204 results about "Recurrence risk" patented technology

The invention provides a multi-mode prostate cancer biochemical recurrence risk layering prediction system based on artificial intelligence. Based on an Xgboost framework, a postoperative patient pathological panoramic pathological section scanning image is analyzed through end-to-end, multi-scale, multi-center and large-sample analysis, pathological information is utilized to the maximum extent, meanwhile, the prognosis risk of a patient can be evaluated more comprehensively in combination with clinical indexes such as CAPRA-S scores, and the method has obvious advantages compared with a traditional model. The method aims at better fitting the use scene of a hospital, the risk of prostate cancer recurrence of a patient is more efficiently and accurately predicted by fusing pathological section features and clinical features after a radical operation, and the risk of recurrence of the patient within 3 years and longer time after the radical operation can be accurately predicted. And an interpretable module is further combined to assist a doctor to interpret a result, so that precise layering and personalized follow-up visit of the BCR risk of the prostatic cancer patient are realized, the risk of excessive treatment and missed diagnosis is reduced, and the method has a good application prospect.

The invention discloses a glioma T cell prediction and prognosis evaluation method based on pathological images, particularly relates to the field of patient prognosis health evaluation, and aims to solve the problems that existing pathological evaluation is difficult to combine with tumor structure heterogeneity and immune infiltration distribution, and the future progress risk of a patient cannot be predicted based on follow-up visit pathological data. A spatial heterogeneity map of a tumor core area and an invasion edge is constructed in a digital pathological section, density gradients of T cells in different areas are calculated to generate a distribution heterogeneity coefficient, interaction processing is performed on the two types of characteristics in combination with historical follow-up visit records, and a time sequence neural network constructed based on a gating structure is combined to obtain a high-efficiency characteristic of the tumor. And outputting disease progress probabilities of a plurality of follow-up visit time points in the future to form a prognosis trajectory prediction curve, calculating a quantitative recurrence risk score according to curve slope change and an immune fluctuation mode, and finally generating an individualized management scheme, thereby realizing quantitative prediction evaluation and risk management of the prognosis trend of the glioma patient.

The invention relates to the technical field of medical data analysis, in particular to a brain tumor curative effect analysis system which comprises a tumor data sensing layer for collecting multi-department diagnosis and treatment data, tumor image data and patient pathology monitoring data; the tumor feature processing center extracts data features, correlates data and core features before and after treatment through an attention mechanism, and generates a tumor complete-cycle unified feature map; the therapeutic effect dynamic analysis unit evaluates the therapeutic effect in stages, and outputs a therapeutic effect index and a recurrence risk value through a self-supervised model; the dynamic adaptation decision module is used for generating personalized treatment adjustment suggestions based on the dynamic change of the blood brain barrier in combination with the curative effect index, the recurrence risk value and the multi-omics characteristics of the patient; and the AI multi-department consultation unit automatically matches similar cases with field expert suggestions, and formulates a target diagnosis and treatment scheme based on a visual platform and multi-department doctor collaborative consultation in combination with personalized treatment adjustment suggestions. Therefore, the problems of lagging effect evaluation, insufficient diagnosis and treatment suggestions and the like in the prior art are solved.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention belongs to the field of biomedicine, and particularly relates to application of CRYL1 protein in preparation of a product for evaluation, diagnosis or auxiliary diagnosis of postoperative recurrence risk of calcium oxalate kidney stone combined with renal nipple calcium spot, and the sequence of the CRYL1 protein is shown as SEQ ID NO.1. The invention discovers that the expression of the CRYL1 protein in renal tubules and collecting tube cells of renal papillary calcium spot tissues is reduced for the first time, and tests prove that the urine CRYL1 protein is a predictive factor and a predictive marker for postoperative recurrence of a patient with CaOx kidney stone combined with renal papillary calcium spot. The CRYL1 protein detection kit is further researched and developed to be used for detecting or evaluating the postoperative recurrence risk of patients suffering from CaOx kidney stone combined with renal nipple calcium spots, personalized follow-up visit and prevention schemes of the patients are promoted, and the CRYL1 protein detection kit has huge clinical application prospects.

