antigen repair solution, blocking serum, a DAB developing solution, a hematoxylin staining solution, a hydrochloric acid-alcohol differentiation solution, an ammonia water blue-returning solution and a neutral gum mounting medium. The obvious correlation between the expression level of the PHF6 and the neuroendocrine subtype of the bladder cancer is determined by detecting the expression condition of the PHF6 in the bladder cancer tissue and combining a series of cell experiments and clinical pathological analysis. The invention provides a novel and effective diagnosis means which can help a clinician to accurately judge the neuroendocrine subtype of bladder cancer, so that a more accurate individualized treatment scheme is formulated for a patient, and more reliable prognosis evaluation is provided.

The application provides a laryngeal squamous cell carcinoma prognosis gene methylation marker and application thereof, and relates to the technical field of clinical medicine. The methylation marker is CORO1C and MAPK11, and a recurrence risk prediction model is constructed based on the two sites. The prediction model is independent of factors such as stage, age and differentiation of patients, proving the independence and universality of the prediction model. Through the RRBS technology, the methylation changes of the genes can be comprehensively analyzed at high resolution, and the prognosis value of the markers in laryngeal squamous cell carcinoma is verified. Compared with traditional single clinical pathological indicators, the methylation marker based on the molecular level provided by the application has high accuracy and sensitivity, and can provide more accurate basis for the recurrence prediction of laryngeal squamous cell carcinoma.

This invention belongs to the field of artificial intelligence technology, specifically a deep learning-based method for early warning and auxiliary diagnosis of animal epidemics in animal husbandry and veterinary medicine. It includes constructing an environmental-physiological feature decoupling model to separate environmental stress from pathological deviations; using graph convolutional neural networks to analyze the dynamic evolution of group social topology and capture individual out-of-group tendencies; employing a spatiotemporal multi-scale feature fusion architecture to integrate and process physiological sequences, posture features, and social relationships; and using a biological attribute mapping layer to reverse-map the neural network state into clinical pathological descriptors. This application achieves environmental interference removal and accurate capture of the epidemic incubation period, provides logically traceable auxiliary diagnostic reports, and significantly improves the certainty of epidemic prevention decisions.

The application discloses a pathological grading method and device, a storage medium and a computer program product, relates to the technical field of medical image processing, and relates to the technical field of medical image processing. The pathological grading method comprises the following steps: acquiring a light microscope image of a kidney biopsy, and detecting the light microscope image by using a preset glomerulus recognition model to obtain a section image of each glomerulus; according to the section image, determining a proportion result of each type of glomerulus in the light microscope image; and according to the proportion result and a preset pathological grading rule, determining a pathological grading result. The application automatically identifies and classifies the glomerulus in the biopsy image, and automatically classifies according to the clinical pathological grading standard, thereby reducing the work load of the clinician in the pathological grading, and improving the accuracy and efficiency of the pathological grading.

The invention discloses a Parkinson depression micro-expression recognition method and system based on pathological association transfer learning, and belongs to the field of medical artificial intelligence. The method comprises the following steps: firstly, collecting a face video and clinical pathological data, extracting micro-expression spatial-temporal features and pathological features after preprocessing, fusing through an attention mechanism, combining migration, confrontation and a multi-task learning training model, and outputting a recognition result. The system comprises a data acquisition module, a data preprocessing module, a feature extraction module, a pathological association feature fusion module, a transfer learning and model training module and a micro-expression recognition module. The problems that feature extraction is difficult, over-fitting is caused and pathological information is not used are solved, and the Parkinson depression micro-expression recognition accuracy is effectively improved.

The invention discloses a chronic nasosinusitis auxiliary typing method based on radiomics data and related equipment. The method comprises the following steps: acquiring paranasal sinus radiomics data, clinical feature data and biological sample analysis result data of a chronic sinusitis patient; dividing the paranasal sinus radiomics data into a plurality of image clustering categories by adopting an unsupervised clustering algorithm, and associating clinical feature data and biological sample analysis result data corresponding to different image clustering categories respectively; and analyzing clinical feature data differences and biological sample analysis result data differences among different image clustering categories, and constructing a systematic mapping relationship between the image clustering categories and clinical pathological typing so as to carry out chronic sinusitis auxiliary typing. The problems that in typing research of chronic nasosinusitis, traditional intrinsic typing needs to depend on complex and expensive biological sample examination, generalizability is not high, and sampling cannot completely reflect individual heterogeneity of patients and is invasive can be solved.

