472 results about "Treatment response" patented technology

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The invention discloses a liver disease diagnosis and treatment strategy recommendation method based on a knowledge graph, a medium and equipment, and the method comprises the steps: collecting the basic information of a user, constructing a space-time correlated individual liver disease knowledge graph according to the basic information of the user, and enabling the knowledge graph to comprise a disease cause feature node, a pathology grading node, a complication early warning node and a treatment response node which are correlated with each other; inputting the individual hepatopathy knowledge graph into a hierarchical reinforcement learning model for joint reasoning, and outputting multiple groups of strategy options including an antiviral treatment scheme, a liver protection drug combination and a metabolic intervention measure; analyzing a topological propagation path of a complication early warning node through a complication prediction sub-model, and dynamically adjusting an initial candidate diagnosis and treatment strategy set; and updating the individual hepatopathy knowledge graph when obtaining the basic information of the new user. According to the method, multi-dimensional data fusion and dynamic strategy optimization of liver disease diagnosis and treatment are realized, and the accuracy and timeliness of a diagnosis and treatment scheme are remarkably improved.

The invention discloses an intestinal cancer immune response graph neural network prediction method, a medium and equipment, and the method comprises the steps: collecting pathological image information, immunodetection information and basic clinical information, extracting a tissue space distribution characteristic spectrum through a deep convolutional network, and constructing a graph neural network model in combination with an immunomarker expression characteristic matrix; spatial interaction characteristics of a tumor microenvironment are modeled by adopting a graph attention mechanism, finally a treatment response probability, an optimal treatment opportunity and an adverse reaction risk are predicted through a multi-task learning framework, and a clinical decision report containing a prediction response curve, a risk early warning threshold and a treatment time window suggestion is output. According to the method, through multi-modal data fusion and spatial interaction modeling, accurate prediction of intestinal cancer immunotherapy response is realized, and a more comprehensive reference basis is provided for clinical decision making.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention discloses a treatment response prediction method and system, a storage medium and a terminal, and the method comprises the steps: carrying out the heterogeneity analysis and cell division of first single cell sequencing data of a patient through employing a clustering algorithm, and obtaining a plurality of corresponding cell subgroups; performing feature extraction and biomarker recognition on all the cell subgroups to obtain corresponding cell subgroup features; wherein the cell subset characteristics comprise a cell development trajectory and a plurality of cell markers; and inputting the clinical information and the cell subset characteristics of the patient into the treatment response prediction model, so that the model performs multi-factor analysis and outputs a treatment response prediction result of the patient for each treatment scheme. By adopting the embodiment of the invention, the tiny key difference between the cells is captured, the cells are divided based on the difference between the cells, and then feature extraction and biomarker recognition are performed on each cell subset, so that the specific gene expression modes of different cell subpopulations corresponding to diseases can be recognized, and the treatment response prediction accuracy is further improved.

The invention discloses an AI-based child nebulization treatment management system and method, and relates to the field of treatment scheme management, and the system comprises a management and control module which is used for obtaining the medical general archive access and read-write authority of a target medical place, carrying out the editing and sending of a control instruction for each functional module, and sending the control instruction to the medical general archive access and read-write authority to the medical general archive access and read-write authority to the medical general archive access and read-write authority; summarizing and storing working results and analysis data of the functional modules; the data acquisition module is used for acquiring physique data, illness state data and treatment plans of the patient in the current period based on a medical general archive; the feature selection module is used for performing feature recognition on the data acquired by the data acquisition module by adopting a multi-modal feature coupling algorithm; by collecting and analyzing physical characteristics, disease progression, historical treatment response and feedback information of a patient in real time, a personalized treatment scheme is formulated according to specific conditions of each child, a general treatment mode is avoided, unnecessary side effects are reduced, and treatment effectiveness and safety are improved.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

