219 results about "Optimal treatment" patented technology

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency 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.

The invention belongs to the technical field of tunnel construction geological disaster prevention and control, and discloses a multi-source data fused refined treatment decision-making method for a complex stratum disaster source, which comprises the following steps: collecting and fusing multi-source geological data, and constructing a three-dimensional geological model; generating a disaster source risk dynamic assessment and treatment scheme; based on a fluid-solid coupling similarity theory, verifying the preliminary treatment scheme by adopting a physical model test, and determining an optimal treatment scheme; the optimal treatment scheme is executed, and the treatment process is dynamically regulated and controlled; after treatment, the treatment effect is evaluated through posterior data, and the effect data is fed back to the three-dimensional geologic model and the knowledge base, so that the dynamic updating of the model and the self-learning of the decision-making system are realized. By the adoption of the treatment decision method, the problems that a traditional method depends on experience, information is one-sided, and treatment is extensive are solved, advanced accurate forecasting and refined and personalized treatment of complex stratum disaster sources are achieved, and the safety and efficiency of tunnel construction are remarkably improved.

The invention relates to the technical field of clinical experiment data processing, and discloses a clinical experiment data analysis method based on reinforcement learning, and the method comprises the steps: obtaining time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical feature extraction on the time sequence physiological parameters, constructing a dynamic response feature model, and generating an initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and control data; constructing and training a reinforcement learning decision model, and generating a treatment path correction model and initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain optimal treatment path parameters; reasoning a curative effect standard reaching probability based on a real-time model, and adjusting a strategy to realize dynamic curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.

The invention discloses a self-adaptive aid decision-making method, system and device for patient radiotherapy, and a medium, and relates to the field of smart medical treatment. The method comprises the following steps: standardizing and preprocessing multi-source original data; performing automatic segmentation and change detection on tumor and normal tissues based on the processed data; delineating a tumor target region and a normal tissue in radiotherapy in a detection result; generating radiation therapy dose distribution according to the sketching result; evaluating the radiation treatment dose distribution by adopting an optimization evaluation method to generate a plurality of alternative treatment schemes; determining an optimal treatment regimen based on the plurality of alternative treatment regimens; and performing radiotherapy based on the optimal treatment scheme, monitoring anatomical changes of the patient in real time in the radiotherapy process, and dynamically adjusting the optimal treatment scheme. According to the invention, a plurality of operations of patient radiotherapy can be effectively integrated, and precision and individuation of patient radiotherapy can be realized, so that complex changes in the treatment process can be better coped with, and the overall treatment effect can be improved.

The invention belongs to the technical field of intelligent medical treatment, and discloses a chronic disease information management system based on a behavior interaction model. Comprising the following steps: acquiring background information, internal motivation and cognitive evaluation; based on the background information, a health education content library is constructed, and health education content is pushed in combination with cognitive evaluation; fusing the background information and the internal motivation to generate a multi-dimensional treatment scheme matrix; acquiring willingness information of a patient, performing quantitative evaluation on acceptance degrees of different treatment schemes, and screening out an optimal treatment scheme; acquiring and analyzing real-time interaction data, and dynamically formulating an emotion support strategy; patient health data are integrated, and the interaction effect is evaluated; according to the interaction effect, the health education content, the optimal treatment scheme and the emotion support strategy are intelligently optimized in sequence; behavior interaction is taken as the core, and accurate management of the whole life cycle of the chronic disease patient is realized, so that the health management effect of the patient is remarkably improved, and the complication risk is reduced.

The invention provides a self-adaptive clinical trial design method based on machine learning, relates to the technical field of clinical trials, and adopts a method integrating reinforcement learning, causal inference and data enhancement technologies to predict an optimal treatment scheme and adjust a distribution proportion in real time through a dynamic optimization module, and limits high-risk treatment selection in combination with dynamic ethical constraints. The problems that a traditional clinical test preset rule lacks real-time performance and neglects patient safety are solved, a data collection and enhancement module generates virtual patient data conforming to a causal relationship, real and virtual data are combined to form a unified data set, a high-quality data set is constructed, the data size is effectively expanded, the statistical power of a small sample scene is improved, and the accuracy of a clinical test result is improved. The termination rule module of comprehensive statistics, ethics and resources shortens the test period through weighted evaluation, significantly reduces the risk of improper allocation of high-risk groups, enhances the benefit of patients, ethics compliance and test result reliability, and expands the application range in rare disease research and personalized medical treatment.

