416 results about "Risk stratification" patented technology

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention discloses a hypertensive heart disease early screening system fusing ultrasonic multiple parameters, which comprises the following steps: acquiring ultrasonic image data and physiological index monitoring parameters of a patient at different time points, performing difference comparison on heart structure parameters to generate time sequence change data, and establishing a multi-dimensional data fusion scheme based on the time sequence change data and physiological indexes; according to the fusion scheme, acute risk trigger factors are identified, blood flow abnormal parameters are generated, and emergency threshold comparison is carried out to generate acute risk early warning signals; extracting a myocardial strain degradation rate to determine a chronic degradation acceleration time point, carrying out disease progress stage association to generate a chronic risk layering curve, and establishing a clinical index comparison table; carrying out double-layer correlation on the acute risk early warning signal and the chronic risk layering curve, and carrying out calibration judgment to generate a comprehensive risk judgment sequence; the sequence is decomposed into an acute judgment layer and a chronic judgment layer, a risk threshold value is configured to generate an instant early warning signal, a layered screening report is formed, and technical support is provided for early discovery of the hypertensive heart disease.

The invention discloses a cerebral hemorrhage prognosis analysis system fusing images and clinical data, and the system comprises the steps: carrying out the collection time point alignment of cerebral hemorrhage image data and clinical diagnosis and treatment data, and constructing a multi-modal time sequence association data set; hematoma region segmentation and surrounding structure compression analysis are carried out, and hematoma morphological features and placeholder effect features are extracted to form an image risk feature set; carrying out index fluctuation analysis and collaborative abnormal mode identification based on clinical data, and screening risk sensitive indexes with a linkage relationship to form a clinical risk factor set; consistency detection is carried out on the two types of risk features, deviation causes are traced, modal weight configuration is determined according to the consistency detection, differential fusion is executed, and a fusion prognosis feature vector is generated; two-dimensional risk layering of function prognosis and survival prognosis is carried out, the confidence coefficient is evaluated and predicted in combination with modal consistency and feature stability, a structured prognosis analysis report with a confidence level is output, and interpretable decision support is provided for prognosis evaluation of cerebral hemorrhage patients.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to the field of clinical diagnostics, and discloses an elderly NSC emergency risk layering method and device and a medium, and the method comprises the steps: S1, obtaining multi-dimensional evaluation data of an elderly NSC patient; s2, adopting a preset risk mapping rule to convert the risk values into single risk values of a unified scale; s3, dividing the single risk value into a plurality of risk sub-models, and calculating a dimension risk score of each risk sub-model; s4, based on a preset fusion strategy, integrating the risk sub-model and the single risk value interaction effect, and calculating to obtain a total risk score of the patient; and S5, determining the risk level of the patient according to the total risk score, and outputting a clinical diagnosis and treatment suggestion. According to the method, multiple biomarkers and key clinical parameters are integrated, a multi-dimensional combined risk assessment model is constructed, and the potential pathological state of a patient can be reflected more comprehensively.

The invention belongs to the technical field of artificial intelligence and medical information, and discloses a multi-round inquiry method and system based on a large language model and session state tracking. According to the method, extraction and synonym normalization are carried out for key medical elements, and high-confidence filling and conflict resolution are continuously completed in multiple rounds of conversations; and fusing the red flag symptom rule and model prediction, and carrying out hierarchical scoring and security constraint generation on individual risks. The information gain maximization serves as a target, and the next round of clarification problem is generated in a self-adaptive mode under the risk constraint; and through cooperation of a large language model and a knowledge base / knowledge graph, sorting and gate type calibration are carried out on candidate diseases and matched departments, and doctor-seeing suggestions, examination suggestions and medication precautions are generated. Finally, efficient understanding and multi-round reasoning of the unstructured symptom information are realized through joint supervision of the session state, the slot confidence and the risk hierarchy.

The invention relates to a hierarchical diagnosis and treatment resource allocation system based on an AI priority pre-auditing mechanism, and belongs to the technical field of medical resource allocation, and the system comprises a user input module which is used for receiving initial information of a patient based on an AI applet, and the initial information at least comprises a symptom description, a medical history record, examination data and a diagnosis and treatment stage; the AI intelligent pre-inquiry module is used for performing multiple rounds of questions and answers on the patient according to the initial information to extract an illness state index, inputting the illness state index into a preset risk layering model, and determining an illness state severity / urgency index of the patient; the resource distribution module comprises a special number source pool reserved for experts, and the resource distribution module is used for opening expert numbers for the patients whose illness condition severity / emergency index is greater than an illness condition threshold value; and the referral guidance module is used for recommending a replacement doctor seeing scheme for the patient who does not accord with the open expert number. According to the invention, the technical problem of insufficient fairness and efficiency of an illness state emergency degree registration distribution mechanism caused by structural mismatching of expert resources is solved.