The invention provides a tumor treatment effect prediction method based on multi-modal data, and relates to the field of medical artificial intelligence. The method comprises the following steps: collecting magnetic resonance image data, histopathological section images and clinical baseline data of a tumor patient and a treatment response evaluation record after neoadjuvant chemotherapy; the collected image information is preprocessed; feature extraction is carried out based on the preprocessed image information; screening a feature subset with the highest prediction value from the extracted features; constructing a radiopathomics fusion model by using the feature subset with the highest prediction value; evaluating the radiopathomics fusion model; and predicting the tumor treatment effect by using the evaluated radiopathomics fusion model. The problems that an existing tumor neoadjuvant chemotherapy (NACT) curative effect prediction method is low in accuracy, poor in generalization ability, lack of multi-modal data fusion and insufficient in model interpretability are solved.

The invention discloses a sugar chain marker combination for predicting prognosis of liver cancer transarterial chemoembolization and application of the sugar chain marker combination. The invention discovers and verifies that the combination composed of NA3Fb, NA4Fb and NA4F2b can be used as a novel marker for evaluating TACE treatment reaction and prognosis by analyzing a serum sugar chain map of a liver cancer patient for the first time. Based on the marker combination, a TACE-GT prognosis scoring model and system are established, and the progression risk of a patient within 12 months after a TACE operation can be effectively distinguished by noninvasively detecting the expression levels of three carbohydrate chains in serum of the patient and calculating a specific score. The detection method provided by the invention is based on a blood sample, and has the advantages of noninvasiveness and convenience, and a risk assessment system can provide individualized curative effect prediction and decision support for clinicians before and after TACE treatment, and has important values for realizing precise treatment of liver cancer and improving prognosis of patients.

The invention discloses an establishment method and application of a mouse gastric cancer orthotopic transplantation model, and the establishment method comprises the following steps: injecting a mouse gastric adenocarcinoma cell line MKC1 (Mus musculus) into a mouse gastric tissue, and feeding to obtain the mouse gastric cancer orthotopic transplantation model. Compared with the prior art, the mouse gastric cancer in-situ model established in the invention has the advantages of strong repeatability in the construction process and good passage stability, especially can form tumor subcutaneously in a C57BL / 6 mouse with a sound immune system, has a therapeutic response to immunotherapeutic drugs such as anti-PD-1 monoclonal antibody, and is suitable for research on gastric cancer immune microenvironment and drug resistance mechanism of immunotherapeutic drugs.

The invention discloses an application of a biomarker in preparation of a kit for a prognosis risk early warning method of an acute ST segment elevation type myocardial infarction patient. The biomarker comprises an HBP (Hepatitis Binding Protein). By monitoring the change of the HBP protein level, whether a patient suffering from the acute ST segment elevation type myocardial infarction has poor prognosis within 30 days after a PCI operation can be effectively evaluated, so that early recognition and timely clinical intervention on high-risk patients are realized, the treatment response speed and effect are improved, the occurrence rate of adverse cardiovascular events is reduced, and the treatment effect of the patient suffering from the acute ST segment elevation type myocardial infarction is improved. And the prognosis of the patient is finally improved.

The invention relates to the technical field of medical decision and artificial intelligence, and discloses an artificial intelligence-driven new adjuvant therapy scheme optimization method and system for antral sinus cancer, and the method comprises the steps: S1, obtaining clinical data of a antral sinus cancer patient, and carrying out the preprocessing; s2, screening core features by adopting an iterative ReliefF algorithm, and constructing composite features; s3, constructing a prediction model based on a Bayesian optimization K-nearest neighbor algorithm, taking the composite features as input of the prediction model to predict treatment response, and outputting individual tumor recession grade probability distribution of the patient; s4, taking the tumor recession grade probability distribution as a core component of a state space, inputting the tumor recession grade probability distribution into a double-layer reinforcement learning framework based on a penalty mechanism to optimize a treatment decision, and outputting a personalized treatment strategy parameter set; and S5, performing real-time scanning and verification on the drug dosage parameters in the strategy parameter set, and outputting a final approved personalized treatment scheme. The precision and intelligent level of gastric antrum cancer treatment is improved.