This invention discloses a lymphoma-specific database system based on multi-dimensional data, aiming to address the problems of heterogeneous lymphoma data, inefficient treatment, and limited application. It comprises a data acquisition layer, a treatment layer, and an application layer connected sequentially. The acquisition layer connects to HIS, EMR, LIS, and PACS systems via API, and utilizes ETL to achieve full and incremental acquisition of historical data, obtaining multi-dimensional data including clinical, pathological, and imaging data. The treatment layer de-identifies sensitive information, cleans it, matches it with the EMPI master index, and structures it using a BioBERT pre-trained model for NLP, outputting standardized data. The application layer builds a database with 662 fields (demographic, clinical, etc.) according to standards such as OMOP, providing research analysis functions. It also constructs an AI module using random forests / neural networks to predict disease recurrence, treatment efficacy, and toxicity, and supports federated learning for cross-institutional data sharing. The system improves data quality and contributes to precision medicine and research in lymphoma.

This invention discloses a diabetes complication risk prediction system based on disease prior masks, belonging to the field of diabetes risk assessment technology. By designing a disease relationship learning module based on pathology masks, irrational disease interactions are suppressed and inter-task information flow that violates medical logic is shielded. This forces the model to learn only clinically meaningful shared representations, reducing the risk of learning incorrect associations under noisy data and significantly enhancing the model's medical interpretability, making its internal decision-making mechanism highly consistent with clinical pathological mechanisms. Furthermore, the introduced relationship attention gating mechanism adaptively adjusts the fusion ratio between disease relationship information and task-specific features according to the needs of different complication tasks, enhancing beneficial relationship signals when necessary and automatically suppressing irrelevant features when noise is high. This achieves more stable and efficient multi-task feature interaction and collaborative learning, enabling more accurate prediction of diabetes complications.

A test strip for detecting Aβ in urine includes a polyvinyl chloride (PVC) bottom plate. The PVC bottom plate is laid with a sample pad, a conjugation pad, a chromatography pad, and an absorbent pad that are overlapped in sequence. The conjugation pad is coated with colloidal gold particles conjugated to a monoclonal antibody. The chromatography pad is provided with a test line on the side proximate to the conjugation pad, and is provided with a control line on the side proximate to the absorbent pad. The test line is coated with an Aβ-binding polymer. The control line is coated with a goat anti-mouse IgG polyclonal antibody. The method is suitable for the following: routine clinical pathological examination; general screening of a large number of people and self-screening of home end-users; assisting the early diagnosis and prejudgment of mild cognitive impairment (MCI) clinically.

The invention discloses a diabetic nephropathy prognosis and diagnosis system based on BOLD-MRI and multi-dimensional parameters, and the system comprises an acquisition module which is used for acquiring clinical pathological indexes of a patient and acquiring medullary R2 * parameters through a BOLD-MRI semi-automatic 12-layer concentric object method; the training module is used for training an ESRD prognosis prediction model and a DN / NDRD differential diagnosis model based on clinical pathology indexes and medullary R2 * parameters; the weighting module is used for performing weighting calculation on the clinical pathology indexes and the medullary R2 * parameters based on the trained model, and determining the weight coefficient of each parameter; and the diagnosis prediction module comprises a prognosis prediction sub-module and a differential diagnosis sub-module, and is used for analyzing to-be-predicted clinical pathological indexes and medullary R2 * parameters based on the prediction diagnosis model to realize ESRD risk prediction and DN / NDRD differential diagnosis. According to the invention, clinical and BOLD parameters can be effectively integrated, so that the prediction model can more accurately identify diabetic nephropathy and carry out prognosis evaluation, thereby providing earlier intervention opportunities, enabling a diagnosis window to move forward, improving diagnosis performance and reducing unnecessary renal biopsy.

