47 results about "Treatment decision making" patented technology

The invention provides a dental implant data monitoring analysis method based on deep learning, and the method comprises the steps: collecting the multi-modal structural data of a patient, such as preoperative images, clinical examination and surgical parameters, carrying out the standardized preprocessing, and inputting the data into a deep neural network model with a double-branch structure, thereby achieving the extraction of the implant failure risk score and local causal association strength; on the basis of model output, a dynamic individualized causal map is constructed, key clinical intervention variables serve as regulation and control nodes, anti-fact disturbance simulation is carried out, variable intervention sensitivity is quantified, and a key causal path is analyzed; according to the method, a priority intervention suggestion list and an auxiliary decision report are generated, the causal atlas can be adaptively optimized according to actual follow-up feedback, long-term closed-loop evolution is realized, the accuracy of implant failure risk prediction and the pertinence and operability of an intervention scheme are improved, and clinical individualized treatment decision support is enhanced.

Embodiments pertain to a method of assessing the cardiometabolic health of a subject by (1) receiving a plurality of parameters of the subject, where the plurality of parameters includes a T2 relaxation time constant of a subject's blood (T2 value) and one or more additional parameters; and (2) feeding the plurality of parameters into an algorithm, where the algorithm correlates the plurality of parameters to the subject's cardiometabolic health. The methods may also include a step of communicating cardiometabolic health risk. The methods may also include a step of implementing a treatment decision. Further embodiments pertain to system for assessing the cardiometabolic health of a subject in accordance with such methods.

The present disclosure encompasses methods for molecularly subtyping formalin-fixed paraffin-embedded tumor samples. The disclosure works particularly well for old and degraded (archival) samples for which standard methods are unfeasible. Further, the methods disclosed allow for the correlation of patient outcome data with the molecular subtype of the tumor and provides a wealth of information which will guide treatment decisions and / or selection of therapeutic agents.

The application provides a cross-institutional referral intelligent routing method and system based on a medical intelligent agent middleware, relates to the technical field of medical informatization, and comprises the following steps: establishing a sign data monitoring window, mapping sign index changes and medication record time sequences, constructing a drug reaction sensitivity curve to calculate a treatment effective coefficient; adopting a dynamic time warping algorithm to match a drug-sign correlation graph, and generating a treatment decision tree; and predicting disease development, identifying the best referral opportunity and target institution based on the treatment effective coefficient and specialty characteristics. The application improves cross-institutional referral efficiency, optimizes medical resource allocation, and enhances patient treatment effect.

The disclosure relates to targeted Chromatin Accessibility and Mutation Sequencing (tCAM-seq) for parallel analysis of genetic and epigenetic analysis of patients with pancreatic adenocarcinoma (PDAC) to 1) determine responsiveness to standard of care chemotherapy, and 2) identify actionable allelic variant mutations that can be used to guide treatment decisions. Prognostic methods and treatment strategies are also provided.

A method of detecting disease through analysis of hair images can indicate specific diseases is an intriguing topic intersecting dermatology, diagnostic medicine, and technology. By obtaining a hair from the body and analyzing images for fungus as an indicator of various health conditions. Disclosed herein is a methods relating to the classification of hair samples which can be used to identify and to diagnose conditions or to support treatment-related decisions.

Devices, systems, and methods for monitoring of heart failure patient fluid volume with improved accuracy and reduced lag time to identify changes in volume status using one or more of passive, implantable sensors and non-invasive receiving equipment that measure the temporal variation in IVC area as a means to remotely assess and manage heart failure disease status and support self-management optionally employing a new, normalized index of heart failure (HF) congestion. The new normalized HF congestion index further contributes to improved speed and accuracy of heart failure diagnostic and treatment decisions and thus can help to improve patient outcomes and reduce hospitalization occurrences and costs.

The invention belongs to the technical field of intelligent medical treatment and clinical decision, and particularly relates to an idiopathic membranous nephropathy prognosis prediction method and system based on deep learning and a cross-modal attention mechanism. In order to realize accurate prognosis evaluation of IMN, depth features of a kidney tissue pathological image are automatically extracted through a deep learning technology and are innovatively and deeply fused with clinical data of a patient, and finally a high-precision and explainable intelligent prediction model is constructed to guide clinical early intervention and individualized treatment decision.