The disclosure relates to methods and kits for detecting tau, e.g., tau that is phosphorylated at amino acid position T181 (pTau181), tau that is phosphorylated at amino acid position T217 (pTau217), and / or total tau. The disclosure further provides methods for distinguishing between individuals whose cognitive condition will remain stable and whose cognitive condition will decline during their lifetime. The disclosure also provides methods for determining the eligibility of individuals for participation in clinical trials for Alzheimer's disease treatments. Also provided are methods for distinguishing between individuals with Alzheimer's disease and non-Alzheimer's dementia, and for monitoring response to treatment for Alzheimer's disease.

The invention relates to an ultra-long-range medical record diagnosis and treatment question answering system and method based on a dynamic time sequence semantic map, belongs to the technical field of medical artificial intelligence and information processing, and solves the problems that in the prior art, the inference ability is insufficient when ultra-long-range medical records are processed. The system comprises a pre-processing module for pre-processing overlong-range medical record information of a patient to generate standardized data; the medical entity recognition module is used for recognizing event entities and descriptive entities in the standardized data; associating a time attribute for the event type entity to generate a time sequence entity tuple; outputting a medical entity recognition result; the dynamic semantic map construction module is used for constructing a specific dynamic semantic map of the corresponding patient; the text-map dual-mode fusion query engine is used for analyzing the medical problem proposed by the user and carrying out text-map dual-mode fusion query based on the specific dynamic semantic map; and the diagnosis and treatment reply module is used for generating a diagnosis and treatment reply result according to the text-map dual-mode fusion query result.

A system for personalized treatment, the system including an input device, at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to retrieve physiological data from at least the input device associated with a user, wherein the physiological data further includes a previous ailment, receive an ailment inquiry from the user generate a treatment response as a function of the physiological data and the ailment inquiry including inputting the ailment inquiry into a large language model and generating the treatment response as a function ailment, inquiry, the physiological data and the large language model and transmit the treatment response to at least the input device.

The invention discloses a drug-resistant marker for treating prostatic cancer olaparib and application of the drug-resistant marker, and relates to the technical field of medicine, and the marker is a PCAR1 gene. Research finds that the PCAR1 gene is remarkably and highly expressed in prostate cancer Olaparil drug-resistant cells and clinical drug-resistant samples, and can be used as a key marker for judging the drug resistance of prostate cancer patients to Olaparil. By detecting the expression level of the PCAR1 gene, the treatment reaction of a patient to the olaparil can be effectively predicted, and a basis is provided for clinically and accurately selecting a treatment scheme. Meanwhile, the specific siRNA designed aiming at the PCAR1 gene can obviously inhibit the expression of the PCAR1 gene, and can effectively reduce the drug resistance of cancer cells to olaparione, promote the apoptosis of the cancer cells and inhibit the proliferation of the cancer cells when being combined with the olaparione for use. Based on the siRNA, the invention prepares a pharmaceutical composition containing the siRNA and olaparil, and provides a new drug combination scheme for the treatment of prostatic cancer.

An ensemble of predictive models that elucidate how cancer mutations impact the response to common replication stress-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multi-drug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK (receptor tyrosine kinase)-JAK-STAT assembly governing resistance. This invention defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.

Computer-implemented method of predicting whether a tumour is likely to acquire a gene amplification are provided, the method comprising obtaining a tumour copy number profile for the tumour; determining, for each of one or more signatures of chromosomal instability, a metric indicative of the presence of the signature in the tumour copy number profile, and determining whether the tumour is likely to acquire a gene amplification based on the output of a model trained to take as input the values of said metrics and produce as output a score indicative of the likelihood that the tumour will acquire a gene amplification. Methods of predicting treatment response and prognosis, and related systems and products are also described.

