81 results about "Clinical scenario" patented technology

The invention provides a scientific and technological literature intelligent retrieval method and related equipment based on generative artificial intelligence, and the method comprises the steps: carrying out the multi-layer semantic annotation of medical scientific and technological literatures, constructing a symptom-disease dynamic association map, and building a medical scientific and technological literature knowledge base; performing medical context analysis and multi-modal feature extraction on user query, and generating a unified retrieval vector in combination with Boolean operation nested analysis and semantic alignment processing; performing evidence grading retrieval and clinical scene matching based on the unified retrieval vector to obtain a preliminary candidate literature set, and optimizing the preliminary candidate literature set into a target candidate literature set through fine-grained semantic recalculation; and calculating a retrieval prior probability according to the evaluation dimension, performing knowledge weighted fusion on the candidate literature, and generating a medical science and technology literature recommendation report. According to the method, the medical term association relationship is deeply understood through the association map, the result is ensured to be matched with the patient characteristics through evidence grading retrieval and clinical scene matching and screening, and the accuracy of document retrieval is improved.

The invention relates to the technical field of tumor treatment, in particular to an intelligent decision-making system for tumor precise targeted medication based on individual mutation information, which comprises a data processing layer, a variation annotation and function prediction layer, a knowledge base integration layer and a scheme decision-making engine and report visualization module. According to the intelligent decision-making system for tumor precise targeted medication based on individual mutation information, a rule engine and a prediction model are combined, dynamic priority ranking is output, multi-model fusion decision making is achieved, and clinical scene deep adaptation is achieved by predicting primary and secondary drug resistance, calculating liver and kidney function adjusting dosage and generating a combined medication time sequence scheme; through an individualized drug delivery scheme, combination drug use optimization is achieved, a visual clinical report is generated, clinical executable operation is further strengthened, and through algorithm quantification, a dynamic knowledge graph, AI auxiliary decision making and a clinical operation closed loop, the next-generation technical research direction of a tumor precise drug use system can be represented.

The invention belongs to the technical field of biomedical materials, and relates to ultrasonic response adhesion integrated hydrogel as well as a preparation method and application thereof, the hydrogel comprises a hydrogel matrix, an interface anchoring agent and a functional load component; the preparation method comprises the following steps: 1, preparing a hydrogel precursor solution, and preparing a hydrogel matrix into a solution; 2, carrying out mold molding or injection crosslinking on the solution prepared in the step 1 to form hydrogel; 3, preparing an interface anchoring agent solution, and dissolving or dispersing an interface anchoring agent in a solvent to prepare the interface anchoring agent solution; the ultrasonic responsive adhesive hydrogel platform provided by the invention can provide an efficient, controllable and universal solution for tissue adhesion, targeted therapy, wearable attachment and the like in a complex microenvironment, and is suitable for a plurality of clinical scenes such as chronic wound management, surgical postoperative closure, infection control, adhesive bioelectronic fixation, soft tissue engineering and the like.

The invention discloses an intelligent auxiliary diagnosis system and method based on big data analysis, and relates to the field of intelligent auxiliary diagnosis. Original CT scanning data and electronic medical record data of a patient are processed in parallel, and a deep learning model is introduced to extract high-dimensional image features and text features; through constructing a knowledge prior relation matrix derived from clinical guidelines and expert rules, knowledge-guided cross-modal attention fusion is carried out on image features and text features so as to realize deep and accurate joint characterization of patient conditions, and then intelligent risk prediction and classification are carried out on the basis of the deep and accurate joint characterization. Thus, through deep coupling of data-driven feature learning and medical knowledge-driven logical reasoning, intelligent and logical integration of multi-modal medical information can be realized, and the accuracy and reliability of an auxiliary diagnosis model in a complex clinical scene are effectively improved.

The invention discloses an emergency treatment high-risk patient real-time grading method based on an improved multi-mode Transform algorithm. The method comprises the following steps: S1, generating a time synchronization multi-mode event sequence; s2, obtaining a corresponding single-mode feature representation tensor; s3, obtaining a risk weighted cross-modal attention matrix; s4, in the improved multi-modal Transform fusion network, generating a fusion feature representation tensor by using a time sequence sliding window cache and incremental updating mechanism, and outputting a risk grading label and a corresponding risk grading confidence coefficient based on the fusion feature representation tensor; s5, inputting the risk grading label and the risk grading confidence into the interpretive sub-network, and generating clinical causal chain prompt information; and S6, synchronizing the risk grading label, the risk grading confidence and the clinical causal chain prompt information. According to the method, the accuracy of high-risk patient identification and the adaptability of the model to a clinical complex scene are remarkably improved, and a test result shows that the real-time identification accuracy of a high-risk case is improved compared with that of a conventional multi-modal model.