The invention discloses a supply chain quality collaboration method and system based on a block chain, and relates to the technical field of block chain and supply chain quality management, and the method comprises the steps: collecting quality events according to a batch quality collaboration baseline table, generating a credible collection proof package and a to-be-verified quality event queue, executing dynamic endorsement verification, and writing the dynamic endorsement verification into a quality event account book, outputting a batch state table and an under-chain evidence pointer; performing rapid detection and judgment based on the batch state table and the under-chain evidence pointer, generating a pre-release report and establishing a pre-release mapping table; and triggering a difference correction contract according to the pre-release mapping table, updating the batch state table, generating a collaborative work order, pushing a quality collaborative strategy instruction, and outputting a quality collaborative closed-loop record. According to the method, the risk feedback analysis result table is generated by auditing and evaluating the quality collaborative closed-loop record, the risk layering strategy table is revised, and the batch quality collaborative baseline table is updated, so that the key field constraint is continuously calibrated, and the traceability and consistency of collaborative processing are improved.

The invention belongs to the technical field of medical health monitoring, and provides a human health prediction method and system based on facial video physiological signal detection, and the method comprises the steps: collecting a facial video stream, and extracting time sequence physiological signals such as heart rate, HRV, respiratory rate and the like through an rPPG algorithm; constructing a graph database individual health portrait in combination with multi-scale time sequence alignment; adopting a dynamic threshold algorithm to detect instantaneous anomaly, and fusing nonlinear dynamics and waveform morphological characteristics to quantify anomaly; constructing a hybrid model, extracting space-time and high-order features, and modeling multi-parameter interaction; a prediction result is dynamically corrected based on a Bayesian algorithm, and health risk layering is realized through clustering; and outputting the visual health report. Through non-contact monitoring, multi-modal fusion and edge-cloud collaborative architecture, the problems that traditional equipment is low in compliance, non-contact technology is insufficient in precision and prediction is shallow are solved, dynamic health prediction and closed-loop management are achieved, and the system is suitable for scenes such as remote monitoring and chronic disease screening.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

A Predictive Diagnostic Information Capability-Technology (PreDICT™) system (100) enables users including expert and nonexpert users to provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information. The illustrated system (100) generally includes a user device (102) for use by a user assisting a subject (104), a processing platform (108), and a network (106) for connecting the user device (102) to the processing platform (108). The system (100) may also involve an emergency response network (130) that includes public-safety answering points (PSAPs) (132). The processing platform (108) processes the sensor information and other information from the user device (102), determines risk stratification information as well as medical diagnosis and treatment option information based on machine learning technology, and provides output information to the user device to assist the user in treating the subject (104).

The invention discloses a data-driven early-diabetic future diabetes-related disease progress risk prediction model and a construction method thereof, and the method comprises the steps: collecting clinical index data, including age, gender, BMI, WHR, HOMA-IR, HDL-C, TG, SBP, DBP, SCR and ALT, of early-diabetic patients in a training and verification queue; using an unsupervised soft clustering method combining dimension reduction based on UMAP, graph clustering and a Gaussian mixture model to identify the phenotypic heterogeneity of the prediabetes mellitus; obtaining the probability of the individual phenotype characteristics, evaluating the association between the probability and the development risk of the future diabetes related diseases in the early stage of diabetes, and constructing a development risk prediction model of the future diabetes related diseases in the early stage of urine diseases; and performing model optimization and robustness verification in the verification queue. According to the method, the heterogeneity of the prediabetes mellitus can be effectively identified, and accurate risk stratification and personalized prevention are realized.

The invention relates to the field of biosensors, and discloses a cardiovascular data monitoring method for the cardiovascular medicine department. According to the method, electrocardio, respiration and blood oxygen signals are synchronously collected through a non-invasive terminal, sleep apnea events are analyzed and recognized in a combined mode, the electrocardio signals are deeply analyzed to detect cardiovascular abnormalities, and time sequence characterization of the function state of the autonomic nervous system is generated based on heart rate variability; and further constructing a time sequence causal reasoning model, quantifying the triggering effect of the respiratory event on the cardiovascular event, generating a comprehensive night cardiovascular risk layering index, and triggering graded early warning. According to the invention, non-sensitive, long-time-history and high-precision night cardiovascular risk dynamic assessment and causal mechanism analysis are realized.