The invention relates to a patient diagnosis and treatment method based on a vector database and a medium, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting patient medical data, vectorizing the medical data comprising digital information, text information and image information, carrying out the feature fusion of the vectorized data, and obtaining a first fusion vector; calculating the similarity between the first fusion vector and a second fusion vector corresponding to each historical patient to obtain a preset number of second fusion vectors with the highest similarity; and obtaining an optimal treatment scheme based on a preset number of second fusion vectors with the highest similarity. Compared with the prior art, dependence on the diagnosis and treatment level of doctors can be reduced with the help of previous excellent diagnosis and treatment cases, a better treatment scheme can be quickly obtained, and the diagnosis and treatment level of existing doctors can be quickly improved.

The invention discloses a transcranial magnetic stimulation target region recommendation method based on craniocerebral position estimation. The method comprises the following steps: constructing a face key point cloud based on binocular stereo vision; reconstructing the face key point cloud into a cranial surface model by using a pre-trained generative model; the method comprises the following steps: collecting magnetic resonance imaging data of patients with various diseases, establishing a transcranial magnetic stimulation target region standardized template with disease specificity, and determining a stimulation target region of each disease in a standard space; and based on the transcranial magnetic stimulation target region standardization template, mapping the corresponding stimulation target region from the standard space brain region to the scalp of the patient according to the disease type of the patient so as to realize recommendation. According to the method, the graph neural network is innovatively combined with the SHAP algorithm, the scientificity and interpretability of target spot selection are improved, important brain region target spots are obtained by calculating the contribution value of classification model features to classification decision of each sample, and then intervention target spots are sorted and selected to obtain an optimal treatment target spot recommendation scheme.

The invention discloses an informatization process processing method for a thoracic pain stroke trauma treatment mode, and the method comprises the following steps: 1, information collection and preprocessing: collecting the physiological sign data of a patient through a mobile terminal, and analyzing the physiological sign data based on a deep learning model, so as to obtain a preliminary diagnosis result of an illness state; classifying and marking the patient according to the preliminary diagnosis result of the illness state to obtain a classification and marking result, and uploading the classification and marking result, the physiological sign data and the geographic position information of the patient to a regional medical collaborative server; step 2, multi-center cooperative distribution: after receiving the classification marking result, the physiological sign data and the geographic position information of the patient, a regional medical cooperative server performs calculation processing based on a preset medical resource scheduling model; and step 3, performing a treatment process: sending the optimal treatment path scheme to a medical information system of a target medical center by the regional medical cooperation server, triggering the target medical center to start an emergency plan, and deploying medical resources.

Systems and methods for determining a treatment time for treatment zone of a target tissue are disclosed herein. In some embodiments, a system may include a cryoballoon, a mapping array, a catheter, and one or more hardware processors. The hardware processors may generate a pacing signal at the electrodes of the mapping array at the treatment zone and determine a minimum activation energy of a non-target tissue. The processors may activate the cryoballoon at a first time and detect an artifact in the electrical signals collected from the electrodes of the mapping array at a second time. Based on the minimum activation energy of the non-target tissue and the difference between the second time and the first time, the processors may determine an optimal treatment time for the treatment zone of the target tissue.