The present invention relates to the field of intelligent medical technology, and in particular to an intelligent outpatient triage method and system based on a large model. An intelligent outpatient triage system based on a large model comprises: a data acquisition module and an outpatient triage module. The present invention determines the triage priority corresponding to the patient by sending the patient's structured data into the outpatient triage model for processing, and performs triage operations on the patient based on the patient's corresponding triage priority, without the need for medical staff to make judgments based on experience; four different judgment criteria are incorporated into the outpatient triage model, and the feature information expression effect and feature information fusion effect under the perspectives of the four different judgment criteria are improved through a multi-standard feature extraction layer, a feature transmission layer, and a difference complementary enhancement layer, so that the outpatient triage model can be adapted to more clinical scenarios and has higher adaptability to complex patient conditions.

The invention relates to the technical field of health data processing, in particular to a clinical patient self-health management recommendation method and system. Obtaining multi-dimensional health data of the target patient, including a clinical measurement index set and a self-report symptom set, and performing standardized preprocessing on the multi-dimensional health data to generate standardized health data; constructing a personal health state map based on the standardized health data; performing health risk traceability analysis according to the personal health state map, and identifying a key risk node set and a risk propagation path; generating an initial health management recommendation strategy set in combination with a predefined medical knowledge base and patient personalized constraints; and dynamically optimizing the initial health management recommendation strategy set based on the patient feedback data and the health state change information, and outputting an optimized personalized health management recommendation scheme. According to the invention, personalized, precise and dynamic patient self-health management recommendation in a clinical scene can be realized.

The invention relates to the technical field of intelligent traditional Chinese medicine diagnosis and treatment, and discloses a thermosensitive moxibustion tracking algorithm and system based on long-acting moxibustion therapy sensing and posture three-dimensional sensing, and the algorithm comprises a long-acting flexible thermosensitive sensing subsystem, a three-dimensional posture sensing module, a multi-modal data fusion processing unit, a dynamic thermal field modeling algorithm, and a self-adaptive moxibustion applying control subsystem. Through full-chain innovation of long-acting sensing, three-dimensional sensing, coupling modeling and intelligent control, the performance stability problem (drift error is less than or equal to + / -0.3 DEG C) of the flexible sensor under long-term work (more than or equal to 72 hours) is solved; establishing a posture-thermal field-time four-dimensional coupling model, and realizing submillimeter thermosensitive point tracking under a dynamic posture; a millisecond-level response (less than or equal to 50ms) closed-loop control system is constructed, and the adaptability to complex clinical scenes is improved; therefore, the key technical problem in the dynamic moxibustion therapy scene is systematically solved.

The invention relates to the technical field of medical instruments, and discloses a surgical instrument preparation early warning system and method based on big data, and the system comprises an association module, an urgent module and an early warning module. The method corresponds to the system. According to the application, the instrument set association library is constructed based on the historical operation content through the association module, and accurate matching of the operation content and the doctor habits is realized in combination with the knowledge relationship and the preference relationship; the urgent module uses a traceability system to automatically compare the inventory and finely identify the urgent instrument package, so that manual counting errors are avoided; the early warning module determines the circulation starting time of the instrument set, monitors the time difference value in real time, and quickly responds to the operation demand of the day; therefore, automatic and refined surgical instrument preparation early warning based on big data is realized, matching accuracy and circulation efficiency are improved, resource management is optimized, personal errors and resource waste are reduced, adaptability to complex clinical scenes is enhanced, and surgical safety and high efficiency are guaranteed.

The invention provides a hospital equipment department consumable management method based on an AIGC technology. The hospital equipment department consumable management method comprises the following steps of S1, performing multi-modal data acquisition and fusion modeling; s2, a step of constructing a generative demand prediction model; s3, generating a dynamic inventory optimization strategy; s4, performing semantic generation of an intelligent replenishment strategy; s5, a supplier portrait and intelligence matching step is carried out; s6, automation of compliance review is carried out; s7, carrying out three-dimensional digital twin modeling; s8, performing an anomaly detection and self-healing mechanism; s9, performing consumable full life cycle tracing; and S10, performing a cognitive evolution system. According to the method, mathematical modeling and clinical scenes are deeply fused, compared with a traditional SPD mode, leap-type upgrading is achieved in three dimensions of inventory cost, management efficiency and medical safety, and technical support is provided for hospital refined operation under DRG / DIP payment reform.

