5721results about How to "Good treatment effect" patented technology

The invention discloses an intelligent nursing management system and method for a critical patient, and relates to the technical field of medical health monitoring, and the method comprises the steps: setting a multi-physiological signal collection channel, synchronously collecting multi-dimensional physiological signals of the patient, carrying out the preprocessing of the collected signals, and forming a standardized multi-modal physiological data flow; the preprocessed multi-modal physiological data are input into a deep learning model based on LSTM and a multi-head attention mechanism, intelligent fusion analysis of different physiological signals is achieved in combination with a dynamic weight adaptive algorithm, and a patient individualized physiological system coupling model is established; and based on an analysis result of the deep learning model, calculating an illness state risk assessment value of the patient in real time, and outputting a specific early warning level according to a set multi-level early warning threshold. According to the invention, the multi-stage and multi-dimensional early warning threshold system is constructed, so that the condition of the patient can be monitored and early warned in all directions. The monitoring quality of critically ill patients can be remarkably improved, and the missed judgment rate and the misjudgment rate are reduced.

The invention relates to the technical field of skin disease diagnosis, and discloses an AI auxiliary decision support system for skin disease diagnosis, which integrates an image acquisition module, an image analysis module and a comprehensive analysis and diagnosis decision module. A high-definition image of the skin of a patient is obtained through high-precision skin photographing equipment, and a training data set is formed by combining basic information of the patient. A deep learning algorithm is utilized to train a skin disease recognition model and an illness state evaluation model, and accurate recognition of skin disease types and objective evaluation of severity are realized. And the comprehensive analysis module fuses output of the two models, constructs a fusion decision model and provides comprehensive diagnosis suggestions. And the diagnosis decision module is used for generating a disease type judgment and illness state evaluation report based on the trained model in combination with real-time image data so as to effectively assist doctors in making decisions. The accuracy, the efficiency and the popularization degree of skin disease diagnosis are improved, and complex and changeable diagnosis requirements in clinical practice are met.

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention discloses a critical patient nursing intelligent monitoring system and method, and relates to the technical field of intelligent monitoring. Collecting physiological data and activity data of a patient in real time by using a multi-sensor system, and establishing an initial health file of the patient; according to the collected physiological data and activity data, daily activities and behavior modes of the patient are analyzed, and an individualized health baseline is established; monitoring physiological data and activity data of the patient in real time, identifying an abnormal trend, and generating an early warning signal; preferentially distributing nursing measures according to the state of the patient; the nursing measure effect is evaluated, and if the expected nursing effect is not achieved, the monitoring strategy is readjusted; according to the invention, the health state of the patient is comprehensively monitored and accurately analyzed in real time, so that abnormity can be found and intervened in time when the abnormity occurs, the patient can be nursed and treated in time, waste of nursing resources and nursing errors are reduced, and the treatment effect and the nursing quality of the critical patient are improved.

The invention relates to the technical field of skin disease health evaluation, in particular to a skin disease health evaluation system integrating multiple clinical evaluation tools. Comprising a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module. The method comprises the steps of performing dynamic risk assessment in combination with a machine learning algorithm, performing quantitative assessment on the skin health state of a patient in combination with microbiome analysis and real-time physiological index monitoring, performing quantitative assessment on symptoms through an image analysis technology and a standardized questionnaire, and generating a personalized health assessment report and a treatment scheme by using a deep neural network and an optimization algorithm. Continuously improved closed-loop management is realized through a feedback optimization module; according to the invention, not only is a scientific evaluation basis provided for doctors, but also a patient can obtain customized treatment suggestions.

The invention relates to the technical field of medical care, in particular to an intelligent query method for a nursing knowledge base, which comprises the following steps of: performing multi-form standardized processing on a nursing problem input by a user, deeply analyzing a query request by utilizing a semantic understanding model, and obtaining an intelligent query result based on an inference algorithm; the method comprises the following steps: automatically selecting related literatures, clinical data and nursing experience data from a nursing knowledge base, generating a preliminary nursing scheme candidate set in combination with a nursing professional knowledge graph, and performing personalized screening and optimization on the scheme by using an intelligent recommendation model in combination with the current illness state, treatment progress and historical nursing data of a patient; the intelligent recommendation model is self-optimized through user feedback, and the nursing scheme recommendation precision is continuously improved. The nursing knowledge base automatically fuses newest medical research results and clinical nursing practice data. The problems that in the prior art, nursing scheme recommendation accuracy is low, updating is not timely, and the individuation degree is insufficient are effectively solved.