The invention discloses a metabolite screening method for promoting expression of porcine intestinal defensin pBD1 and application of the metabolite screening method. The screening method comprises the following steps: 1) constructing a porcine defensin pBD1 gene fluorescence report intestinal epithelial cell line; 2) evaluating the promotion effects of different metabolites based on the pBD1 gene fluorescence report intestinal epithelial cell line; and 3) further evaluating the promotion effects of the metabolites screened in the step 2) based on a porcine intestinal epithelial cell IPEC-J2 model. And 4) further evaluating the promotion effect of the metabolite screened in the step 3) based on the pig intestinal organ. The invention can be used for preparing or screening drugs influencing the expression of the porcine intestinal defensin pBD1. According to the application disclosed by the invention, the effect of the D-malic acid for improving the immune function of the pig intestinal tract by promoting the expression of the defensin pBD1 is found for the first time, verification is carried out on a pig intestinal tract organ-like model, and meanwhile, the optimal treatment concentration of the D-malic acid for promoting the expression of the defensin pBD1 in the pig intestinal tract is optimized.

The invention discloses a traditional Chinese medicine diagnosis and treatment aided decision-making method and system, and relates to the technical field of traditional Chinese medicine intelligent diagnosis and treatment aided decision-making, and the method comprises the steps: carrying out the wavelet transformation analysis of pulse condition waveform time series data, extracting a pulse condition dynamic change feature sequence, inputting a tongue image color change rate and a pulse condition feature change rate into a space-time convolution network, and obtaining a time-space convolution network; generating a tongue pulse dynamic association index and outputting a preliminary syndrome classification; fourier transform analysis is carried out on the spleen channel acupoint impedance spectrum, false-true state judgment is carried out in combination with tongue pulse dynamic correlation indexes, preliminary syndrome classification is corrected, and a false-true state quantification result and corrected syndrome classification are output; and matching the corrected syndrome classification with the traditional Chinese medicine knowledge map, adjusting the dosage according to a virtual-real state quantification result, reversely verifying the drug-syndrome specificity through a curative effect database, screening an optimal treatment scheme, and outputting a personalized diagnosis and treatment scheme. According to the method, the syndrome classification precision is improved, and an objective basis is provided for personalized dose adjustment.

The invention relates to the field of oral clinical orthodontics, and discloses a method for generating an oral correction scheme, comprising: acquiring a face composite model of an initial state of a patient, the face composite model comprising a dental jaw portion and a soft tissue portion; respectively obtaining P correction schemes of the patient, wherein each correction scheme corresponds to a target state of tooth jaw movement; correspondingly obtaining face prediction models of P target states according to the tooth jaw movement and deformation face composite models of the P correction schemes; and in the P correction schemes, selecting an optimal correction scheme as a target correction scheme according to evaluation of the face prediction model in the target state. The invention further discloses electronic equipment and a storage medium, the orthodontic scheme is determined by predicting the appearance, the predicted result is closer to the real effect after orthodontic treatment, more bases are provided for the patient to select the orthodontic scheme, the patient can conveniently determine the ideal orthodontic scheme from the face expression, and a more preferable orthodontic scheme is generated.

The invention belongs to the technical field of medical data processing, and relates to a urinary surgery diagnosis and treatment data intelligent processing method and system, and the system comprises a multi-modal data collection module which is used for collecting basic diagnosis and treatment data, behavior data and diagnosis and treatment process data of a patient; the diagnosis and treatment data intelligent analysis module is used for calculating the intention value of the patient for the diagnosis and treatment item and generating a diagnosis and treatment path diagram; the intelligent decision support module is used for generating personalized diagnosis and treatment suggestions in combination with a guide and a model; the doctor-patient interaction feedback module is used for collecting patient feedback and updating an analysis weight; and the follow-up management module is used for generating a follow-up plan and relapse early warning. Basic diagnosis and treatment data and patient behavior data are fused, and diagnosis and treatment requirements are reflected more comprehensively; the dimension weight is adjusted and analyzed in real time through patient feedback, and the personalized diagnosis and treatment precision is improved; a guide rule and a machine learning model are combined to assist a doctor to quickly generate an optimal treatment scheme; through the block chain and privacy protection technology, compliance use of the medical data is ensured.

