849 results about "Personalized treatment" patented technology

The invention relates to the technical field of cardiovascular disease treatment, and discloses a cardiovascular disease medical decision support method based on a large model, and the method comprises the steps: obtaining multi-modal cardiovascular disease medical data, constructing a multi-source heterogeneous data set, carrying out the preprocessing and feature fusion, and constructing a combined representation model based on the pre-training large model and the fusion of traditional and western medicine knowledge. And designing a hierarchical space-time diagram convolutional network to generate a dynamic physiological state code of the patient, and constructing a multi-target optimization decision tree to generate a treatment scheme containing western medicines, traditional Chinese medicine prescriptions and non-drug intervention. A federal learning framework is adopted to cooperatively train a local model, data security is guaranteed, a global model is optimized, and a curative effect attribution module is further arranged to correct decision deviation. According to the method, multi-modal data and traditional Chinese and western medicine knowledge are integrated, personalized treatment scheme generation is achieved, data safety is guaranteed, and the cardiovascular disease diagnosis and treatment level is improved.

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 discloses a cardiovascular disease risk prediction method and system based on dietary multi-modal data and ensemble learning, and the method comprises the steps: constructing a multi-modal set through integrating multi-source heterogeneous data such as demographic statistics, dietary nutrition, clinical physiological and biochemical indexes and lifestyles; data cleaning is completed based on a box plot method and missing value processing, and key features are screened through Pearson's correlation coefficients, variance expansion factors and feature importance evaluation; a plurality of heterogeneous base models are fused by adopting a Stacking integration framework, a meta-feature matrix is generated through five-fold layered cross validation, and multi-level decision fusion is realized through a logistic regression meta-model; and quantifying the contribution weight of the dietary characteristics to the risk in combination with an SHAP method, and generating a visual interpretation chart and personalized intervention suggestions. According to the method, the accuracy and the stability of a prediction result are remarkably improved, the contribution degree and the action mechanism of dietary factors and other characteristics to the prediction result can be deeply analyzed, powerful support is provided for accurate prevention and personalized treatment of cardiovascular diseases, and the method has good practical value.

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

The invention discloses an artificial intelligence multi-modal medical image processing diagnosis and treatment system, and relates to the technical field of medical image processing and diagnosis and treatment, and the system collects multi-angle multi-modal brain image data, extracts brain region features based on multi-modal brain images, formulates a brain normal model, carries out the set fusion of the obtained brain region features, and carries out the diagnosis and treatment of the brain normal model. Generating a three-dimensional structure model of the brain area, compensating the three-dimensional structure model by using the treatment data of the patient to obtain patient features, and analyzing the patient features to obtain a diagnosis report; according to the method, the brain three-dimensional structure model is constructed by fusing the multi-modal image features and the patient treatment data, and the pathophysiological process of the brain diseases is simulated, so that the diagnosis accuracy and reliability are improved, and technical support is provided for early diagnosis and personalized treatment of the brain diseases.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention provides a radiotherapy patient skin injury real-time detection record management system based on image processing, and belongs to the technical field of radiotherapy patient skin injury real-time detection. Comprising a multispectral image acquisition module for imaging the skin of a patient at the same visual angle by adopting different spectrums to obtain three spectral images including near-infrared imaging, infrared imaging and ultraviolet imaging; a first identification module is used for fusing three spectral images to obtain an image data set, and a second identification module is used for constructing an epidermis injury coefficient # imgabs0 #, a dermis injury coefficient # imgabs1 # and a subcutaneous injury coefficient # imgabs2 # of the ith patient and evaluating the epidermis injury coefficient # imgabs0 #, the dermis injury coefficient # imgabs1 # and the subcutaneous injury coefficient # imgabs2 # respectively. A skin injury evaluation model is constructed, the comprehensive injury area ratio is calculated, the classification of injury levels is realized, data support is provided for a personalized treatment scheme, and a powerful auxiliary diagnosis tool is provided for a medical institution.