The invention discloses a depression recurrence risk intervention method, device and equipment and a medium, and belongs to the technical field of medical treatment. The method comprises the following steps: integrating a gene risk score, neuroimaging brain region characteristics and clinical medical history data through a multi-modal fusion neural network, and generating an individual baseline risk score; the baseline score is dynamically corrected based on the self-assessment data, and the real-time performance of risk assessment is enhanced; analyzing time sequence characteristics of the physiological and behavior data by using an LSTM time sequence model, and outputting a short-term recurrence early warning label; and in combination with emotion knowledge graph analysis and dynamic risk grading of real-time voice / text data, a hierarchical intervention strategy is triggered. Through multi-dimensional data fusion and a dynamic calibration mechanism, the problems that a traditional method is single in evaluation dimension and lags in response are solved, closed-loop management from risk early warning to accurate intervention is achieved on the premise that direct clinical diagnosis is avoided, and comprehensiveness and timeliness of prevention and control of depression recurrence are improved.

The invention provides a dynamic adjustment method based on a depression mind-correcting treatment scheme, and relates to the technical field of medical care information, and the method comprises the steps: collecting multi-dimensional information of a patient, and collecting life behavior risk data, psychological scale data and cognitive function evaluation data; comprehensively evaluating the depression level of the patient; determining the priority of a treatment therapy module according to the multi-dimensional information of the patient; generating an initial treatment scheme; calculating a daily task comprehensive weight value, and dynamically adjusting the training content of the next day; and evaluating the state of the patient in stages, recalculating the priority of each therapy module, and optimizing the therapeutic scheme. Through multi-dimensional data fusion evaluation and intelligent algorithm dynamic adjustment, a complete closed-loop feedback optimization system is constructed, precision and individuation of a depression treatment scheme are achieved, the treatment effect and patient compliance are effectively improved, the recurrence risk is reduced, and a more scientific and effective treatment scheme is provided for depression patients.

The invention discloses a tumor recurrence risk prediction method and system based on electronic medical record data, and relates to the field of electronic medical record data processing and analys.The historical electronic medical record data are processed in a structured mode, a recurrence risk mapping model is established, individualized recurrence risk assessment can be achieved based on multi-dimensional features, and the tumor recurrence risk prediction accuracy is improved. The subjective judgment error is obviously reduced; a risk level layering mechanism can automatically distinguish patients needing emergency intervention and conventional monitoring, excessive medical treatment or delayed treatment is avoided, and the method is particularly suitable for chronic diseases such as tumors needing long-term management; through periodic marker measurement and risk level feedback, closed-loop management of evaluation-intervention-re-evaluation is formed, and the requirement for continuous optimization of clinical diagnosis and treatment is met; in addition, through preprocessing, the preprocessed multi-dimensional features can be directly called subsequently, and repeated data cleaning work is avoided.

The invention provides an atrial fibrillation postoperative recurrence prediction method fusing electrocardiosignals and clinical characteristics. The method comprises the following steps: acquiring data of a patient before an ablation operation, and carrying out resampling, denoising and normalization preprocessing and data segment segmentation on an electrocardiosignal; extracting spatio-temporal features by using a deep network containing a residual convolutional block and a long and short term memory module; screening high-discrimination clinical baseline features through statistical analysis and a machine learning model; extracting time-frequency domain and nonlinear features of short-time heart rate variability; designing a cross-modal attention fusion module to carry out feature adaptive weighted fusion; and outputting a recurrence probability through a multi-layer perceptron based on the fusion features. The method improves the prediction precision through feature complementarity, facilitates the recognition of high-recurrence-risk patients, is suitable for sinus heart rhythm signals or atrial flutter and atrial fibrillation signals, and has a certain application value in the field of cardiovascular precision medical treatment. The method can be popularized to all prediction researches based on the electrophysiological signals.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention discloses a breast cancer recurrence risk prediction method and system based on multi-modal data missing interpolation and gene interpretability enhancement. The method comprises the following steps: firstly, dynamically generating and complementing features of a missing mode by matching a generative adversarial network with a mode missing mask matrix; then, a feature screening mechanism driven by gene information is introduced, through a multi-task learning network, image feature extraction is supervised by using a gene expression tag in a model training process, and image features highly associated with recurrence-related genes are screened out; and finally, fusing the complemented multi-modal time sequence characteristics by adopting Transform, and outputting a recurrence risk probability. According to the method, the robust prediction performance can be realized under the condition of data missing, and meanwhile, image interpretation with a molecular biology basis is provided for the feature screening process of the model, so that the reliability and clinical acceptability of the whole system are enhanced.