The invention relates to the technical field of medical artificial intelligence, and discloses a non-small cell lung cancer immunotherapy risk assessment method and system and a storage medium, and the method comprises the steps: constructing an optimal strategy tree model capable of dividing a patient into a plurality of subgroups based on a historical patient data set, and determining an initial optimal treatment strategy for each subgroup; determining a subgroup to which a new patient belongs and an initial treatment strategy thereof according to the model; in the treatment process, the deviation degree between the individual response trajectory of the patient and the average response trajectory of the subgroup to which the individual response trajectory belongs is calculated; and when the deviation degree exceeds a preset threshold value of the subgroup, triggering an adaptive adjustment mechanism, and outputting an updated treatment suggestion. By establishing a dynamic monitoring and closed-loop feedback mechanism based on the subgroup benchmark, the technical problem that an existing scheme is difficult to carry out prospective identification and dynamic intervention on individualized risks is solved, so that the accuracy, safety and individualized level of non-small cell lung cancer immunotherapy are improved.

The present disclosure provides apparatus and methods to both infer stent expansion (vessel expansion or compliance) and probability of procedural success (vessel patency) from pretreatment diagnostic imaging at the point-of-care and also train an inference model to generate such an inference, thus allowing a physician to choose with greater certainty an optimal treatment tool and treatment protocol for treating a vessel of a patient.

The invention discloses a transcutaneous electrical stimulation carotid sinus blood pressure lowering device which adjusts electrical stimulation parameters through the following steps that a processor recognizes that the device is taken off, and a current optimal treatment scheme is stored as a historical treatment scheme; the processor refers to treatment scheme selection suggestions of historical treatment schemes, if the treatment scheme selection suggestions are enhanced, the processor compares the user bearing time Tx with an abnormal exit judgment threshold value T, if Txgt is greater than Txgt, if Txgt is greater than Txgt; if Tx is smaller than T, the processor judges that the user normally exits electrical stimulation treatment, if Tx is smaller than or equal to T, the processor judges that the user cannot accept the enhanced current treatment scheme, and a new maximum acceptable threshold value in the current blood pressure state is reset. Whether the user can accept and enhance the current treatment scheme or not is judged according to the user bearing time after the treatment scheme is changed, so that the maximum acceptable threshold value of the treatment scheme is adjusted, the probability of pain caused by electrical stimulation is reduced, the treatment time is prolonged, and the treatment effect is improved.

The invention discloses a method for identifying somatic depression by using brain structure and function abnormality, which comprises the following specific steps: selecting depression patients into groups, dividing the depression patients into a somatic depression patient group and a non-somatic depression patient group according to whether the score of a health questionnaire somatic symptom group scale of the patients is greater than or equal to 10, and recruiting a health control group at the same time. The three groups of subjects are subjected to resting state structure and functional magnetic resonance imaging scanning, and the brain grey matter volume and the brain region with significant difference are analyzed through a voxel-based morphological index and functional connection method. Pearson correlation analysis is adopted in SPSS26.0, and the relevance between the gray matter volume and the abnormal function connection of the patients in the SD group and the NSD group and the PHQ-15 scale score is compared. The method has the advantages that the illness state of the depression patient can be found in time, the patient can be treated at the best treatment opportunity, and early recognition of the body symptoms of the depression is beneficial for reducing waste of medical resources and relieving the pain of the patient.

The invention relates to the technical field of ovarian granule cell apoptosis, and discloses a method for regulating oxidative stress induced sheep ovarian granule cell apoptosis by using allicin, and the method comprises the following steps: firstly preparing a culture solution, then preparing an allicin stock solution, and identifying sheep ovarian granule cells; the method comprises the following steps: firstly, measuring the activity of ovarian granular cells after independent treatment of H2O2 and allicin, determining the optimal treatment time and concentration of H2O2 and allicin, then carrying out co-treatment of H2O2 and allicin, detecting the apoptosis of the granular cells by using flow cytometry, detecting the expression of apoptosis-related genes by using real-time fluorescent quantitative PCR, detecting the secretion of steroid hormones by using ELISA, and carrying out data analysis. The invention solves the problems that in the prior art, an effective regulation and control method for ovarian granular cell apoptosis induced by oxidative stress is lacked, and traditional medicines possibly have large side effects, unobvious effects and the like.