The invention relates to application of tsRNA-3025a as a prognostic marker of acute myocardial infarction and a treatment target spot of myocardial ischemia reperfusion injury. A DNA (Deoxyribonucleic Acid) sequence corresponding to the tsRNA-3025a is shown as SEQ ID NO: 1: 5 '-ATCCTGCCGACTACGCCA-3'. The tsRNA-3025a can be used for treating acute myocardial infarction and myocardial ischemia reperfusion injury. In the aspect of prognosis, a detection kit is provided, and the risk of heart failure and short-term adverse events of a patient is evaluated by quantitatively detecting the expression level of the tsRNA. In the aspect of treatment, the invention provides the application of the anti-tagomir for inhibiting the function or expression of tsRNA-3025a in the preparation of the medicine for treating the myocardial ischemia reperfusion injury, and the anti-tagomir is subjected to specific chemical modification. A novel biomarker is provided for prognosis risk stratification of acute myocardial infarction, and an effective treatment strategy is provided for prevention and treatment of myocardial ischemia-reperfusion injury.

The invention discloses a severe patient organ failure assessment and risk stratification method based on the occurrence time point of blood flow infection, and relates to the field of severe medicine prognosis risk assessment modeling. Comprising the following steps: firstly, collecting daily sequential organ failure evaluation scores of a blood flow infection patient during an infection occurrence period, and dividing data into a training set and a verification set; importing the training set into a group trajectory model for fitting, determining the optimal grouping number of trajectory groups, and verifying the model through a verification set; then, according to a verification result, the patients are allocated to the track group with the highest membership probability for research; finally, a Cox proportional risk regression model is adopted, the correlation between different track groups and the death rate is evaluated, and decisive factors influencing prognosis are determined. According to the method, a robust framework is provided for hierarchical management of ICU blood flow infection patients, the death rate of the patients can be independently predicted, reference is provided for early intervention and formulation of personalized management strategies, and the clinical outcome of the patients is finally improved.

The invention relates to a layered prediction method and system for the risk of malignant tumors related to dermatomyositis. The method comprises the following steps: collecting clinical index data of a patient suffering from dermatomyositis, the clinical index data comprising an anti-TIF1-gamma antibody detection result, an interstitial lung disease existence state, a dermatomyosis existence state, an anemia state and a dermatomyositis type; constructing a comprehensive scoring model to generate a risk stratification prediction result according to the clinical index data; the comprehensive scoring model is used for performing binary assignment on each index to obtain a score of a corresponding item; calculating the sum of scores of all indexes of the patient to obtain a total risk score; and layering the risk of the malignant tumor accompanied by the patient based on the total risk score, and outputting a risk layering result. According to the method, the cancer risk of the patient with dermatomyositis can be effectively predicted, and the reliability, convenience and practicability of layered prediction of the cancer risk are improved.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to the technical field of medical information, and particularly discloses a blood transfusion strategy optimization system based on clinical indexes and molecular markers, which comprises the following steps: firstly, collecting clinical indexes of a patient and molecular marker data derived from circulating tumor cells; standardized integration is carried out to construct a multi-dimensional feature set reflecting correlation between clinical dynamics and molecular features; key association rules are identified through data mining, quantitative abnormal expression profiles and risk hierarchical labels are generated for each patient according to the key association rules, the blood transfusion urgency state is evaluated in combination with real-time data, physiological responses and risk changes under different blood transfusion schemes are deduced, and the optimal individual blood transfusion strategy parameters are solved. Performing deduction verification on the strategy by constructing a patient-specific virtual simulation scene, comparing the strategy with historical data to calculate an expected efficiency deviation degree, and outputting a final execution scheme subjected to optimization verification; according to the invention, the transformation from static threshold transfusion to individualized dynamic prediction and optimized transfusion is realized.

The present invention relates to clinical diagnostics including diagnosis, prognosis, prediction, risk assessment and / or risk stratification of preterm birth (PTB) and subsequent treatment in a pregnant subject, and corresponding methods and products. The invention provides decision tools to help clinicians choosing the most appropriate management for the pregnant women. In particular, the present invention relates to a method for the diagnosis, prognosis, prediction, risk assessment and / or risk stratification of preterm birth (PTB) in a pregnant subject, the method comprising determining a level of one or more biomarkers in a sample that has been isolated from said pregnant subject, wherein the one or more biomarkers comprise at least one of matrix metallopeptidase 9 (MMP9) or fragment(s) thereof and Pappalysin-2 (PAPP-A2) or fragment(s) thereof, wherein the level of the one or more biomarkers in the sample is indicative of the presence or absence of a subsequent PTB.