The invention relates to the technical field of medical emergency monitoring, in particular to an ICU emergency response system which comprises a preliminary risk assessment module, an emergency recognition module, a treatment scheme optimization module, a vital sign monitoring module, an emergency response coordination module, a disease evolution prediction module, a resource allocation management module and a medical decision support module. According to the method, feature engineering and logistic regression analysis are combined with medical history and real-time vital signs of a patient, emergency medical events are recognized by applying pattern recognition and natural language processing, a treatment scheme is optimized by using a support vector machine and a decision tree, personalized treatment suggestions are provided, and the time sequence and a biometric method are used for finely monitoring the vital signs; health risks are early warned, resources are reasonably allocated in emergency response through a resource scheduling algorithm and a priority queue, illness state prediction is assisted through machine learning and probability statistics, resource utilization is improved through linear programming and a resource optimization strategy, and waste is reduced.

The invention relates to the technical field of natural language processing and image processing, and discloses an emotional music adjusting method and terminal based on dynamic perception and multi-modal fusion. The method comprises the following steps: acquiring multi-dimensional feature data of a user, processing an unstructured text through a dynamic sensing field based on a large language model, and carrying out dynamic weight distribution on the multi-dimensional feature data to generate fusion features; collecting facial image data of a user, extracting facial emotion features through a visual converter model, and mapping the facial emotion features to a music feature space; the features of the two modes of text and image, namely fusion features and facial emotion features, are input into a music generation module, and personalized treatment music is generated through feature fusion coding, music structure planning and a layered decoding architecture. And music generation parameters are adjusted through a dynamic feedback mechanism to form closed-loop control. According to the method and the computer terminal, full-process intelligent processing from user feature capturing to emotional music generating is achieved, and the treatment effect and the user experience are improved.

The invention discloses a deep learning-based lung cancer early screening system, and particularly relates to the technical field of medical assistance, which comprises an intelligent data ecological module, an intelligent analysis and diagnosis module, an intelligent decision support module and a user interaction management module. According to the invention, cross-mechanism data sharing and cooperative training are realized by using data semantic understanding and federated sharing technologies, transparency and traceability of data are ensured through a block chain technology, and in an intelligent analysis and diagnosis module, a deep learning model comprehensively analyzes a multi-modal image and identifies molecular features of cancer cells, so that the accuracy of cancer diagnosis is improved. Potential early-stage lung cancer signs are found through an unsupervised learning algorithm, individual risks are dynamically evaluated, focus development is tracked, and an intelligent decision support module combines personalized treatment suggestions, optimizes clinical pathways and multidisciplinary collaboration, provides accurate decision suggestions and assists doctors in making scientific and effective treatment schemes.

The invention relates to the technical field of artificial intelligence, in particular to an old-age care chronic disease management method, system and device based on artificial intelligence and a medium. The method comprises the following steps: firstly, acquiring historical medical data of a patient, extracting features by using a pre-trained deep neural network, and performing feature analysis through an old-age disease knowledge graph; then, based on the health feature data, a time sequence prediction model is adopted to evaluate the chronic disease risk; combining a risk assessment result with physiological index data collected in real time to predict a disease development trend; a personalized treatment scheme is formulated according to the illness state evaluation and prediction result; the scheme is dynamically adjusted by analyzing treatment compliance data of the patient; and finally generating a scheme including medication reminding, complication prevention and lifestyle guidance. Through mining of historical data, accuracy of risk assessment is ensured by using time sequence prediction, individuation of a treatment scheme is ensured by using reinforcement learning, and adaptability of a management scheme is improved through dynamic adjustment.

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

The invention discloses percutaneous nerve field vagus nerve stimulation diagnosis and treatment equipment and a diagnosis and treatment method thereof, and particularly relates to the technical field of neuromedicine, intelligent medical equipment and multi-mode physical therapy. The equipment comprises a neural signal acquisition module, a nerve positioning module, a data processing and analysis module, a treatment execution module and a diagnosis result output module; the system is further provided with a battery power management module, a self-adaptive closed-loop cloud platform and a wearable fixing device, by collecting bioelectricity signals, light absorptivity and temperature data of an ear nerve area, ear target cranial nerve branches are positioned, a dynamic diagnosis map is constructed, and a diagnosis result and personalized treatment parameters are generated. Electric pulse, near-infrared light and local thermal therapy multi-mode collaborative stimulation is adopted for treatment, and parameters are monitored and adjusted in real time through a cloud platform. According to the invention, accurate diagnosis and personalized treatment can be realized, the medical efficiency and the treatment effect are improved, and an effective means is provided for nerve disease diagnosis and treatment and pain relief.