The invention discloses a cardiothoracic surgery postoperative complication assessment method and system based on data analysis, and relates to the technical field of medical treatment, and the main scheme is as follows: collecting basic operation data, preoperative examination data, basic information data of a patient and real-time postoperative recovery data of the patient, calculating the complexity of the operation according to the basic operation data, and determining the postoperative complication of the patient according to the calculated complexity; the method comprises the steps of calculating preoperative complication indexes through preoperative examination data, calculating basic information indexes through patient information data, calculating vital sign comprehensive indexes through real-time postoperative recovery data, jointly calculating complication risk scores through the indexes, comparing the complication risk scores with a complication risk score threshold set, and then judging risk levels. And formulating a management strategy and generating an evaluation report. According to the system, the accuracy and systematicness of postoperative complication evaluation are improved, and powerful support is provided for clinical management.

The invention relates to a digital acupuncture dynamic control optimization method. According to the technical scheme, the method comprises the steps that real-time physiological data, including the heart rate, the oxyhemoglobin saturation, the skin temperature and the muscle tension, of a patient are collected through a wearable device and a sensor; performing cleaning, denoising and feature extraction on the multi-modal physiological data by using a data preprocessing algorithm; a personalized digital acupuncture model is established, and the physical condition and acupoint sensitivity of a patient are dynamically evaluated; adjusting acupuncture output parameters, control frequency, pulse width and current intensity according to real-time feedback of a patient by adopting a self-adaptive fuzzy control algorithm through the obtained personalized digital acupuncture model; multi-channel biofeedback equipment including EEG, EMG and ECG is integrated, and instant response of a patient is monitored; through a closed-loop control algorithm, biological feedback data are input into the control model in real time, and dynamic adjustment of acupuncture and moxibustion parameters is controlled; a deep reinforcement learning algorithm is introduced into acupuncture therapy.

The invention discloses a vagus nerve stimulation parameter closed-loop adjustment method and system based on electroencephalogram characteristics. The method comprises the steps that real-time electroencephalogram signals of a patient are obtained, and time domain information and frequency domain information of the electroencephalogram signals of all frequency bands in the real-time electroencephalogram signals are extracted; on the basis of the time domain information and the frequency domain information of the electroencephalogram signals of all the frequency bands, electroencephalogram signal index features are calculated, and the electroencephalogram signal index features comprise an electroencephalogram signal power spectrum, a brain dynamic symmetry feature, a brain function connection feature and a rhythm feature; determining the state type of the patient according to the electroencephalogram signal index characteristics; determining parameter configuration information of vagus nerve stimulation according to the state type of the patient; and adjusting parameters of the vagus nerve electrical stimulation equipment according to the parameter configuration information of vagus nerve stimulation. According to the invention, timely and accurate electrical stimulation can be applied to a patient, the treatment accuracy is improved, the stimulation efficiency and the treatment effect are enhanced, side effects are reduced, and more accurate and individualized treatment management is realized.

The invention discloses a multi-parameter monitoring data analysis method, and particularly relates to the technical field of medical monitoring, and the method comprises the following steps: obtaining a plurality of preset physiological parameters, carrying out the classification analysis, and dividing the parameters into a correlation physiological parameter set and an independence physiological parameter set; carrying out joint credibility analysis on the relevance physiological parameter set, identifying reliability hidden dangers, and obtaining a consistency quality index through overall trend consistency analysis; independent credibility analysis is carried out on the independent physiological parameters, and an accurate quality index is obtained through accuracy analysis; based on the quality indexes, the physiological parameter groups are divided into high-quality data and low-quality data, and the low-quality data are recollected to improve the data quality; through the universality and flexibility of the method, the method can adapt to different medical scenes, stable monitoring data quality guarantee is provided, clinical risks caused by data quality problems are effectively reduced, and monitoring data processing is more scientific and efficient.