The invention provides an artificial-intelligence–based platform for automated analysis of Pap smear slides. The system integrates preprocessing, dual deep learning models (CNN-Transformer and EfficientNet-LSTM), and reporting modules to classify cytological images into diagnostic categories including NILM, ASC-US, LSIL, HSIL, and SCC. The platform includes interpretability outputs, secure data management, and active learning capabilities, enabling scalable, accurate, and transparent cervical-cancer screening across diverse laboratory settings. The platform is suitable for implementation in clinical pathology laboratories, telemedicine networks, and population-level screening programs. It allows rapid, reproducible, and objective evaluation of cytology slides, improving throughput while reducing reliance on manual slide examination. The invention is deployable on local servers, private clouds, or public cloud infrastructures and is adaptable to various cytology specimens. Adoption of this system enhances early detection of precancerous lesions, reduces inter-observer variability, and facilitates integration with laboratory information systems. It also supports continuous learning and adaptation to evolving imaging protocols. The invention provides multiple advantages over existing methods: (1) Dual-model architecture combining CNN-Transformer and EfficientNet- LSTM ensures robust classification across both dense and sparse cellular regions. (2)Preprocessing and adaptive tiling improve feature extraction from variable-quality images. (3)Interpretability modules enhance clinician trust and facilitate regulatory compliance. (4) Active learning allows continuous improvement based on expert feedback. (5) Modular deployment supports both local laboratory and cloud-based operation. (6) Data security features meet international standards for privacy and encryption. (7) High scalability enables screening of large slide volumes without compromising accuracy. The invention provides a clinically relevant, efficient, and technologically advanced solution for automated cervical-cancer screening, applicable in diverse healthcare environments and adaptable to future cytology domains.

This application relates to the field of gastrointestinal metaplasia technology, and in particular to a method and system for classifying gastrointestinal metaplasia images based on cross-scale feature aggregation. The method includes: acquiring a set of gastrointestinal pathological images; analyzing the classification information of intestinal epithelial metaplasia in metaplasia identification based on the gastrointestinal pathological image set to obtain a preliminary metaplasia information set; acquiring a set of patient clinical pathological information; analyzing the correlation between metaplasia patterns and inflammatory background under different pathological driving factors, and inferring and ranking the pathological driving factors to obtain a driving factor information set; and generating a comprehensive analysis report based on the driving factor information set, including metaplasia type determination, driving factor attribution, and carcinogenesis risk stratification, and outputting a gastrointestinal metaplasia image classification log. This achieves precise hierarchical classification of pathological driving factors, clarifies the primary and secondary roles of each factor, and provides a basis for accurate attribution of gastrointestinal metaplasia causes and scientific stratification of carcinogenesis risks.

The invention belongs to the technical field of medical experimental animal models, and particularly relates to a construction method of a spleen and stomach damp-heat syndrome chronic atrophic gastritis animal model. The construction method comprises the following steps: firstly, performing spleen and stomach damp-heat modeling on a tested animal to obtain spleen and stomach damp-heat syndromes, and then performing chronic atrophic gastritis induction. According to the specific three-step atrophic gastritis induction method, the clinical pathological process of inflammation caused by stimulation, gastric mucosa atrophy caused by damp heat and blood stasis for a long time and intestinal metaplasia can be accurately copied, the clinical pathological process is highly matched with the stage progress characteristic of clinical chronic atrophic gastritis, and the model is high in success rate, good in stability and high in repeatability.

The present application relates to the technical field of medical imageomics, and particularly relates to a pancreatic cancer early recurrence prediction method and system based on an interpretable machine model, comprising the following steps: extracting intratumoral and peritumoral imageomics features in CT images; performing single factor analysis and multivariate logistic regression analysis on body composition parameters and clinical pathological data to obtain clinical features; constructing six groups of classifier models based on the intratumoral and peritumoral imageomics features through six machine learning algorithms, and obtaining an imageomics model according to model performance comparison; constructing a clinical-imageomics combined model by combining the clinical features and the imageomics model, and performing an interpretable SHAP analysis on the clinical-imageomics combined model. The present application combines intratumoral and peritumoral CT imageomics features with body composition parameters, constructs a machine learning model for predicting the early recurrence risk of PDAC after resection, and incorporates the interpretable SHAP analysis to enhance the transparency of the machine learning model decision-making process.

This application relates to a method, system, device, and medium for predicting the trend of vascular crisis in replanted fingers. The method includes: acquiring clinical pathological index data, infrared monitoring image sequences, and visual monitoring image sequences; inputting the visual monitoring image sequences into a neural network model for appearance image feature extraction to extract a feature sequence of the replanted finger's filling state and a feature sequence of the replanted finger's swelling progression; inputting the infrared monitoring image sequences into a neural network model for vascular imaging feature extraction to extract a feature sequence of blood supply patency; concatenating the blood supply patency feature sequence, the replanted finger's filling state feature sequence, and the replanted finger's swelling progression feature sequence to obtain a multi-source image monitoring feature sequence; and inputting the multi-source image monitoring feature sequence and clinical pathological index data into a vascular crisis trend prediction model to generate a vascular crisis level prediction sequence. This method can identify the filling state and swelling progression of the replanted finger, enabling dynamic trend prediction of the risk of crisis.