Embodiments of the present disclosure pertain to methods of assessing glioma in a subject by (1) receiving a plurality of measured biomarker levels of the subject; and (2) assessing glioma in the subject based on the measured biomarker levels by at least correlating differentially expressed levels of the measured biomarkers to at least one of (a) diagnosis of glioma, (b) progression of glioma, (c) treatment outcome, and / or (d) tumor burden. The methods of the present disclosure also include a step of (3) making a treatment decision based on the assessment. Additional embodiments of the present disclosure pertain to systems for assessing glioma in a subject.

The invention provides a therapeutic response model for acute myelogenous leukemia (AML) accompanied with myelodysplastic syndrome related genetic abnormality (MRGA) and application thereof, a survival risk scoring model and application thereof, and the survival risk scoring model is constructed based on the lifetime, cytogenetics abnormality, gene mutation and clinical factors of a patient. Patients are divided into a low-risk group, a medium-risk group and a high-risk group through the model, the AUC value of the patients reaches 0.789-0.831 in the period of 1-3 years, and the clinical practicability of the model in the aspect of making AML treatment decisions based on myeloid associated dysplasia (MRGA) is proved through remarkable survival differences among different risk groups. In addition, a combined scoring system for the treatment response and lifetime of the MRGA AML patient is further developed, and a new technical support is provided for clinical decision-making of the MRGA AML.

The application provides a palate midline suture maturity prediction method based on deep learning, obtains a plurality of head lateral film image samples, respectively labels palate midline suture maturity information, and obtains a head lateral film image dataset; a palate midline suture maturity prediction model is constructed, the palate midline suture maturity prediction model comprises an image preprocessing module, a feature extraction module, a hybrid multi-scale attention convolutional neural network and a feature fusion module; the head lateral film image dataset is used to train the palate midline suture maturity prediction model, and a trained palate midline suture maturity prediction model is obtained; after a head lateral film image to be predicted is input into the trained palate midline suture maturity prediction model, a palate midline suture maturity prediction result is obtained; the application can reduce subjective differences of artificial interpretation, can improve the accuracy, consistency and reliability of bone age prediction, can realize non-invasive, objective and efficient palate midline suture maturity prediction, and provides a scientific basis for clinical expansion arch treatment decision-making.

The invention belongs to the technical field of intelligent medical treatment, and discloses a gastric cancer far-end metastasis prediction method and system based on multiple examinations, and the method comprises the steps: firstly obtaining imaging, endoscope and blood examination data of the same patient; mapping each check original feature to a predefined unified semantic concept set, and converting the check original features into a unified dimension semantic embedding vector through a modal encoder adapted to a data type; then, a cross-modal semantic alignment module is constructed, and the same-semantic different-source vector distance is shortened based on comparative learning; semantic conflicts are detected, credibility weights of all sources are dynamically distributed, and same-semantic multi-source vectors are weighted and fused according to the weights to obtain fused feature vectors; and finally inputting a downstream prediction model to output a transfer prediction result. The method solves the problem of semantic conflict of multi-modal examination data, improves the prediction accuracy and stability, and provides a reliable basis for clinical treatment decision of gastric cancer.

A method and system for tracking movement includes performing person detection (204) in frames from multiple video streams to identify detected images. Visual and location information from the detected images is combined (206) to generate scores for pairs of detected images across the multiple video streams and across frames of each video stream. A pairwise detection graph is generated (210) using the detected images as nodes and the scores as weighted edges. Movements of individuals are tracked (212) based on a constrained answer set programming problem determined based on matching scores and logical assumptions. Actions corresponding to the tracked movements are performed (606). Tracking patient movements in a healthcare facility can be used to inform treatment decisions by medical professionals.

The present application relates to a kind of insomnia patient rTMS curative effect prediction method based on electroencephalogram microstate, comprising: step 1: obtaining data set, wherein the data set includes the baseline electroencephalogram microstate index of multiple insomnia patients and corresponding rTMS treatment effect category label;Step 2: establish curative effect prediction model, utilize data set to train curative effect prediction model;Step 3: according to the curative effect prediction model of training completion, the treatment effect of rTMS of insomnia patient to be predicted is predicted.The insomnia patient rTMS curative effect prediction method based on electroencephalogram microstate proposed in the present application, by carrying out rTMS treatment insomnia experiment to construct data set, further establish reliable curative effect prediction model, utilize the curative effect prediction model can accurately predict the treatment effect of rTMS to individual, can provide individualized treatment decision information and reasonable medical advice for insomnia patient.