The invention provides an electrocardiogram data anomaly recognition method, device and equipment based on deep learning and a storage medium. An original electrocardiogram signal of a patient is collected, and a target electrocardiogram signal is obtained through multi-stage preprocessing. And identifying an abnormal signal in the target electrocardiogram signal by using a deep fusion learning model, and generating a preliminary identification result. And then, the preliminary recognition result is combined with a pre-trained machine learning model, mixed model processing is performed through a stack generalization fusion method, a Bayesian network is constructed based on the detailed information of the patient, a reasoning result with context sensing is obtained, and a target recognition result is finally generated. And generating an abnormal report according to the target identification result, and storing and displaying the abnormal report to the user in a classified manner according to a predefined standard. According to the method, high precision and personalized diagnosis of anomaly detection are ensured, and the reliability and efficiency of electrocardiogram analysis are improved.

The invention relates to the technical field of medical information, in particular to a hospital pharmacy dispensing information intelligent processing method and system, and the method comprises the following steps: S1, obtaining the basic data of a medicine in real time; s2, acquiring medical record information, current medication conditions and physiological parameters of the patient from a hospital information system; s3, analyzing an existing drug combination, and detecting potential drug incompatibility; s4, a medication scheme of the patient is automatically generated; s5, pushing the medication scheme and the taboo information generated in the step S4 to work terminals of doctors and pharmacists, and providing medication suggestions at the same time; and S6, continuously monitoring the reaction of the patient, and when abnormity is monitored, performing drug adjustment through an adjustment algorithm. According to the system and the method, the medicine data is updated in real time, the patient information is integrated, and incompatibility identification and medication adjustment are carried out by utilizing an intelligent algorithm, so that the safety and the efficiency of hospital pharmaceutical dispensing are remarkably improved, and meanwhile, the individuation and the scientificity of a treatment scheme are optimized.

The invention relates to the technical field of medical information, in particular to an esophageal cancer multi-mode treatment effect comprehensive evaluation system which comprises a patient data acquisition module, a drug resistance dynamic monitoring and adjusting module, an immune system response dynamic analysis module and a treatment effect evaluation module. The patient data acquisition module is used for acquiring multi-modal health data of a patient; the drug resistance dynamic monitoring and adjusting module analyzes drug resistance signs appearing in the treatment process; the immune system response dynamic analysis module comprehensively evaluates the state change of the immune system; and the treatment effect evaluation module evaluates the treatment effect of the patient in real time based on the drug resistance and immune system response analysis result, and provides treatment adjustment suggestions for the doctor according to the evaluation result. According to the invention, the accuracy of treatment is improved, the risk of treatment failure is reduced, and decision support based on data is provided for doctors.

The preparation method comprises the following steps: mixing a tannic acid solution and a tetravalent cerium salt solution, adjusting the pH value to 9-11, and reacting to obtain the cerium tannate nano-enzyme, the molar ratio of the tannic acid to the tetravalent cerium salt is (1-4): 1; the hydration dynamic diameter of the cerium tannate nano-enzyme is 20 to 80 nm. The TA-Ce nano-enzyme prepared by the preparation method disclosed by the invention is smaller in particle size, can keep stable and good dispersibility in a physiological environment, and has good biocompatibility, low hemolysis rate and small systemic toxicity; meanwhile, the compound has good kidney targeting property, can be effectively enriched into the injured kidney, eliminates various ROS (reactive oxygen species) and relieves oxidative stress by utilizing unique redox cycle between Ce < 3 + > and Ce < 4 + > states, and has a good effect in treating acute kidney injury induced by rhabdomyolysis and / or cis-platinum. The research lays a foundation for the metal polyphenol nano material in the treatment of acute kidney injury.

The invention discloses a whole course rehabilitation management system and management method based on an AI technology, and relates to the technical field of medical health management. The system mainly comprises a medical record file module, a rehabilitation management module and a doctor-patient communication module. Personal medical records of a patient are stored and managed through the structured information storage technology, free interaction between the system and a doctor-patient terminal is achieved through the doctor-patient communication module, a channel for timely consultation and feedback is provided for the patient, and doctor-patient communication is promoted. Rehabilitation schemes of different disease course stages are provided for each patient through the rehabilitation management module, a rehabilitation file of the next stage is adjusted according to execution data of the patient in each stage, and the whole disease course of the patient is throughout until complete rehabilitation is achieved. Personalized whole course rehabilitation management for different patients is realized, and the overall efficiency and the service quality of medical services are improved.