The invention relates to the technical field of bioinformatics analysis, in particular to an enhanced CT image-based hepatocellular carcinoma postoperative early recurrence multi-modal prediction model and a construction method thereof, and the construction method comprises the following steps: S1, data acquisition: collecting clinical data and liver enhanced CT images of a hepatocellular carcinoma surgical patient, and evaluating the recurrence condition of the HCC patient; s2, image preprocessing: sketching a region of interest in the image, and extracting deep learning features; s3, establishing a model: establishing a plurality of image omics models, constructing a plurality of deep learning models, and constructing a multi-modal model; s4, evaluating and verifying the model: evaluating the prediction accuracy of the multi-modal model by adopting an ROC curve and an AUC value, and evaluating the prognosis prediction capability of the patient by using a Kaplan-Meier curve; according to the method, the characteristic expression ability and the recurrence risk discrimination performance are improved, a scientific basis can be provided for follow-up visit and treatment of the hepatocellular carcinoma postoperative patient, and the lifetime of the patient is prolonged.

The invention provides a prostate cancer biochemical recurrence prediction system based on pathological sections and a construction method. Based on a high-definition panoramic pathological section scanning image of a pathological section after a radical operation, a multi-instance algorithm based on a cyclic cross attention module and a pseudo-packet strategy is adopted, deep features of tumor images with different objective lens multiples are extracted firstly, and then feature representation of a panoramic pathological section scanning image level is generated in a weak supervision network; postoperative biochemical recurrence risk prediction results under different multiples are obtained; and then integrating model results under different scales, and predicting whether the patient finally has biochemical recurrence or not through multi-center verification. According to the system, multi-center, multi-slice and multi-scale panoramic pathological section scanning images are creatively used for training and verification, the risk of prostate cancer recurrence of a patient is predicted through pathological sections after radical treatment, the risk of recurrence of the patient within 3 years and longer time after the radical treatment is accurately predicted, and more personalized treatment is achieved.

The invention discloses a construction method of a risk prediction model for early diagnosis of liver cancer. The construction method of the risk prediction model comprises the following steps: 1, preparing clinical data; 2, processing, analyzing and learning clinical data, and constructing a project database of a hepatocellular carcinoma clinical diagnosis path based on big data; 3, determining main diagnosis key points in the disease timing sequence diagnosis scheme and related indexes influencing the operation, and formulating a diagnosis and treatment path extraction rule; 4, constructing a quality-efficiency evaluation index system for the diagnosis and treatment path set, and calculating the early diagnosis probability and postoperative recurrence probability of the liver cancer; and 5, establishing a Markov model for early screening and diagnosis of liver cancer and risk prediction of postoperative recurrence. According to the method, the ANN principle is utilized, an HCC early diagnosis risk prediction model and an HCC clinical diagnosis and treatment path evaluation system are established, HCC high-risk groups are subjected to early recognition, and the HCC early diagnosis rate is increased; meanwhile, an optimal clinical diagnosis and treatment decision is provided for HCC treatment.

The invention provides a gout diagnosis and recurrence risk prediction method and system based on Raman spectrum and multi-modal machine learning, and the method comprises the steps: generating a Raman spectrum sample data set, and carrying out the preprocessing; performing data dimension reduction and feature extraction on the preprocessed Raman spectrum sample data set to obtain key feature vectors; performing feature screening and enhancement on the key feature vectors before modeling; modeling the data before and after dimension reduction by using a machine learning algorithm to obtain a plurality of machine learning models; and training the multiple machine learning models by using a training set, verifying the models by using a ten-fold cross validation method, selecting the machine learning model with the best discrimination effect as a prediction model, and performing risk prediction of medical history development and gout recurrence. The Raman spectrum and the advanced artificial intelligence technology are integrated, a new prospect is provided for identifying unique metabolic characteristics related to the diseases, the clinical management level of gout is improved, and intervention is earlier and more accurate.