The invention discloses a cardiovascular nursing medical resource allocation system, and relates to the technical field of medical information, and the system comprises a multi-sensor data collection network which is used for collecting various signal data related to cardiovascular health from a patient and an environment; and the data integration and preprocessing module is used for performing format unification, noise suppression and feature extraction on the original data acquired by the multiple sensors. According to the cardiovascular nursing medical resource allocation system, real-time monitoring and accurate early warning of the cardiovascular state are realized through accurate data acquisition, data preprocessing and mixed deep learning risk assessment technologies; meanwhile, hospital resources are dynamically dispatched through multi-objective optimization and a genetic algorithm, and it is ensured that optimal treatment resources can be rapidly allocated when acute events occur.

The invention provides a special disease first-aid full-process auxiliary decision-making method based on multi-modal perception and AI, and relates to the technical field of medical first aid, and the method comprises the steps: obtaining first-aid field multi-modal data, constructing a feature dependence graph, and recognizing the type and symptom features of a special disease; indexing special disease diagnosis and treatment data based on the symptom feature vector, and determining a reference case and a treatment path; the action space is constructed in combination with clinical specifications, the expected income is predicted by applying Monte Carlo search, and the optimal treatment scheme is generated, so that the first-aid special disease recognition accuracy can be improved, the treatment decision time can be shortened, and the first-aid intervention effect can be improved.

The invention relates to an immunoglobulin A nephropathy risk prediction method and system based on ultrasound, and the method comprises the steps: obtaining a kidney longest axis ultrasound image of a sample patient, determining a kidney tissue region in the kidney longest axis ultrasound image as a target region, and generating a boundary contour; a target area image in the kidney longest axis ultrasonic image is preprocessed, and image omics feature extraction processing is carried out; dimensionality reduction and screening are carried out on the extracted radiomics features to obtain target radiomics features, radiomics scores are calculated, and the radiomics scores are combined with preset independent risk variables to obtain combined features; constructing a prediction model and performing training; and inputting the combined features corresponding to the target patient into the trained prediction model to obtain a corresponding risk type prediction result. The method realizes accurate prediction of immunoglobulin A nephropathy, can be used in clinical scenes where renal biopsy cannot be performed or provides information guidance for replacement of renal biopsy, and avoids delay of optimal treatment opportunity.

The invention discloses a combined treatment medicine for acute myelogenous leukemia. The combined treatment medicine is prepared from vinaclaria, azacitidine and aclarithromycin. The medicine is applied to preparation of an acute myelogenous leukemia cell proliferation inhibitor; the medicine is applied to preparation of an acute myelogenous leukemia cell apoptosis inducer. The three medicines are combined to achieve a synergistic effect: the three medicines have a synergistic anti-tumor effect instead of a simple superposition effect, and a new combined medication mechanism is a result obtained through laboratory research and an initial effect of clinical application. The subsequent consolidation treatment scheme may also be used for part of initially treated patients with acute myelogenous leukemia, which are not suitable for enhanced chemotherapy or intense intense treatment by using targeted drugs, and relapse / refractory patients with acute myelogenous leukemia, and a better treatment scheme can also bring new hope and dawn to more patients.

The invention discloses a real-time feedback and self-adaptive phototherapy parameter adjustment method based on AI, and the method comprises the steps: S1, collecting multi-source data of a patient in a phototherapy process, and forming a multi-source biological feedback data set; s2, constructing an AI algorithm model, inputting the multi-source biological feedback data set into the AI algorithm model, and outputting the change trend of the physiological state of the human body; s3, setting a phototherapy effect evaluation index system, comparing the change trend of the physiological state of the human body with a preset phototherapy effect evaluation index, and calculating a deviation value between a current phototherapy parameter and a parameter required for realizing an optimal treatment effect; s4, dynamically adjusting the phototherapy parameters through a reinforcement learning algorithm according to the deviation value, and determining a parameter combination of phototherapy in the next stage; and S5, the optimized phototherapy parameters are sent to phototherapy equipment for adaptive dynamic adjustment. The problems that phototherapy parameters cannot adapt to individual differences and physiological state changes of patients, so that the treatment effect is poor, and safety and comfort are insufficient are solved.