The invention relates to the technical field of medical temperature control, and discloses an ice blanket machine dynamic temperature control method based on body temperature feedback. The method comprises the following steps: acquiring body surface temperature sensing data and core temperature simulation data of a target object through body temperature multi-source sensing, and constructing an original body temperature sensing data set; generating a physiological state feature vector according to the data, and performing physiological state evaluation; based on the feature vector, generating a multi-modal body temperature state tensor through multi-source sensing fusion modeling; then, an optimized temperature control strategy is generated according to the multi-modal body temperature state tensor by adopting a reinforcement learning method for dynamic binding of a clinical constraint rule component; performing integrity verification and clinical safety rule conflict detection on the optimization strategy to generate a safety control instruction sequence; and finally, the ice blanket machine is driven to execute temperature regulation and control operation according to the sequence. According to the method, the accuracy and safety of temperature regulation and control of the ice blanket machine are improved, and complex clinical scene requirements are met.

The invention discloses a patient rehabilitation effect evaluation system based on nursing information processing. The system comprises an evaluation data acquisition module, a data preprocessing module, an effect evaluation model construction module, a model hyper-parameter optimization module and a rehabilitation effect evaluation module. The invention relates to the technical field of intelligent patient rehabilitation effect evaluation, in particular to a patient rehabilitation effect evaluation system based on nursing information processing, and the system comprises the steps: obtaining an effect evaluation original data set through data collection; a data preprocessing method of data cleaning, feature extraction, time sequence alignment, data standardization and data set segmentation is adopted; a double-flow deep network model is adopted as an effect evaluation model, and it is ensured that an evaluation result conforms to clinical logic while feature collaboration of heterogeneous medical data is achieved; an improved leapfrog algorithm is adopted to optimize model hyper-parameters, dynamic balance exploration and development are achieved, meanwhile, the stability of the algorithm is enhanced through population division, and stable performance of an evaluation system in different clinical scenes is ensured.

The invention relates to the technical field of medical image processing, in particular to a dynamic ultrasonic noise reduction model training method and a dynamic ultrasonic noise reduction method and device. The method comprises the following steps: acquiring an ultrasonic video covering multiple clinical scenes such as antral pneumatosis, hydrops, solid contents, long antral antrum and cardiac anatomical occlusion; marking the precise boundary of the organ frame by frame by a clinical expert, segmenting and complementing a missing section, optimizing the contour of a fuzzy region, and inhibiting or erasing artifacts; a deep learning model is trained based on the labeled data, so that the model can call an exclusive noise reduction algorithm according to organ categories (the antral sinuses adopt morphological reconstruction filtering guided by ellipse / long axis prior, and the heart adopts time-space joint deblurring driven by a cardiac cycle and combining motion compensation and anisotropic filtering); and weighted noise reduction is carried out on the suspected lesion or the user indication area. In the reasoning stage, a high-fidelity and low-noise video stream can be output in real time by inputting any dynamic ultrasonic video, and organ boundary metadata is attached.

The invention relates to the technical field of medical informatization and intelligent nursing, in particular to a general surgery department patient postoperative complication risk prediction and nursing decision-making system, which comprises a baseline manifold construction module for initializing an individualized steady state manifold in a preset multi-dimensional state space based on historical operation parameters and basic state data; the perturbation response topology evaluation module is used for calculating a recovery attenuation coefficient after the real-time phase trajectory deviates from the individualized steady state manifold so as to generate a steady state dissipation index and a growth rate; the offset course quantity decoding module is used for adjusting a dynamic threshold value based on a time delay parameter and an embedded dimension parameter on the basis of the basic state data; the risk prediction module is used for weighting, merging and normalizing the growth rate of the steady-state dissipation index and the modulus length of the course deviation vector to generate a risk prediction evaluation result; the decision closed-loop module is used as a secondary perturbation variable and sends the secondary perturbation variable to the perturbation response topology evaluation module for continuous monitoring; according to the invention, the baseline anchoring precision in different clinical scenes is improved.