The invention discloses a CAR-T treatment response intelligent prediction system based on medical images, and relates to the field of medical image analysis and tumor immunotherapy, and the system comprises a data integration module, a multi-modal feature extraction module, a prediction model construction module, a model optimization module, a clinical analysis module and an adverse reaction risk early warning module. The system integrates PET-CT, MRI, ultrasound and other multi-mode medical image data, and extracts the form, texture and metabolic characteristics of the tumor; constructing a treatment response prediction model by adopting a three-dimensional convolutional neural network; innovatively applying a differential geometry theory, constructing a medical image manifold structure and generating a tumor adversarial sample maintaining topological characteristics, and executing an adaptive optimization strategy by using a Riemannian metric space; accurate prediction of CAR-T treatment response of a lymphoma patient is realized, and adverse reaction risk early warning is carried out through brain and visceral organ image analysis.

The invention belongs to the technical field of prognosis models, and particularly relates to a diffuse large B-cell lymphoma risk prognosis model based on lactic acid metabolism related genes. The model construction comprises the following steps: S1, data acquisition and processing; s2, constructing a prognosis model; s3, verifying and evaluating the prognosis model; and S4, application of a prognosis model. GSE10846 and GSE87371 data sets are used for screening lactic acid metabolism related genes (LMRGs), a prognosis risk model is constructed and verified, and association of the prognosis risk model with clinical pathological characteristics, immune characteristics and treatment response is disclosed.

The invention provides an intelligent auxiliary diagnosis and treatment service system and method based on a large language model, and the system comprises a large language model engine and a medical knowledge dynamic updating device, and the medical knowledge dynamic updating device carries out the automatic and quantitative arbitration of the conflict between the new knowledge extracted from the literature and the existing knowledge of a knowledge graph, and carries out the automatic and quantitative arbitration of the conflict. A conflict detection unit identifies conflict knowledge pairs with mutually exclusive logic, an evidence intensity quantification unit calculates and synthesizes document timeliness, source authority and research type evidence grade multi-dimensional evidence intensity scores for two conflict parties, and a grading arbitration decision unit compares a score difference value with a dynamic threshold value. And making a grading decision of automatically adopting a high-score party, generating an artificial review task or marking dispute coexistence, and finally executing updating by a knowledge graph updating unit. The invention further provides a method corresponding to the system, double-chain collaboration of online diagnosis and treatment response and offline knowledge evolution is achieved, and the problems of automation and intelligentization of conflict processing in medical knowledge updating are solved.

The invention relates to the technical field of medical image analysis, and discloses a breast cancer treatment response prediction system based on multi-time-sequence images. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

The present disclosure relates to methods and systems for providing treatment response predictions based on full slide images. Embodiments disclosed herein relate to a computer-implemented method and corresponding system for providing treatment response prediction (TRP) for a patient suffering from a cancerous disease. Methods and systems are based on obtaining (S10) a full slide image (WSI) of a patient showing a tissue sample associated with a cancerous disease; providing (S20) a prediction function (PF) configured to derive a treatment response prediction (TRP) for one or more treatment options from the full slide image (WSI); and applying (S30) the prediction function (PF) to the full slide image (WSI) in order to provide a treatment response prediction (TRP).

The invention discloses an intelligent VTE prevention and control system, and the system comprises a risk assessment module which is used for obtaining a risk index of VTE according to a risk factor; the preventive measure recommendation module is used for recommending preventive measures according to the clinical data; the VTE early warning module is used for acquiring an early warning index of the VTE according to the iconography data, the laboratory examination result and the clinical symptoms; and the treatment scheme optimization module is used for adjusting the treatment scheme according to the individual characteristics, the treatment reaction and the drug interaction, and helping a doctor to provide an auxiliary reference result for the illness state of the patient and subsequent treatment.