The invention relates to the technical field of medical equipment, in particular to a preset tooth cutting method and system based on image data, and the method comprises the following steps: S1, obtaining multi-modal image data by using CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and optical scanning technologies, and carrying out consistent analysis after preprocessing; and S2, performing automatic image segmentation by using a deep convolutional neural network (CNN). S3, optimizing the implant design through a generative adversarial network (GANs); s4, simulating and optimizing a cutting path through an augmented reality (AR) environment; and S5, laser micromachining is used for surface treatment of the implant. According to the method, the dentin cutting scheme matched with the specific shape and size of the bone defect area is designed through the data generated by scanning the three-dimensional shape of the complete tooth in advance, so that the image definition and the diagnosis accuracy are improved, the preoperative analysis is more accurate, and the subsequent personalized treatment design is effectively supported.

The invention provides a medical agent middleware-based full-specialized collaborative diagnosis and treatment method and system, and relates to the technical field of medical agents, and the method comprises the steps: building a basic medical knowledge graph through reinforcement learning, converting expert diagnosis and treatment experience into diagnosis and treatment probability distribution through a personalized treatment decision model, and forming an enhanced medical knowledge graph; and calculating a matching probability with a disease based on the symptom feature vector of the patient to generate a preliminary diagnosis decision, and selecting and adjusting a treatment scheme through reinforcement learning. According to the invention, general and specialized collaborative diagnosis and treatment can be realized, and medical decision accuracy and diagnosis and treatment efficiency are improved.

The invention aims to provide an acne precise treatment and drug delivery system based on multi-modal data and feedback control. The system comprises an image acquisition and identification module, which carries out acne focus identification on an obtained skin image based on a deep learning algorithm, and extracts focus feature data; the sensor acquisition module is used for acquiring skin physiological parameters and environmental parameters related to acnes; the multi-modal fusion module carries out fusion processing on the focus characteristic data, the skin physiological parameters and the environmental parameters to generate current acne state parameters; the digital twinborn prediction module constructs a digital twinborn model of the patient according to the current acne state parameters and predicts the development trend of acnes; the individualized treatment decision module formulates an individualized treatment scheme and a corresponding administration dosage according to a prediction result of the digital twinning prediction module; and a set of system capable of realizing multi-source data acquisition, automatic evaluation, prediction analysis and personalized treatment scheme recommendation of the acne of the patient is constructed.

The invention provides a prostate cancer biochemical recurrence prediction system based on a deep learning image and clinical information multi-mode model. The prostate cancer biochemical recurrence prediction system comprises an input module, a preprocessing module, a feature selection module, a fusion module, a prediction module, a data management and storage module and a result output and decision support module. According to the method, bpMRI image data and clinical information of a patient are fully utilized, multi-mode information is fully fused through a deep learning technology, and accurate prediction of biochemical recurrence is achieved. And the robustness of the model is further enhanced by adopting 5-fold cross validation and a diversified data enhancement strategy. Experimental results show that the method can assist in making more accurate patient layering and personalized follow-up visit schemes, and potentially reduces the over-treatment and missed diagnosis risks. The invention provides an efficient and reliable AI solution for prostatic cancer prognosis evaluation, and provides powerful support for promoting precise medical treatment and making personalized treatment strategies.