The invention discloses a nomogram pancreatic cystic disease diagnosis model construction method, which comprises the following steps of S1, acquiring enhanced MRI (Magnetic Resonance Imaging) images and clinical pathological data, and completing data set division; s2, acquiring a focus region of interest for analyzing region calibration; s3, preprocessing the focus region of interest, extracting radiomics features, completing repeatability evaluation and feature screening, and constructing a candidate feature set; s4, an improved quadratic discriminant analysis algorithm is adopted to construct a radiomics model, hyper-parameters are adjusted, indexes such as AUC are evaluated, and radiomics scores are output; s5, screening key clinical variables based on regression analysis, and generating a clinical input feature set; s6, combining radiomics scores and clinical features to construct a combined diagnosis model; and S7, outputting a Nomoh map, performing three-data-set performance evaluation, and verifying model discrimination capability, calibration consistency and clinical effectiveness. The intelligent, accurate and visual pancreatic cystic lesion diagnosis system realizes intelligent, accurate and visual pancreatic cystic lesion diagnosis.

The invention provides a tsRNA biomarker for hepatocellular carcinoma diagnosis and prognosis and application of the tsRNA biomarker, relates to the technical field of biomedicine, and is technically characterized by providing application of 5 '-tiRNA-iMet-CAT as a biomarker in preparation of a hepatocellular carcinoma diagnostic reagent. According to the application, clinical pathological data analysis shows that the tsRNA biomarker which is highly expressed in the serum of a hepatocellular carcinoma patient is related to the differentiation degree, the tumor size, the TNM (Tumor Necrosis Module) staging, the age, the metastasis and the BCLC (Barcel Clinic Liver Cancer) staging, and the tsRNA biomarker has the advantages that the tsRNA biomarker is high in expression in the serum of the hepatocellular carcinoma patient; besides, the 5 '-tiRNA-iMet-CAT biomarker is sequentially combined with tumor markers AFP and PIVKA-II for detecting the hepatocellular carcinoma in blood, the AUC of the 5'-tiRNA-iMet-CAT biomarker for distinguishing a hepatocellular carcinoma patient from a healthy normal person in serum can reach 0.960, the sensitivity and the specificity can reach 95% and 68%, and the result shows that the combined index of 5 '-tiRNA-iMet-CAT + AFP + PIVKA-II has higher diagnosis efficiency compared with the single 5'-tiRNA-iMet-CAT detection.

The application discloses a prediction model training method for predicting the relative curative effects of EC and T in NAC, and the method comprises the following steps: acquiring MRI image data and clinical pathological data of a subject to construct a data set, and constructing an imageomics model based on DCE and ADC sequences through image segmentation, feature extraction, qualitative relative curative effect feature, model construction and verification, and a mixed model combined with clinical pathological factors is used to predict the relative curative effects of EC and T treatment. The application can effectively predict the relative curative effects of EC (epirubicin plus cyclophosphamide) and T (taxane drugs) treatment schemes in the middle stage of NAC, so as to timely adjust the treatment scheme and improve the treatment effect.

The application provides a clinical application level pathological large model construction method and system based on weak supervision learning, comprising: respectively performing weak supervision training on each historical clinical pathology image, adjusting upstream learning parameters of a large model according to a training result and constructing an upstream task of the large model, respectively performing segmentation decoding on each weak supervision training result, determining downstream learning parameters of the large model according to a segmentation result and constructing a downstream task of the large model, acquiring upstream fine features and downstream fine features, constructing an upstream and downstream collaborative task of the large model, combining the upstream task of the large model and the downstream task of the large model to generate a pathological large model, inputting a current clinical pathology image into the pathological large model to perform pathological information recognition, obtaining a plurality of pathological recognition labels corresponding to a patient, and generating a pathological report of the patient, so that the computational complexity in processing a high-resolution image is reduced, the collaborative mechanism between different tasks in the large model is optimized, and the pathological recognition technology can be widely applied.

This invention discloses a method for constructing an animal model of pituitary tumors based on primary pituitary tumor cells. First, tumor tissue from a patient with a pituitary adenoma is obtained in vitro. This tumor tissue is then cultured in its primary form. Finally, the cultured cells are injected in situ into the pituitary fossa of the experimental animal. The model constructed using this method exhibits imaging and pathological manifestations consistent with the intracranial space-occupying effect and hormonal level disorders associated with pituitary adenomas. It demonstrates excellent clinical pathological and symptom simulation, making it suitable for pituitary adenoma pathological research and drug screening, with broad application prospects.