The application discloses a dynamic knowledge graph construction method, an electronic device and a computer readable storage medium. The method comprises the following steps: determining a four-diagnosis element set for a patient according to a medical record text of the patient, wherein the four-diagnosis element set comprises a plurality of four-diagnosis elements, and the four-diagnosis element is a characteristic related to a disease possessed by the patient described from the perspective of four-diagnosis of traditional Chinese medicine; performing traditional Chinese medicine long logic chain deduction based on the four-diagnosis element set, performing through different thinking threads, and obtaining at least one target syndrome element, scores of each target syndrome element, at least one target syndrome matching the target syndrome element, and scores of each target syndrome; and generating a dynamic knowledge graph according to the four-diagnosis element set, the scores of each target syndrome element and the scores of each target syndrome. Through the scheme, a knowledge graph based on dynamic changes of clinical conditions can be obtained, so that fast, efficient and accurate clinical thinking training for traditional Chinese medicine is realized.

This application relates to a method and system for training neurosurgical specialist nurses based on a virtual teaching and research room. The method includes: acquiring patient data and clinical scenario parameters corresponding to preset neurosurgical training tasks from a medical database; constructing a virtual training environment using virtual simulation technology based on the patient data and clinical scenario parameters; collecting user interaction behavior data in real time through a sensor network deployed in the environment; analyzing the behavior data in real time and driving the virtual environment to generate real-time feedback effects; identifying key operation points using a pre-trained LSTM analysis model based on the behavior data and feedback effects; and generating a training feedback report based on the key operation points. This method can improve the realism of neurosurgical specialist nurses' operations in the virtual environment, the assessability of the training process, and the real-time nature of feedback, effectively assisting in professional skills training and assessment.

This invention discloses a seamless switching medical simulation training system that integrates virtual and real-world childbirth and postpartum hemorrhage scenarios. Its features include: a maternal model, a childbirth simulation mechanism, and a postpartum simulation mechanism, one of which can be selectively mounted on the maternal model; the childbirth model mechanism includes an infant model, a birthing belly component, a birthing vagina component, and a drive device. The drive device is located within the maternal model, and the infant model is mounted on the drive device, allowing the drive device to move the infant model linearly and rotate towards the birthing vagina component; the birthing belly component is equipped with undulating components; this system achieves seamless switching between childbirth and postpartum hemorrhage scenarios through a modular and detachable structure, realistically simulating clinical scenarios such as the childbirth process and postpartum hemorrhage care. It is comprehensive in function, realistic in operation, and can effectively improve the standardized operation and emergency response capabilities of medical personnel.

This invention provides a sepsis consultation method and system based on multi-turn dialogue, belonging to the field of intelligent consultation equipment technology. This invention achieves effective integration of standardized medical knowledge and personalized clinical experience through the construction of a two-dimensional knowledge graph and cross-graph interactive retrieval, improving the accuracy and reliability of sepsis diagnosis and treatment recommendations. Through structured sepsis diagnosis and treatment chain constraints and a confidence-driven dynamic strategy, it alleviates the illusion problem of large language models in complex clinical reasoning, ensuring that the generated content conforms to clinical diagnosis and treatment logic norms. Through semantic anchor guidance, hierarchical activation, and knowledge feedback mechanisms, it achieves multi-granular semantic association from macro-clinical scenarios to micro-medical concepts, balancing retrieval accuracy and knowledge coverage. The confidence evaluation system comprehensively considers knowledge sufficiency, redundancy, and consistency, adaptively balancing knowledge utilization and information exploration, improving the reasoning efficiency and diagnosis and treatment quality of multi-turn dialogue.

The invention relates to the cross technical field of natural language processing and medical semantic understanding, and discloses a dialect medical term recognition capability improvement method based on a natural language error correction model, and the method comprises the steps: collecting dialect medical term voice data and text data, and covering a plurality of dialect types and clinical scenes; based on the dialect medical term voice data and the text data, carrying out structured corpus construction processing to generate structured dialect medical term corpus data; voice data and text data covering multiple dialect types and clinical scenes are collected, the universality and representativeness of data sources are guaranteed, meanwhile, structured corpus construction processing is conducted, pronunciation annotation, expression annotation, semantic annotation and scene annotation data are generated by adopting a four-dimensional annotation rule, corpus information is complete and standard, and the data annotation efficiency is improved. And the accuracy and reliability of subsequent model training are ensured.