The invention relates to the field of traditional Chinese medicine effective component new dosage form pharmaceutical preparations, and discloses an M2 type macrophage membrane coated ROS responsive bionic nano drug delivery system, preparation and application. Mesoporous silica is used as a core to load a traditional Chinese medicine effective component, namely tripterine, and lipid components, namely PEGylated distearoyl phosphatidyl ethanolamine (DSPE-PEG), dioleoyl lecithin (DOPC), cholesterol and the like, are used as raw materials to prepare a ROS-responsive lipid coating, so that ROS-responsive tripterine mesoporous silica is formed. And then realizing bionic fusion with the M2 type macrophage membrane on the outer layer through an ultrasonic coating technology to prepare the M2 type macrophage membrane coated ROS responsive bionic mesoporous silica nano drug delivery system. The invention can realize targeted therapy of rheumatoid arthritis, and has the advantages of biological safety and the like.

The invention relates to a CBCT image tooth segmentation method. The method comprises the following steps: generating a two-dimensional oral cavity panoramic film from a CBCT image; carrying out tooth two-dimensional segmentation and tooth ID classification on the two-dimensional oral cavity panoramic film by adopting a deep learning algorithm; calculating a key point coordinate of a two-dimensional single tooth and mapping the key point coordinate to a three-dimensional CBCT image coordinate; respectively cutting the CBCT image to obtain an area of each tooth; segmenting each three-dimensional tooth region by adopting a deep learning algorithm; and assigning ID to all the tooth segmentation results, and splicing to obtain a three-dimensional tooth segmentation result. According to the method, accurate segmentation of the tooth area in the CBCT image can be realized, complex conditions such as tight tooth arrangement, similar shapes and artifact interference can be effectively handled, the problems of tooth segmentation result fusion, ID confusion and metal artifact interference can be effectively solved, and resource consumption is remarkably reduced by determining the tooth key points on the panoramic film, so that the tooth segmentation efficiency is improved. The overall segmentation efficiency is improved, and the segmentation time is shortened, so that the accuracy of the segmentation result and the processing speed are improved.

The invention discloses a micro-pressure oxygen cabin control system and a control method thereof, and relates to the technical field of medical treatment, and the system comprises an oxygen cabin operation monitoring module which is used for monitoring and obtaining information data of a micro-pressure oxygen cabin in real time during working, comprise the running state of the micro-pressure oxygen cabin, the air pressure value in the oxygen cabin, the oxygen concentration in the oxygen cabin and the vital sign state of a user in the oxygen cabin; and a data processing and analyzing module. According to the micro-pressure oxygen cabin control system and the control method thereof, a data processing and analysis module, especially a coupling relation analysis unit, is introduced, an oxygen and pressure dynamic change model is constructed by utilizing an algorithm, the safety of equipment and a user is protected, and the safety of the equipment and the user is improved through an intelligent and automatic control means. The problems of an existing micro-pressure oxygen cabin are effectively solved, the treatment effect and the use safety are improved, and the more advanced and reliable micro-pressure oxygen cabin control system and the control method thereof are provided for the medical field.

The invention discloses a tongue picture characteristic change rule analysis method based on the rehabilitation process of a chronic disease patient, and relates to the technical field of health big data analysis, and the method comprises the steps of tongue picture data standardized collection, tongue picture data recording system establishment, tongue picture characteristic data analysis, result visualization and report generation. According to the invention, through a standardized tongue picture data acquisition mechanism, the quality and consistency of the tongue picture data are ensured, and through controlling illumination conditions, background colors, diet restrictions and posture requirements, the influence of external factors on the tongue picture data is reduced, and the accuracy and reliability of the data are improved. By comprehensively evaluating definition, noisy points, contrast ratio and color accuracy verification parameters, it is ensured that collected tongue picture data reaches a preset quality standard, and a high-quality data basis is provided for subsequent analysis; meanwhile, a comprehensive tongue picture data recording system is established, and tongue picture data are effectively stored and managed.