The invention relates to the technical field of identity security verification, and discloses a multi-identity security verification system based on big data. The system comprises a verification collection module, a feature construction module, a risk modeling module and a decision engine module, wherein the verification collection module obtains user multi-source biological features, behavior tracks and equipment environment data, and performs preprocessing to generate an initial feature set; the feature construction module extracts spatiotemporal behavior patterns and cross-device association features and fuses the spatiotemporal behavior patterns and the cross-device association features into a multi-dimensional The risk modeling module loads a pre-trained dynamic threat model, carries out real-time risk layering on the multi-dimensional feature matrix, and outputs a risk level identifier and an abnormal feature vector; and the decision engine module calls a corresponding verification strategy library, and generates a multi-factor verification instruction set in combination with the abnormal feature vector. The system realizes comprehensive verification of user identities through multi-source data integration, dynamic risk assessment and precise strategy matching, and adapts to various high-security demand scenes.

The embodiment of the invention discloses a gastric biopsy pathological risk degree hierarchical identification processing method and system, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: obtaining a digital pathological image of a to-be-diagnosed gastric biopsy pathological section; analyzing the digital pathological image based on an artificial intelligence classification model to obtain a risk level corresponding to the digital pathological image; distributing the digital pathological image to a preset diagnosis path matched with the risk level; wherein the preset diagnosis paths corresponding to different risk levels are different in detail degrees of auxiliary diagnosis information provided by the system or triggered automatic auditing processes; receiving a diagnosis result of manual examination and verification for the digital pathological image, and using the diagnosis result as feedback data for updating the artificial intelligence classification model; through automatic risk layering and differentiated path distribution, diagnosis resources can be optimized, diagnosis efficiency and accuracy can be improved, and continuous evolution of the model can be realized.

The invention relates to the technical field of medical image processing and artificial intelligence diagnosis, in particular to an oral and maxillofacial surgery image recognition and diagnosis method and system based on deep learning, and the method comprises the following steps: multi-modal image collection and cooperative preprocessing: collecting an oral and maxillofacial surgery CBCT image, a cone beam CT curved surface tomography image, an oral endoscope image and an ultrasonic image, a standardized multi-modal image data set is obtained through inter-modal registration and an adaptive enhancement algorithm; according to the method, a traditional diagnosis framework of'single-mode image + manual film reading 'is broken through, and a three-order diagnosis logic of'multi-mode image cooperative enhancement-cross-scale feature dynamic fusion-focus typing and risk hierarchical linkage' is innovatively provided; and accurate identification, typing and malignant transformation risk prediction of common oral and maxillofacial surgery diseases (such as jaw cyst, wisdom tooth impediment, temporomandibular joint disorder and maxillofacial tumor) are realized.

The invention provides an image-based eye lesion grading analysis method and device, a medium, a program product and a terminal, and the method comprises the steps: obtaining one or more eye lesion images, inputting the one or more eye lesion images to a pre-trained first model, and generating a corresponding eye feature vector; for a single image, calculating the similarity between the feature vector and each eye disease category, and outputting a prediction category with the highest similarity; and for a plurality of images, respectively calculating the similarity between the feature vectors and each category, and realizing adaptive weighted integration by using the second model to obtain a final prediction category. Sub-model subdivision and risk level layering are carried out according to prediction categories, and a structured risk layering report is output. The problem that in the prior art, the diagnostic value of multi-image information in complex cases is difficult to effectively evaluate is solved, the accuracy of ultrasound in serious vitreous lesion and intraocular tumor recognition is remarkably improved through a self-adaptive weighted integration mechanism, high-risk patient screening and clinical decision making are supported, and the application scene of ultrasound in ophthalmic diagnosis is expanded.