The present disclosure discloses a method, electronic device, and storage medium for simulating coil placement in an aneurysm. The method includes: obtaining a coil model, a three-dimensional rigid body model, and an aneurysm model, wherein the coil model is a non-rigid body model; compressing the coil model using the three-dimensional rigid body model; placing the compressed coil model into the aneurysm model so that the coil model deforms under its own internal force; and obtaining a final simulation result in response to the deformation parameters of the coil model meeting a preset condition. Through the solution of the embodiment of the present disclosure, it is possible to impose constraints on the non-rigid coil model through the three-dimensional rigid body model, and use the internal force of the coil model itself to achieve expansion deformation in the aneurysm model, thereby directly simulating the coil compression and placement process quickly through physical methods, which can improve the simulation speed and have higher clinical value for emergency clinical scenarios.

Improved perioperative medicine involving semantic AI The invention provides, amongst other aspects, a device comprising: an input interface; a control module connected to the input interface; an output interface connected to said control module; wherein the control module is configured, upon initiation of a respective next measurement cycle, 5 to: receive, via the input interface, respective next measurement data measured with respect to a patient; process the respective next measurement data, based at least on a respective previous graph associated with a respective previous measurement cycle, for obtaining a respective next graph associated with the respective next measurement cycle; output, via the output interface, the respective next graph; wherein the previous graph and the next graph each relate to knowledge 10 graphs and each comprise: a plurality of entities relevant to a clinical scenario to which the respective measurement cycles relate, and a plurality of relations between the entities; and wherein the previous and the next graph differ in at least one entity or relation.

Systems and methods for controlling fluid delivery via a manually administrable medication container to a patient through a fluid delivery pathway are provided. The systems and methods described herein incorporate rules-based clinical decision support logic to drive a flow control valve within a flow pathway based on a determination of whether or not an IV fluid connected to an input port on the pathway is appropriate for patient administration by considering such factors as patient-specific clinical circumstances, current medical orders, and accepted delivery protocols. Related apparatus, systems, methods and articles are also described.

This invention relates to the field of simulation training equipment technology, and discloses a whole-body vascular interventional simulation model and a blood circulation control system, including an integrated base in which a simulated blood vessel is disposed; and a blood circulation component, which includes a reservoir and a drive pump. The reservoir is connected to the inlet end of the simulated blood vessel through the drive pump. This invention solves the technical problems of existing vascular interventional simulation models being unable to simulate vascular compression scenarios and having insufficient linkage between blood circulation and compression states by coordinating the simulated blood vessel with blood circulation, compression, and air circuit components, combined with the overall coordinated control of the control unit. It can also realize multi-position vascular compression simulation, accurately reproduce clinical scenarios such as vascular stenosis and compression hemostasis. The overall structure is simple and highly realistic, effectively improving the completeness and pertinence of interventional training, and providing physicians with a training environment close to clinical reality.

The present invention discloses a nerve block simulation training method and related equipment. The method includes: constructing ultrasonic simulation models of different tissue parts of the human body based on standard three-dimensional anatomical structure information and simulated ultrasonic information of different tissue parts of the human body, and associating the ultrasonic simulation models of different tissue parts with the tissue simulator through coordinate information; tracking the relative position information and relative angle information of the simulated ultrasonic probe and the tissue simulator, and generating corresponding simulated ultrasonic images through the ultrasonic simulation model; tracking the position information and angle information of the simulated nerve block puncture needle in the tissue simulator, taking the intersection of the coordinate range of the puncture needle and the coordinate range of the simulated ultrasonic probe section, obtaining the coplanar coordinate range of the puncture needle and the simulated ultrasonic probe section, and converting the point-to-point into the simulated puncture needle ultrasonic image. It solves the problem that the simulated puncture site is limited, the clinical situation cannot be truly simulated, the efficiency of the transformation of simulation teaching to clinical practice is reduced, and the application value of actual clinical cases is lacking.

The present invention relates to a medicament injector including an applicator with a cartridge-accepting receptacle, and interchangeable, pre-filled medicament-containing cartridges. Each cartridge includes a barrier member having a closed position in which access to the medicament is inhibited. The barrier member is movable from the closed position to an open position, in which the medicament is accessible via the applicator for administration, when the cartridge is inserted into the receptacle. The barrier member is moves back to the closed position when the cartridge is automatically removed from the receptacle once the medication is delivered. This allows for plurality of pre-filled medicament cartridges to be selected on demand via a need-based scenario for quick assembly with the medicament injector for use in various clinical scenarios to reduce the bulk, errors, complexity, and steps in medicament delivery while increasing the versatility, safety, and speed of medicament delivery in time critical situations.