The invention provides a chronic disease co-patient accurate medication real-time management system based on an AI large model, and the system comprises accurate medication guided by the AI large model, through the mathematical modeling and simulation technology, the related information of patients, medicines and diseases is integrated, and accurate medication is guided. According to the method, the administration scheme can be formulated more accurately, the curative effect is improved, side effects are reduced, a patient medication report is provided for physicians and pharmacists, the physicians and pharmacists can dynamically master patient medication information in real time, reference is provided for the physicians to formulate the next medication scheme, and the treatment effect on chronic disease patients is improved.

The invention discloses a livestock intestinal parasitic disease diagnosis and treatment method based on a visual language large model, which comprises the following steps of: training the visual language large model by utilizing a parasite image-text feature alignment data set, so that the large language model has visual capability on parasites; then, constructing a livestock and poultry parasitic disease diagnosis and treatment dialogue data set, and training the LoRA fine-tuning visual language large model by using the diagnosis and treatment dialogue data set to obtain a diagnosis and treatment dialogue model; utilizing the diagnosis and treatment dialogue model to realize livestock parasitic disease diagnosis and treatment. The diagnosis and treatment efficiency of parasitic diseases of livestock and poultry can be improved, the misdiagnosis rate is reduced, and the diagnosis and treatment process is optimized.

The invention provides a tumor electric field treatment system, tumor treatment equipment and an electrode slice quality detection method, the system comprises at least one pair of electrode slices, each electrode slice comprises a plurality of electrode units and a plurality of temperature detection units, the plurality of electrode units are divided into a plurality of row groups and a plurality of column groups, and the temperature detection units are connected with the electrode units; the grounding ends of the temperature detection units in each row group are jointly connected to a grounding pin through a control switch, and the signal ends of the temperature detection units in each column group are jointly connected to a switching unit through a dual-purpose signal line after being in short circuit with the corresponding electrode units. Thus, zone control and temperature detection signal sampling can be performed on the plurality of electrode units by using fewer conductive traces, and whether the electrode slices are damaged or qualified can be monitored based on the sampled temperature detection signals.

The invention relates to the technical field of spine deformation recognition, in particular to a spine deformation recognition method and system based on image recognition, and the method comprises the following steps: S1, data collection and preprocessing: obtaining multi-view and multi-time-point spine image data of the spine of a patient; s2, soft tissue shielding correction: automatically identifying and correcting interference of the soft tissue on the spine image data; s3, dynamic change prediction of the spine: performing dynamic change prediction on the spine image data subjected to soft tissue shielding correction; and S4, deformation judgment and result output: judging the deformation condition, the deformation type and the severity of the spine, and generating a visual report containing the spine deformation condition and future change prediction. According to the method, the noise in the image is effectively removed, the definition of the spine structure is enhanced, and meanwhile, the interference problem of soft tissue on the spine image is solved.

The invention relates to a personalized medical decision-making method based on artificial intelligence, and relates to the field of medical health and artificial intelligence, and the personalized medical decision-making method comprises the following steps: S1, inputting medical data of a patient in a patient information input module, including historical cases, examination reports, genomic sequences and real-time health monitoring data; and S2, based on the patient information, the patient health comprehensive evaluation module evaluates the health state of the patient, and the medical decision module outputs a preliminary treatment scheme according to the evaluation result. By collecting comprehensive medical data of a patient and comprehensively evaluating the health state of the patient based on the data, the illness state of the patient is more accurately grasped, a more accurate treatment scheme is made, individual differences, including age, physiological indexes, psychological conditions and disease duration, of the patient are fully considered, and by calculating the comprehensive evaluation score of the patient, the patient health state is comprehensively evaluated. The timeliness and effectiveness of a treatment scheme are ensured, and the rehabilitation speed of a patient is increased.

The invention discloses a PICC catheter real-time data acquisition-based nursing quality monitoring system, and relates to the technical field of nursing quality monitoring, the system comprises a data acquisition module, a real-time analysis module, a long-term tracking module, a risk dynamic evaluation module, a nursing intervention module and a patient monitoring module, and the modules are in electric signal connection. According to the nursing quality monitoring system based on PICC real-time data collection, by continuously monitoring physiological parameters of a patient and PICC related data, nursing personnel can respond to demand changes of the patient in time, PICC related complications can be effectively recognized and prevented, abnormal conditions can be found in time, intervention measures are taken, and the nursing quality is improved. Therefore, the risk of complications is reduced, the safety of the patient is improved, nursing personnel can make a personalized nursing plan more accurately, the pertinence and effectiveness of nursing are improved, and the treatment effect of the patient is improved.