The application provides a dental implant data monitoring analysis method based on deep learning, which realizes implant failure risk scoring and local causal correlation strength extraction by collecting preoperative images, clinical examination, surgical parameters and other multi-modal structured data, standardizing preprocessing and inputting a deep neural network model with a double-branch structure; based on the model output, a dynamic individualized causal graph is constructed, key clinical intervention variables are used as regulation nodes, and counterfactual disturbance simulation is carried out to quantify variable intervention sensitivity and analyze key causal paths; a priority intervention suggestion list and an auxiliary decision report are generated, and the causal graph can be self-adaptively optimized according to actual follow-up feedback to realize long-term closed-loop evolution; the application improves the accuracy of implant failure risk prediction and the pertinence and operability of intervention schemes, and enhances clinical individualized treatment decision support.

The invention discloses an intelligent traditional Chinese medicine packet treatment system based on multi-physics field dynamic coupling, a preparation method and a regulation and control method, and relates to the technical field of intelligent traditional Chinese medicine instruments. The far infrared bag comprises a graphene heating sheet and a double-PID temperature control module; the composite magnetic therapy bag contains Al2O3 core-shell structure neodymium iron boron particle magnetic powder formed by plasma vapor deposition; the four-point magnetic coupling positioning mechanism is composed of an annular N-S pole alternating magnetic snap group and a Hall sensor array; and the biocompatible isolation cloth is assembled on the human body contact side of the composite magnetic therapy bag and is used for isolating the composite magnetic therapy bag from the human skin. The system has the advantages of high temperature control precision, high field domain coupling efficiency, strong high-temperature stability of the magnetic powder, short clinical treatment course, low safety risk, high intelligent degree and the like, and can be widely applied to the fields of medical rehabilitation, primary medical treatment, family health, sports medicine and the like.

This invention discloses a method and system for rapidly formulating treatment plans for Angle Class II malocclusion, belonging to the field of orthodontics. The method includes: acquiring cephalometric and model measurement data, and assigning a unique variable code to each data point; calculating the extraction gap values ​​corresponding to all maxillary and mandibular extraction combinations based on the acquired crown widths; establishing constraints on key parameters based on the data with assigned unique variable codes and the extraction gap values; calculating each maxillary and mandibular extraction combination individually, selecting Z candidate treatment plan combinations that satisfy the constraints, and outputting the corresponding parameter calculation results. This invention not only provides more precise treatment plans but also effectively reduces decision-making uncertainty in clinical practice, helping clinicians to quantitatively analyze and determine key treatment decisions such as whether extraction is necessary and anchorage selection, thereby assisting in clinical decision-making and improving the scientific rigor and accuracy of treatment planning.

The application aims to provide a HER2-positive breast cancer clinical auxiliary decision system based on multi-modal analysis, comprising: a data acquisition layer; a data processing and storage layer for cleaning, format standardization and feature extraction of the patient multi-modal data; an AI model analysis layer comprising a multi-modal deep learning model of a fusion convolutional neural network (CNN), a graph convolutional network (GCN), a multi-layer perceptron (MLP) and a Transformer text encoder, which outputs risk assessment of patient prognosis and treatment response according to multi-source features obtained from the data processing and storage layer; a decision support layer for generating individualized treatment path recommendations according to the risk assessment results generated by the AI model analysis layer, combining clinical guidelines and a rule engine, and presenting the recommended scheme and prediction basis to the clinician through a visual interactive interface; a feedback learning layer for application in the actual clinical execution process.

This invention discloses a metabolomics-based method for predicting the efficacy of non-invasive pretreatment intervention (NAIC) in gastric cancer and specific metabolites, belonging to the field of biodetection technology. The method includes: obtaining plasma samples from gastric cancer patients before treatment, detecting the abundance of their metabolites, and obtaining baseline plasma metabolomics data; classifying patients into a response group and a non-response group based on post-treatment imaging assessment results; comparing the baseline metabolomics data of the two groups to screen for differentially expressed metabolites; using these differentially expressed metabolites as features, training the model using LASSO regression, determining the optimal parameters through cross-validation, and constructing an efficacy prediction model composed of specific metabolites and their regression coefficients; for the sample to be predicted, detecting the abundance of its specific metabolites and inputting them into the model to obtain a risk score, and predicting the patient's response to treatment by comparing it with a risk threshold. This invention enables accurate efficacy prediction before treatment, providing an important basis for individualized clinical treatment decisions.