The invention discloses a thyroid tumor classification and recurrence risk prediction method based on deep learning, and belongs to the field of medical image processing, and the method comprises the following steps: collecting and preprocessing multi-modal data related to thyroid follicular carcinoma FTC or follicular adenoma FTA; performing feature extraction on the preprocessed multi-modal data to obtain image features and pathological features; respectively adopting different fusion and optimization methods for the combination of the image features and the pathological features and the image features to obtain a first optimized fusion feature and a second optimized fusion feature; and inputting the first optimized fusion feature into a preoperative classification model to obtain a thyroid tumor type classification probability, and inputting the second optimized fusion feature and the pathological feature into a postoperative recurrence risk prediction model to obtain a recurrence risk score. By means of the multi-mode image classification model and the recurrence risk prediction model which are specially constructed, the sensitivity and specificity of preoperative classification are improved, and accurate individualized management is provided for postoperative patients.

The invention provides a coronary heart disease recurrence risk assessment method and device, equipment and a storage medium, and relates to the technical field of medical data processing. The method comprises the following steps: acquiring dynamic behavior data, physiological data and static risk indexes of a target patient; calculating a treatment compliance index and a rehabilitation health index of the target patient based on the dynamic behavior data and the physiological data of the target patient; and inputting the treatment compliance index, the rehabilitation health index and the static risk index into a trained coronary heart disease recurrence risk scoring model to obtain a coronary heart disease recurrence risk score of the target patient. According to the method, objective and quantitative recurrence risk scores can be obtained, more accurate decision support is provided for clinicians, and early warning and personalized intervention can be realized, so that the prognosis of patients is improved, and the medical cost is reduced.

The invention discloses a cerebral apoplexy recurrence risk monitoring method and device and a medium, and relates to the technical field of medical health monitoring, the cerebral apoplexy recurrence risk monitoring method comprises the following steps: according to a preparation result, collecting electroencephalogram, oxyhemoglobin saturation, electrocardio, pulse waves and acceleration signals, synchronously recording timestamps, and generating multi-modal physiological data; performing de-noising processing and feature extraction on the multi-modal physiological data to generate de-noised feature data; performing multi-modal feature fusion on the de-noised feature data by adopting a convolutional neural network to generate a multi-modal feature vector, identifying feature signal modes of epilepsy, brain structures and brain diseases according to the multi-modal feature vector, calculating a cerebral apoplexy recurrence risk score, and generating a risk score result and an anomaly identification report; and carrying out risk grade division on the risk scoring result and the abnormity identification report according to a recurrence risk threshold value and a personalized judgment rule, and generating risk early warning information and personalized intervention suggestions. According to the invention, real-time and explainable risk early warning information is provided for clinicians and patients.

The invention discloses a model uncertainty-driven man-machine collaborative risk grading interpretation method, system and device and a computer readable storage medium. According to the method, under federated learning deployment, calibration confidence, bucket-level calibration deviation and multi-model inconsistency are simultaneously calculated for a single sample, and a comprehensive uncertainty score is formed to perform risk grading: when the comprehensive uncertainty score exceeds a threshold value or the calibration confidence is insufficient, manual re-checking is automatically triggered; otherwise, directly outputting the AI result. Artificially confirmed samples enter a feedback sample library for subsequent federation retraining, temperature parameters and barrel counting are periodically updated, and a continuous learning closed loop is constructed. According to the scheme, in medical scenes such as lung CT nodule detection and lung cancer I-stage recurrence risk prediction, the diagnosis efficiency and clinical safety are effectively considered, and the long-term stability and credibility of the model are improved.

The invention discloses a depression recurrence risk intelligent early warning method, system and device based on multi-modal data and artificial intelligence, and relates to the field of depression classification early warning, and the method comprises the steps: carrying out the individualized deviation calculation based on the baseline features and multi-modal brain image features of a target patient, obtaining an individual multi-modal brain image deviation feature vector; dimension reduction processing is carried out on the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features, the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features are input into a pre-trained layered integrated classification model and a pre-trained layered integrated risk early warning model, and depression subtype classification tags and risk probabilities are obtained; in the pre-training process of the hierarchical integration classification model and the hierarchical integration risk early warning model, multi-modal feature fusion and hierarchical integration learning strategies are adopted, and samples from a plurality of data centers are used for model training and verification. According to the method, the accuracy and individualization degree of classification and early warning are improved, and the generalization ability and robustness of the model are also improved.