The invention relates to a clinical psychological treatment effect evaluation method and system based on machine learning, and the method comprises the steps: obtaining biological signals, behavior patterns and psychological state data of a patient during treatment, constructing a three-dimensional tensor structure with aligned timestamps, and carrying out the standardization of the three-dimensional tensor structure to generate a multi-dimensional data matrix; dimensionality reduction, reconstruction and verification are carried out on the matrix through a deep auto-encoder network, and unified feature vector representation is output; utilizing an improved support vector regression algorithm to establish a nonlinear mapping model of the feature vector and the curative effect score; according to the method, the curative effect score is predicted in real time, when the score is abnormal or fluctuation exceeds a threshold value, a personalized treatment scheme optimization mechanism based on the knowledge graph is triggered, deep fusion of multi-source heterogeneous data is achieved, evaluation precision and treatment adaptability are improved through a dynamic optimization mechanism, and intelligent decision support is provided for clinical psychological intervention.

The invention provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention discloses a CNN-Transform method based on multi-scale feature interaction, and mainly relates to the technical field of medical image analysis. Comprising the following steps: S1, acquiring sufficient amount of pancreas and tumor CT data, labeling the data, defining an ROI (Region of Interest), and distributing corresponding labels; s2, dividing the data set obtained in the step S1 into a training set and a test set, and preprocessing the training set and the test set by a data enhancement method including random rotation, scaling and mirroring; and S3, a CNN-Transform pancreas and tumor segmentation model PanUNet based on multi-scale feature interaction is constructed. Visual and accurate image information can be provided for doctors through accurate pancreas and tumor segmentation, personalized treatment schemes can be formulated in time, and the survival rate of patients is increased.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention discloses a pain management method based on bioelectrical impedance data and a machine learning algorithm, and relates to the technical field of medical health, and the method comprises the steps: carrying out the phase delay analysis of an impedance phase spectrum matrix, calculating the change rate of a phase angle of each frequency point along with time, and obtaining the maximum phase lag time; according to the temperature field sequence and the maximum phase lag time, time-space registration is carried out through a multi-modal data fusion method and impedance modulus changes of the corresponding areas, and a pain biomarker feature library is generated; through a bidirectional LSTM and a cross attention fusion method, time sequence modeling and cross-modal correlation analysis are carried out on bioelectricity signal characteristics extracted by a sliding window and historical pain data, a dynamic evolution rule of pain development is captured, a pain management triple containing pain intensity, intervention response and prognosis evaluation is generated, and a pain management result is obtained. And a quantitative basis is provided for personalized treatment.

The invention provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the technical field of biomedicine. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

The invention discloses a tumor electric field therapeutic apparatus convenient for adjusting electric field intensity, which comprises a physiological signal detection system used for monitoring physiological indexes of a patient in real time and obtaining real-time physiological index data of the patient, an electric field stimulation issuing system used for generating and sending an alternating electric field to the body of the patient according to electric field intensity parameters calculated by an algorithm model; and the data processing and analyzing system is responsible for collecting and processing data from the physiological signal detection system and the electric field stimulation system, combining the physiological signal detection system and the electric field stimulation issuing system to form closed-loop control, and adjusting the intensity of the alternating electric field in real time according to the output of an algorithm model. The invention belongs to the technical field of electric field treatment, and can form closed-loop control, and adjust the intensity of an alternating electric field in real time according to the output of an algorithm model, so as to adjust the intensity of the electric field according to the specific conditions of different patients, carry out personalized treatment for different patients, and improve the treatment effect.

The invention relates to an automatic image segmentation and recognition system for the radiology department, and relates to the technical field of intelligent medical treatment. Comprising the following steps: an image preprocessing module provides a high-quality image for subsequent analysis by applying a multi-scale transformation and adaptive filtering technology; the feature extraction and fusion unit accurately excavates features by means of a CNN and an attention mechanism; the disease diagnosis reasoning engine is combined with the RNN and a medical knowledge base to deduce disease details; and the personalized treatment scheme recommendation module customizes a scheme by using reinforcement learning according to diagnosis and patient information. In addition, the system also comprises a virtual case generation unit, a model interpretability analysis unit, a self-supervised pre-training unit, a continuous learning unit, a distributed cooperative training architecture and a cross-platform adaptive interface. The method comprises the steps of collecting and standardizing image data, inputting the image data into a system to obtain diagnosis and schemes, and visually displaying auxiliary decision-making. The system efficiently processes radiology department images, improves diagnosis accuracy, optimizes a treatment scheme, adapts to multi-center and multi-platform requirements, and is expected to promote intelligent development of radiology department diagnosis and treatment.