The invention belongs to the technical field of intelligent medical treatment, and discloses a dynamic risk assessment method and system for light-chain amyloidosis. A traditional staging system does not consider age factors and does not consider interaction between hematology and organ remission, so that obvious defects exist in the aspect of prognosis personalized risk prediction. According to the method, a 6-month risk scoring model and a 12-month risk scoring model are created for the first time, the age, hematological remission and heart and kidney organ remission depth of a patient are subjected to weighted integration through a multivariable Cox proportional risk model, and risk scores are generated and divided into corresponding risk grades. According to the method, the independent contribution and the interactive influence of each factor on the death risk are quantified, the method is closer to the complicated biological nature of the disease, continuous monitoring, dynamic evaluation and accurate prediction of prognosis are realized, the clinical use threshold is greatly reduced, the treatment decision can be directly linked, and an objective basis is provided for the treatment decision.

The invention relates to a myocardial injury assessment method and device, a sample analysis system and application thereof. According to the method, the device and the sample analysis system, individual myocardial injury pathogenesis and risk can be rapidly and accurately evaluated, and diagnosis decisions and treatment decisions can be guided.

The application discloses a non-small cell lung cancer anti-PD-1 individualized treatment decision support system and a method thereof. The system comprises a data acquisition and standardization module, which adopts standard integration of multi-source heterogeneous data; a dynamic knowledge graph construction module, which stores structured knowledge based on a Neo4j storage structure; an incremental learning module, which realizes real-time updating of a knowledge base; an intelligent decision support module based on a domestic large language model, which performs indication screening, efficacy and side effect prediction and conflict detection through technologies such as multi-modal fusion, hierarchical decision model, time series Transformer and graph neural network; an individualized treatment plan and clinical trial matching module, which optimizes a treatment path in combination with economic analysis and semantic matching algorithm; a patient education and interaction module, which provides personalized content generation, intelligent question answering and toxicity management; and a clinical verification and optimization module supporting lightweight adaptation of primary medical institutions. The application realizes intelligentization of the whole process from data integration to clinical decision, and improves the precision and accessibility of immunotherapy.

The invention discloses a mercuric chloride-based multi-modal medical large model and a medical image tumor segmentation modeling method. Comprising the following steps: integrating various medical image data and expert diagnosis reports, constructing a multi-modal data set, providing rich information for model training, comprehensively improving the recognition and analysis ability of tumor features, and further assisting doctors to make more accurate diagnosis and treatment decisions; through an edge computing technology, two business systems of mobile medical treatment and local medical treatment are constructed, and computing power is pushed from a cloud end or a data center to a network edge; aiming at the characteristics of a mercuric chloride server, carrying out all-parameter fine tuning on the multi-modal medical large model, carrying out fine tuning through a high-resolution image and supervised learning annotation data, and optimizing the model to adapt to a specific task; and a residual module and attention fusion are introduced, so that the complex feature extraction capability of the network is enhanced, and the gradient disappearance problem is effectively solved. The medical AI technology is effectively promoted to be widely applied to clinical scenes, and the efficiency and accuracy of medical diagnosis are practically improved.

The present application belongs to the technical field of intelligent medical treatment, and discloses a stomach cancer distal metastasis prediction method and system based on multiple examinations, which comprises the following steps: first, acquiring the imaging, endoscopy and blood examination data of the same patient; then, mapping each examination original feature to a pre-defined uniform semantic concept set, and converting it into a uniform dimension semantic embedding vector through a modal encoder adapted to the data type; subsequently, constructing a cross-modal semantic alignment module, narrowing the distance between vectors of the same semantics and different sources based on contrast learning; further, detecting semantic conflicts and dynamically allocating the credibility weight of each source, weighting and fusing the multi-source vectors of the same semantics according to the weight to obtain a fused feature vector; finally, inputting the downstream prediction model to output the metastasis prediction result. The present application solves the semantic conflict problem of multi-modal examination data, improves the prediction accuracy and stability, and provides a reliable basis for the clinical treatment decision of stomach cancer.

The application discloses a clinical path deviation risk management and control tracing method and device based on big data and AI, equipment and a medium, comprising: preprocessing multi-source heterogeneous clinical data; dividing a time sequence stage based on the preprocessed multi-source heterogeneous clinical data, and constructing a stage-specific risk feature library; constructing a time sequence causal forest model based on the stage-specific risk feature library, and combining a real-time risk cross-stage prediction mechanism to construct a double-level clinical path deviation risk early warning model; constructing a full-link causal tracing graph based on a structural causal model, and performing hierarchical positioning and tracing of the main cause of deviation; and constructing a personalized correction scheme generation model based on the results of the double-level clinical path deviation risk early warning model, the full-link causal tracing graph and the hierarchical positioning and tracing. The application belongs to the field of medical risk prediction. The application can ensure the continuity of the treatment decision scheme.