The invention relates to the technical field of health risk prediction, in particular to a metabonomics-radiomics prediction method for chronic subdural hematoma recurrence risk, which comprises the following steps: acquiring a CT image and extracting edge gray fluctuation, constructing fluctuation parameters in combination with metabolome data, screening coordination characteristics to generate a risk combination, and predicting a chronic subdural hematoma recurrence risk. Feature pairs consistent in trend are extracted to form a collaborative channel, and a feature matrix is constructed to generate an input vector set; according to the method, disturbance features are extracted through a CT image edge gray level path, a cross-modal fluctuation trend comparison mechanism is established in combination with patient brain metabolism indexes, biological consistency between the features is enhanced, feature combinations with uncoordinated changes are eliminated, feature pairs with collaborative structure and function trends are screened, and a linkage path is constructed. The evolution relation from structural disturbance to metabolic response is reflected, channel data sorting and recombination improve the difference of input characteristics, the stability and accuracy of recurrence discrimination are enhanced, and the systematicness and interpretability of risk assessment are improved.

The invention discloses an application of a training immune agonist in preparation of a drug for sensitizing radiotherapy treatment of tumors, and researches show that the training immune agonist significantly activates immune response of a body by promoting expression of inflammatory cytokines, enhancing phagocytic ability of macrophages, recruiting immune cells and amplifying an immune activation effect, so that the immune response of the body is enhanced. The sensitivity of tumor cells to radiotherapy is improved, and a dual mechanism of'radiotherapy sensitization-immune synergy 'is formed, so that the bottleneck of the traditional radiotherapy curative effect is broken through, the local control rate of radiotherapy is improved, and the recurrence risk is reduced. Experiments prove that the training immune agonist combined with radiotherapy has a remarkable treatment effect on various highly invasive tumors (such as brain glioma and lung cancer brain metastasis) and common malignant tumors (such as colon cancer and lung cancer), and shows wide applicability. The invention provides a new combined therapy direction for tumor treatment, is expected to improve the prognosis of patients, prolong the lifetime and improve the life quality, and has important clinical transformation value.

The invention relates to the technical field of image analysis, in particular to a training set expansion method for cerebral apoplexy patient recurrence risk assessment, and the method comprises the steps: obtaining an initial training set composed of historical images under different recurrence risk levels; clustering is carried out based on similar conditions among different historical images, and a recurrence risk level corresponding to each target cluster is determined; dividing the historical images in each target cluster; determining a target core degree corresponding to each core possible image in each target cluster; determining a feature satisfaction degree corresponding to each core possible image in each target cluster; and screening out a target core image from each target cluster according to the target core degree and the feature satisfaction degree corresponding to all the core possible images in each target cluster, and expanding the initial training set based on all the target core images. According to the method, the historical images are analyzed, so that the training set extension is realized, and the rationality of the training set extension is improved.

The invention discloses an ERCP postoperative common bile duct calculus recurrence prediction system based on machine learning, and the system comprises the following steps: a data collection module which is used for forming an original clinical data set; the data preprocessing module is used for constructing a standardized preprocessed clinical data set; the feature construction and hyper-parameter search space definition module is used for defining a hyper-parameter search space of the improved bidirectional gating loop unit network model; the improved bidirectional gating circulation unit network model construction module is used for outputting a recurrence risk prediction value; the grey wolf optimization hyper-parameter tuning module is used for finally generating an optimized improved bidirectional gating circulation unit network model; the model training and prediction module is used for constructing a risk classification rule according to the prediction probability and the postoperative key indexes; and the individualized result output module is used for outputting the prediction result and pushing the prediction result to a doctor end. The method has significant clinical value in early recognition of high-risk individuals in actual deployment.