The invention provides a construction method of a brain rehabilitation cognitive training effect prediction model. The method improves the accuracy and individuation level of cognitive training effect prediction. Firstly, state data of a subject is obtained and preprocessed; thirdly, obtaining unified features by adopting an extraction layer, carrying out combined modeling of a spatial topological relation and dynamic time sequence evolution by adopting a fusion layer based on the unified features and generating spatio-temporal features, and adjusting model parameters by adopting an adaptation layer according to baseline features of a subject to ensure personalized prediction; and finally, in a model operation process, triggering online updating through concept drift detection, and generating a confidence interval and an interpretability score of a prediction result based on Bayesian reasoning. According to the method, through state data processing, personalized adaptation, dynamic adaptation and Bayesian reasoning, the precision, stability and interpretability of brain rehabilitation cognitive training effect prediction are remarkably improved, and powerful support is provided for personalized treatment and clinical decision making.

The invention belongs to the technical field of ablation puncture navigation, and particularly relates to an ablation puncture surgery navigation system under AI visual guidance. Through cooperative work of the phase monitoring module, the channel construction module, the needle moving control module, the displacement prediction module and the feedback control module, an intelligent surgical navigation system is constructed, breathing phase information and multi-modal medical image data of a patient can be obtained in real time, and therefore a safe and optimized needle moving path is accurately planned, and the operation accuracy is improved. Meanwhile, through real-time monitoring and quantitative evaluation of parameters such as tissue deformation and pressure distribution in the needle moving process, potential operation risks can be found and corrected in time, smooth operation is ensured, in addition, the system can dynamically adjust the needle moving path and the puncture angle according to the real-time breathing state of a patient, and the operation safety is improved. The accuracy and the safety of the operation are further improved, so that intelligent management and personalized treatment in the ablation puncture operation process are realized.

The invention discloses a lung cancer lifetime prediction system based on prognosis factor multi-data fusion, and belongs to the technical field of lung cancer prognosis prediction, and the system comprises a multi-source data collection module which is used for collecting prognosis multi-source data of a patient; the multi-source data processing module is used for carrying out preprocessing and feature extraction on the prognosis multi-source data of the patient; the multi-source data fusion module is used for carrying out cross-modal alignment and fine-grained fusion on the extracted multi-modal feature vectors, and capturing a dependency relationship between modals based on a hierarchical attention mechanism to form patient prognosis fusion data; and the survival analysis and prediction module is used for analyzing the prognosis fusion data of the patient according to the lung cancer lifetime prediction model, automatically predicting the lifetime of the patient and displaying the lifetime in a visual form. The problems that existing lung cancer lifetime prediction is low in accuracy and cannot provide support for personalized treatment are solved. The lung cancer lifetime prediction accuracy can be improved, and support can be provided for personalized treatment.

The invention discloses a positioning puncture surgery robot system with a reconfigurable working space, and the system comprises a multi-mode image collection module which is used for obtaining a high-resolution image of a target region of a patient; the three-dimensional anatomical model reconstruction module is used for generating a three-dimensional anatomical model containing a skeleton structure and a soft tissue structure; the target point selection and puncture path generation module is used for automatically recommending puncture target points according to operation types and clinical requirements and automatically generating a plurality of preliminary puncture paths; the multi-plane segmentation and path injection module is used for segmenting the three-dimensional anatomical model from a coronal plane, a sagittal plane and an axial plane and generating slices, and injecting puncture paths into the corresponding slices; by integrating multi-modal image acquisition, three-dimensional anatomical model reconstruction and automatic path planning and optimization setting, the operation efficiency and accuracy are remarkably improved, the operation risk is reduced through real-time data analysis, and a more personalized treatment scheme is provided for a patient.