This invention provides a biomarker for collateral circulation in CTO patients, MIA3, and its application, belonging to the field of biotechnology. This invention reveals and verifies a significant positive correlation between plasma MIA3 levels and the quality of collateral circulation formation in CTO patients, and its predictive efficacy for good collateral circulation is superior to that of the known key angiogenesis factor VEGFA. This allows MIA3 to serve as a high-value, minimally invasive molecular marker for early, non-invasive identification of a patient's collateral circulation formation potential before interventional surgery, thereby enabling risk stratification and providing an objective basis for individualized treatment decisions (such as whether more aggressive intervention is needed). This invention clarifies that MIA3 is a key link in regulating collateral angiogenesis; by targeting and upregulating MIA3 activity, collateral angiogenesis and maturation in ischemic areas can be precisely promoted, providing a novel non-pharmacological, non-surgical treatment strategy for CTO patients.

The invention belongs to the technical field of medical rehabilitation equipment, and discloses a postoperative intelligent monitoring rehabilitation auxiliary instrument for the laryngeal pharynx and a control method. The method includes: acquiring pharyngeal physiological data of the pharyngeal of a patient; the method comprises the following steps: constructing a physical information neural network model, inputting a four-channel tensor formed by processing pharyngeal physiological data into the physical information neural network model for analysis, outputting a laryngeal closure degree and a pharyngeal wall tissue elastic modulus, and determining a patient pain level based on the laryngeal closure degree and the pharyngeal wall tissue elastic modulus; on the basis of analysis of mucous membrane dynamic displacement sequence data and pressure data, swallowing mode characteristics of the patient are determined, rehabilitation decision analysis is conducted in combination with the laryngeal closure degree, the pharyngeal wall tissue elasticity modulus and the pain level, and a rehabilitation intervention instruction is generated; the drug administration operation is executed based on the rehabilitation intervention instruction, the execution feedback record after the drug administration operation is completed is obtained, the rehabilitation intervention instruction is adjusted based on the execution feedback record, and the timeliness and effectiveness of a treatment decision are remarkably enhanced.

This invention discloses the application of BHLHE40 in the preparation of prognostic assessment kits and therapeutic drugs for retinoblastoma. Through a series of in vitro and in vivo experiments, this invention demonstrates that knocking down BHLHE40 expression significantly inhibits the proliferation of retinoblastoma cells, exhibiting a significant tumor-suppressive effect. Therefore, it is proposed that BHLHE40 inhibitors can be used in the preparation of therapeutic drugs for retinoblastoma, providing a new, effective, and safe treatment option for RB patients. Furthermore, BHLHE40 can serve as a drug target to guide subsequent drug development. In addition, this invention also utilizes clinical tumor tissue samples to demonstrate that high BHLHE40 expression is significantly positively correlated with high-risk pathological features such as optic nerve invasion, scleral invasion, and choroidal invasion, suggesting that BHLHE40 can serve as an effective biomarker for predicting high-risk pathological features, assisting physicians in treatment decisions.

The invention relates to the technical field of implant sensor monitoring, in particular to an orthopedic implant microenvironment intelligent monitoring and regulation system which comprises a biological signal sensing module, a microenvironment dynamic analysis module, an implant state evaluation module, a directional treatment decision module and an osseointegration regulation module. In the invention, the piezoelectric micro-strain sensor array is used for collecting the stress change data of the bone screw interface in real time and extracting the frequency characteristics to construct the stress distribution diagram, so that the early recognition capability of the bone resorption risk is remarkably enhanced, and the microenvironment deterioration trend is accurately inverted in combination with the stress diagram, the local pH value and the inflammatory factor concentration; the stability evaluation precision of the bone fusion interface is improved, the aging sign of an interface material is analyzed through a micro displacement vector, early warning grading of mechanical instability is achieved, targeted drug release coordinates are generated on the basis of superposition of an instability area and inflammation distribution, the drug release pertinence is improved, and the stent deformation and electrodeposition process is synchronously regulated and controlled. And the stress regulation and the bone regeneration process are dynamically coupled.