The invention provides a method and a system for predicting early gastric cancer prognosis by a circulating marker, and relates to the technical field of auxiliary diagnosis. The method comprises the following steps: performing multi-omics detection on a blood sample based on a preset sampling time sequence to obtain a multi-dimensional time sequence characteristic data set containing three groups of heterogeneous data of circulating tumor DNA, exosomes and protein markers; calculating a change slope and a fluctuation variance of the heterogeneous data in adjacent time sequence intervals, constructing a dynamic variation feature matrix in combination with a standard attenuation weighting factor, and deeply mining spatial cross-correlation and sequence dependence features of the matrix to generate a multi-modal fusion feature fingerprint; and performing regression operation on the feature fingerprints by using an integrated learning stack model to obtain a dynamic prognosis risk score, and further retrieving a risk hierarchical mapping table to generate a prognosis evaluation result containing a survival curve. According to the method, multi-modal heterogeneous data can be effectively fused, the biological dynamic characteristics in the tumor postoperative recovery phase are captured, and the accuracy and timeliness of early gastric cancer prognosis prediction are remarkably improved.

The invention discloses an early gastric cancer intelligent screening and risk layering method and system based on multi-modal data fusion and deep learning. The method comprises the following steps: acquiring a gastroscope image, a pathological image, a medical image and clinical data of a patient, performing standardization processing and forming unified feature representation; training the multi-branch neural network model group, and generating a virtual queue tag based on each mode; fusing a multi-modal queue division result to generate a patient comprehensive queue label; adaptively selecting a decision path according to the label, and executing feature fusion and analysis of corresponding depth; generating a structured report containing gastric cancer risk levels and individualized monitoring suggestions; and deploying the system and dynamically tracking the change of a patient queue to realize continuous monitoring of disease progression. Cross-modal semantic alignment, uncertainty evaluation and a Markov prediction model are introduced into the system, the screening accuracy, credibility and dynamic risk evaluation capability are improved, and the system is suitable for early screening and personalized management of gastric cancer.

The invention discloses an early pancreatic cancer prediction and risk stratification system based on artificial intelligence, and belongs to the technical field of medical health data analysis and artificial intelligence. The system comprises a multi-omics data adaptive fusion module, a longitudinal health trajectory coding module, a biomarker combination discovery module, a risk prediction and dynamic layering module and a closed-loop feedback optimization module, and a data confidence index generated by the multi-omics fusion module directly affects the attention weight of longitudinal trajectory coding. Longitudinal track coding adopts a bidirectional long-short-term memory network to extract time sequence characteristics, a biomarker discovery module recognizes a synergistic marker combination through a Transform mechanism, a closed-loop feedback module dynamically adjusts parameters of each module according to a prediction result, and clinical verification shows that the prediction accuracy of the system reaches 85%, the I-stage diagnosis rate is improved by 60%, diseases are discovered 8-12 months in advance, and the diagnosis efficiency is improved. The method is obviously superior to the prior art.

The invention discloses a multi-modal feature fusion preoperative lung adenocarcinoma wettability identification and risk assessment method, which can be applied to preoperative typing judgment and risk stratification of early lung adenocarcinoma patients. According to the multi-modal feature fusion preoperative lung adenocarcinoma wettability identification and risk assessment method provided by the invention, a lung adenocarcinoma wettability diagnosis model and a wettability lung adenocarcinoma risk assessment model are constructed through a multi-modal joint modeling method fusing a 3D chest CT image, a 2D key slice image and a radiation text report; according to the method, lung adenocarcinoma wettability diagnosis and risk level prediction of the wettability lung adenocarcinoma are respectively carried out, through the synergistic effect of multi-modal complementary enhancement and an attention mechanism, the information of each data source is fully utilized, and the accuracy and stability of lung adenocarcinoma wettability identification and risk assessment are remarkably improved; the technical problem that in the prior art, accurate identification and risk grading of the lung adenocarcinoma infiltration degree are difficult to achieve through a non-invasive means before an operation is effectively solved.

The present invention relates to a system and a method for measuring and analyzing minimal residual disease (MRD) in pediatric B-cell precursor acute lymphoblastic leukemia (B-ALL) using multiparameter flow cytometry (MPFC). The invention finds application in clinical diagnostics and hematology-oncology for quantifying MRD in B-ALL patients with high sensitivity and specificity, needed for risk stratification, monitoring treatment response, and informing therapeutic decisions. The system comprises interconnected subsystems including an acquisition subsystem with an MPFC instrument, a control and file generation subsystem, and an analytical subsystem. The analytical subsystem incorporates modules for sequential data reduction, automated data cleaning, automated unsupervised data clustering, and interactive cluster analysis. Key advantages include high MRD detection sensitivity (e.g., 10⁻⁵ or 0.001%) and high specificity, without reliance on reference samples or supervised machine learning models, making it applicable in laboratories with different measuring equipment and using different panels of antibodies for identification of leukemic cells.