The invention discloses a hyperbaric oxygen chamber management method and system based on multi-dimensional data, and belongs to the technical field of medical health management, and the method comprises the steps: collecting the multi-dimensional data of a hyperbaric oxygen chamber and a patient in real time, providing atmosphere adjustment for selection, and relieving the inadaptability of the patient; analyzing according to the multi-dimensional data, identifying key influence factors of treatment requirements and treatment effects of the patient, and recommending an optimal treatment scheme; performing layered management on the hyperbaric oxygen chamber; setting a deployment period, and analyzing treatment requirements of patients and utilization rates of the hyperbaric oxygen chambers to perform dynamic resource deployment of the hyperbaric oxygen chambers of different levels. According to the system, the inadaptability of a patient is effectively relieved, the treatment compliance and experience are improved, the treatment interruption is reduced, the medical service quality and satisfaction are improved through hierarchical management and resource allocation of the hyperbaric oxygen chamber, the resource allocation is optimized, the waiting time is shortened, and meanwhile, the resource utilization maximization is realized.

The invention belongs to the technical field of air pressure treatment, and discloses a self-adaptive multi-point monitoring intelligent pulse anti-thrombus air pressure treatment system. Comprising the following steps: continuously collecting physiological parameters of a plurality of monitoring points of a patient, and constructing a parameter sequence; performing dynamic trend analysis on each parameter sequence in sequence, and extracting a corresponding physiological feature set; sequentially analyzing each group of physiological feature set, constructing a physiological feature sequence, comprehensively evaluating the risk score of each monitoring point, and dynamically evaluating the thrombus risk index of each monitoring point based on the risk score; fusing the thrombus risk index and the physiological feature sequence of each monitoring point, intelligently formulating an optimal treatment strategy, and carrying out differential parameter configuration on pulse air pressure treatment equipment; precise evaluation and personalized management of the physiological state of the patient in the air pressure treatment process are achieved, the treatment accuracy and effectiveness are improved, and the comfort and compliance of the patient are enhanced.

The invention provides a phototherapy device and a using method of the phototherapy device, and belongs to the technical field of medical instruments, and the device comprises an electroencephalogram collection module which is used for collecting an electroencephalogram signal of a target person and converting the electroencephalogram signal into a digital signal; the phototherapy module is used for outputting light with target power to the skull of the target person after starting to work; the temperature detection module is used for detecting the scalp temperature of the target person; and the data analysis and control module is used for controlling the phototherapy module to start working under the condition of determining that the target person enters a non-rapid eye movement sleep window period based on the digital signal, and controlling the phototherapy module to stop working under the condition of determining that the scalp temperature exceeds a preset temperature threshold. The optimal treatment window of the target person can be determined by identifying the electroencephalogram signals, and the technical problem that an existing phototherapy device is poor in use effect is solved.

The invention discloses an orthodontic auxiliary decision-making method based on fuzzy comprehensive evaluation and an operation system thereof, which are used for providing a personalized orthodontic treatment scheme, and specifically comprise the following steps: firstly, comprehensively analyzing key factors influencing an orthodontic treatment effect, screening out representative evaluation indexes, and determining the evaluation indexes; an evaluation system of orthodontic treatment decisions is constructed; then, respectively calculating a subjective weight and an objective weight by adopting an analytic hierarchy process and an entropy weight method, and optimizing weight distribution in combination with a game theory to obtain a comprehensive weight of each index; and finally, based on a fuzzy comprehensive evaluation method, constructing a trapezoidal membership function, establishing a fuzzy relation matrix, and determining an optimal treatment scheme through a maximum membership principle. Scientific and accurate auxiliary decision support can be provided for orthodontic doctors, the individuation level of a treatment scheme is effectively improved, and the developed operation system application can improve the diagnosis efficiency of young doctors.