The invention relates to the technical field of medical diagnosis systems, and discloses an AI physique diagnosis software analysis system which comprises an MCU module used for sending commands to execution modules and receiving and processing data signals of the execution modules to achieve diagnosis operation of the analysis system; the data acquisition module is used for collecting facial images and pulse information physiological indexes of the patient through an acquisition medium; the image processing module is used for analyzing facial features of the patient, the facial features comprise complexion, tongue coating and tongue quality, and corresponding evaluation data are generated through the obtained facial feature information. According to the AI physique diagnosis software analysis system, the accuracy and efficiency of traditional Chinese medicine physique identification are remarkably improved, the subjectivity and professional threshold are reduced, remarkable social and economic benefits are generated, and the system can more accurately evaluate the physique and display experimental data by using a deep learning algorithm and multi-modal data analysis.

The invention discloses an early diagnosis system for Parkinson's disease based on handwriting behavior feature recognition, which belongs to the technical field of Parkinson's disease and comprises a data acquisition module, a data processing module, a model construction module, a test optimization module, a prediction diagnosis module and an intelligent management and control module. The problems that the Parkinson's disease cannot be diagnosed in an early stage based on handwriting behavior feature recognition in the prior art, early intervention and treatment suggestions cannot be provided for patients, and the treatment effect is reduced are solved. Based on the optimal Parkinson's disease early diagnosis model, handwriting behavior real-time data is predicted, Parkinson's disease early diagnosis is carried out, the Parkinson's disease early diagnosis result is determined, a Parkinson's disease early management and control scheme is formulated, Parkinson's disease early patients are intelligently managed and controlled, and the Parkinson's disease early diagnosis and control efficiency is improved. The Parkinson's disease can be diagnosed early based on handwriting behavior feature recognition, early intervention and treatment suggestions can be provided for patients, and the treatment effect can be improved.

The invention relates to the field of medical equipment, and discloses an intracavity radiofrequency ablation system. The system includes a radio frequency generator, a catheter with a heating element, a pressure sensor, and a temperature sensor, a processor, and a display. By monitoring the temperature and the pressure in real time and dynamically adjusting the radio frequency energy output power, the safety and the accuracy of the treatment process are ensured. The system can automatically adjust treatment parameters according to the real-time contraction condition of the cavity tissue, prevent overheating, reduce damage to normal tissue, provide visual tissue contraction feedback and improve the treatment effect.

The invention discloses an intelligent nutrition and metabolism management system for tumor patients, and particularly relates to the technical field of intelligent management. Comprising a patient treatment duration division module, a patient treatment nutrition and metabolism information acquisition module, a patient treatment nutrition intelligent management module, a patient metabolism risk intelligent evaluation module, an auxiliary decision data analysis module, a patient treatment effect intelligent early warning module and a patient treatment nutrition and metabolism intelligent optimization module. According to the invention, the nutrition and metabolism monitoring data of each sub-period of the disease treatment duration of the target patient is collected, the nutrition state evaluation index and the metabolism function risk management index are calculated, and the patient treatment effect early warning index is obtained through analysis, so that the nutrition and metabolism conditions of the patient are intervened, the treatment effect evaluation accuracy is improved, and the patient treatment efficiency is improved. Waste of medical resources is reduced, and intelligent development of treatment effect decision making is promoted.

The invention discloses carboxymethyl chitosan-hyaluronic acid-stem cell exosome eye drops as well as a preparation method and application thereof, and relates to the technical field of eye drops and medical biogel. The carboxymethyl chitosan-hyaluronic acid-stem cell exosome eye drops are prepared from tannic acid modified carboxymethyl chitosan and adipose tissue-derived stromal cell exosome freeze-dried powder, the carboxymethyl chitosan-hyaluronic acid-stem cell exosome eye drops provided by the invention can effectively treat xerophthalmia, and are stable in components, safe and reliable. The three components including the tannic acid modified carboxymethyl chitosan, the hyaluronic acid and the adipose tissue-derived stromal cell exosome coexist, so that the functions of all the components can be fully played, a synergistic effect is achieved, and a better treatment effect on xerophthalmia is achieved.