The invention relates to the field of medical image processing and artificial intelligence, in particular to a pulmonary nodule treatment effect AI evaluation system which comprises an image acquisition module, an image registration module, a feature representation module, a multi-scale analysis module, a trajectory analysis module, a response prediction module and a decision support module. According to the system, accurate alignment of CT images before and after treatment is realized through a 4D registration technology, manifold representation of a pulmonary nodule state is constructed based on a differential geometry theory, and nodule features are mapped into points on a high-dimensional manifold; extracting features of different time and space scales by adopting multi-scale space-time analysis, and constructing a manifold trajectory representing a treatment response process; geodesic prediction is realized by using a Riemann geometric framework, and long-term curative effect is predicted from early treatment response; the system not only evaluates the current treatment effect, but also can provide personalized treatment suggestions and optimal follow-up visit plans.

A high dose rapidly dispersing three-dimensionally printed dosage form comprising a high dose of water soluble drug in a porous matrix that disperses in water within a period of less than about 15 seconds is disclosed. Also disclosed are methods of preparing the dosage form and of treating a condition, disease or disorder that is therapeutically responsive to the drug.

Methods of treatment with a myosin inhibitor using protein levels; methods of monitoring response to treatment based on such protein levels; and methods of determining whether to treat a patient with a myosin inhibitor based on such protein levels are described herein. Such methods may include obtaining a biological sample of a subject, measuring one or more protein levels in the sample, analyzing the one or more protein levels, and determining the treatment response based on the analysis, and may further include treating the subject based on the determination of treatment response.

The present invention provides a computer-implemented method for predicting the treatment response of a subject having a lung cancer to an immune checkpoint inhibitor (CPI) therapy, the method comprising: providing a mutation profile of the subject, said profile comprising the presence or absence of cancer-specific mutations at one or more locations in at least five genes selected from the group consisting of: NF1, STK11, TSC2, BRCA2, BRAF, STAG2, U2AF1, BRIP1, PDGFRA, CTNNA1, PDK1, FGF10, and FLT1; analysing the mutation profile to classify the profile as matching the mutation profile of a response signature or a resistance signature, wherein the subject is predicted to be likely to respond to the CPI therapy if the mutation profile for the subject is classified as matching the mutation profile of the response signature and is predicted to be likely not to respond to the CPI therapy if the mutation profile for the subject is classified as matching the mutation profile of the resistance signature. Also provided are related methods and systems for predicting the treatment response of a subject having a lung cancer to an immune checkpoint inhibitor (CPI) therapy.

The present disclosure provides a method and a gene detection panel for evaluating treatment response, recurrence and survival by detecting genetic variants and their changes before and after concurrent chemoradiotherapy in tumor tissues of patients with esophageal cancer. The present disclosure develops a set of esophageal cancer NGS analysis panel. Aiming at 402 mutation sites including 35 genes that frequently occur in esophageal squamous cell carcinoma tissue cells, 62 pairs of esophageal squamous cell carcinoma tissues before and after CCRT are analyzed for specific site variation, hoping to find new predictive markers. The present disclosure combines these potential markers into an esophageal cancer detection panel, which has extremely high value for improving the prognosis of esophageal cancer.

The invention discloses a longitudinal CT image-assisted rectal cancer neoadjuvant radiotherapy and chemotherapy treatment reaction evaluation device, which comprises a focus area segmentation module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module and a rectum focus area detection module, obtaining a standardized three-dimensional tumor area; a feature fusion module extracts deep learning features of a focus area after segmentation at two time points before neoadjuvant radiotherapy and chemotherapy and before an operation from the three-dimensional tumor area, and performs weighted fusion on the deep learning features at the two time points through a dynamic attention weight layer to obtain longitudinal comprehensive features representing tumor treatment response changes; and the prediction module fuses the longitudinal comprehensive characteristics and clinical baseline data to construct a multi-modal prediction model, and outputs a treatment response prediction result of the rectal cancer patient on neoadjuvant chemoradiotherapy, so that the prediction accuracy of pathological complete remission of the rectal cancer patient can be improved, and the prognosis of the patient and the utilization efficiency of medical resources can be improved.