The invention discloses a wide-neck aneurysm mechanical release embolism spring ring device and a using method thereof, and belongs to the technical field of medical instruments. The device comprises a catheter, a catheter channel is arranged on the inner wall of the catheter, and a thrombus removal mechanism and a protection mechanism are arranged in the channel. The bolt releasing mechanism realizes quick bolt releasing of the spring ring through cooperation of a sliding shaft, a clamping plate and other parts; the protection mechanism can prevent the spring ring from being popped up accidentally due to the fact that the releasing wire falls accidentally. When in use, the catheter is pushed to a cerebrovascular segment where an aneurysm is located under the guidance of X-ray fluoroscopy and a contrast agent, a proper spring ring is selected and pushed to a preset position, the spring ring is released through a series of operations to complete embolism, and the catheter is withdrawn after the position is determined to be stable. The problems that when a spring ring is released through traditional electrolytic fusing, the position is difficult to adjust accurately, the packing effect is affected, and the recurrence risk is increased are solved, the spring ring can be released rapidly and accurately, an operation can be completed in time, and the operation risk is reduced.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention discloses a rectal cancer postoperative recurrence risk prediction system and method based on multi-modal time sequence data, and relates to the technical field of medical artificial intelligence. The system comprises a data acquisition and preprocessing module, a feature extraction module and a multi-modal feature fusion and modeling module. The method comprises the following steps: constructing a cross-modal data set containing time sequence clinical data, a time sequence MR image and a biopsy digital pathological image; respectively extracting clinical features, radiomics and deep learning features of the MR image, and nucleus morphology and spatial distribution features of the pathological image; and fusing all the features by using a Transform network, and constructing a prediction model. According to the method, macroscopic images, micropathology and dynamic time sequence information are integrated, tumor heterogeneity is comprehensively quantified, the problem that prediction of a single-mode static model is not accurate is solved, the postoperative recurrence risk of the stage III rectal cancer patient can be evaluated more accurately, and clinical treatment decision making is assisted.

The invention provides a kidney cancer recurrence risk prediction method based on a deep learning model, and relates to the technical field of deep learning, and the method comprises the steps: collecting an image data set for kidney cancer high recurrence risk prediction; carrying out registration on the collected multi-stage enhanced CT image; constructing and training a kidney tumor automatic detection and segmentation model; carrying out ROI positioning cutting and quality control; and constructing a deep learning model for renal cancer recurrence risk prediction based on the multi-modal convolutional neural network, and realizing renal cancer recurrence risk prediction through the constructed prediction network model. According to the method, the multi-phase enhanced CT image of the kidney cancer patient is analyzed through the deep learning model, the tumor postoperative recurrence risk is predicted, an objective basis is provided for a clinician to make an individualized follow-up visit scheme and an auxiliary treatment decision, and excessive treatment of a low-risk patient and insufficient treatment of a high-risk patient are avoided.

The invention discloses a method for training a stroke recurrence risk prediction model and a related product. The method comprises the following steps: acquiring cerebral perfusion image data, clinical data and follow-up visit data of a patient; the follow-up visit data comprises follow-up visit time and a survival state of the follow-up visit time; the survival state comprises whether stroke relapse occurs or not, whether sequelae exists or not, whether complications exist or not and whether death exists or not; data integration is carried out on the follow-up time and the survival state of the follow-up time to obtain at least one survival time-event indication pair, the event indication is used for identifying whether a preset event occurs in the survival time, and the preset event is stroke relapse; and inputting the brain perfusion image data, the clinical data, the follow-up visit data and the at least one survival time-event indication pair into the stroke recurrence risk prediction model as training data to train the stroke recurrence risk prediction model. By using the scheme disclosed by the invention, the prediction precision of the stroke recurrence risk can be improved.

The invention discloses a sugar chain marker combination for predicting the recurrence risk of a gastric cancer patient after treatment and application of the sugar chain marker combination. The invention discovers that the levels of six specific N-sugar chains (NGA2F, NG1A2F, NA3, NA3Fb, NA4 and NA4Fb) in blood are obviously related to relapse during definite diagnosis of gastric cancer for the first time. On the basis, a gastric cancer recurrence risk prediction model GC-GPSS is constructed through Cox regression. According to the model, individual risk scores are calculated by using a quantification formula containing a fixed regression coefficient (beta), and patients are divided into a high-risk group and a low-risk group according to a preset threshold. A verification result shows that the model can effectively distinguish patient groups with different recurrence risks. The marker combination, the prediction model and the system provided by the invention provide a brand new tool for realizing earlier and more objective recurrence risk assessment in the early stage of gastric cancer diagnosis and treatment, and have important clinical application value.