The invention relates to the field of medical simulation training instruments, in particular to an ultrasonic guided puncture training system and method based on multi-modal feedback. The system comprises a bionic tissue module, a puncture needle and a control host. Conductive target units in various shapes such as a circular shape, an oval shape and an irregular shape are embedded in the bionic tissue module according to the training difficulty gradient so as to correspond to graded training targets of different clinical scenes. And the control host is electrically connected with the puncture needle and each target point unit to form a trigger circuit. During training, an operator punctures and selects a target spot under the guidance of an ultrasonic image, and when a needle point accurately touches the target spot, a circuit is switched on and real-time acousto-optic feedback is generated. According to the method, standardized and stepped full-process puncture skill training is realized through a structured polymorphic target design and an electric and image dual-mode feedback mechanism, and the pertinence, the real-time performance and the clinical conversion effect of training are effectively improved.

This invention discloses an automatic nursing documentation generation system based on speech recognition. The system includes a speech acquisition module, a medical-specific speech recognition engine module, a structured nursing documentation parsing module, a system integration and adaptation module, a data security and compliance management module, and a terminal interaction module. This invention achieves automated, voice-driven generation of medical and nursing documentation throughout the entire process by adapting speech acquisition to all clinical scenarios, optimizing speech recognition specifically for nursing, and automatically generating structured nursing documentation. It can be seamlessly integrated into existing hospital medical document writing systems, bedside nursing cart systems, and PDA mobile nursing terminal systems as a standalone app or functional plugin, allowing for rapid deployment without modifying the original system architecture. This solves the core pain point of tedious medical document writing for frontline clinical medical staff, which consumes more than half of the consultation time, improving nursing documentation writing efficiency by over 80% and significantly reducing the time spent on non-medical documentation by medical staff.

This invention relates to the field of biomedical technology, and more particularly to a PD-L1-targeting polypeptide, its conjugate, and its applications. The polypeptide possesses the amino acid sequence shown in SEQ ID NO.1 or SEQ ID NO.2. This invention de novo designs a high-affinity PD-L1-targeting specific polypeptide. This polypeptide achieves extremely high affinity with the extracellular domain of the PD-L1 protein through its unique spatial conformation, exhibiting excellent targeting and recognition capabilities. The polypeptide provided by this invention can be conjugated with various modified molecules, such as radionuclides, and the constructed molecular imaging probe exhibits excellent tumor tissue targeting and enrichment capabilities, superior biocompatibility, and clear imaging contrast, enabling specific recognition of PD-L1-positive tumor lesions. It can be applied to clinical scenarios such as early precision diagnosis of tumors, screening of patients for immunotherapy, real-time monitoring of efficacy, and prognostic assessment, possessing significant translational medical value and broad application prospects.

This invention provides a virtual laparoscopic digital twin method, device, and storage medium. The method includes the following steps: constructing a three-dimensional anatomical model based on the patient's preoperative medical imaging data; planning the virtual laparoscopic spatial travel path in the model coordinate system and dividing it into path sub-intervals corresponding to each clinical scenario; pre-setting key nodes in each sub-interval and defining the anatomical structure set of each node, integrating them to form a path prior model; during the operation, segmenting real-time laparoscopic video to identify anatomical structures and surgical instruments; extracting semantic features based on the segmentation results of multiple consecutive frames and determining the current state of the surgical scenario; matching the corresponding sub-interval from the path prior model according to the current scenario state and updating the laparoscopic advancement parameters; when advancing to the vicinity of a predefined node, triggering a viewpoint switch and adjusting the laparoscopic viewpoint to the preset pose corresponding to the node. This invention solves the problem in the prior art that the virtual viewpoint cannot automatically and stably advance with the progress of the operation.

This invention discloses a data-driven modeling method for deformation of the stress surface of a dental abutment, belonging to the field of digital modeling. The method includes: acquiring geometric structure, material properties, and clinical stress data of the dental abutment through non-contact 3D scanning and dynamic force sensing technology; constructing a standardized dataset after preprocessing; and annotating deformation features with clinical experience. By extracting core features and expanding the sample by fusing virtual and real data, a data-driven deformation prediction model is established, utilizing a small-sample adaptation framework and attention mechanism to achieve high-precision nonlinear mapping. After clinical sample validation and iterative optimization, the model has adaptive update capabilities and can be applied to abutment design, installation calibration, and stability monitoring. The advantages of this invention are: based on multi-dimensional clinical data acquisition, preprocessing, and expansion, combined with a small-sample-adaptive modeling framework and attention mechanism, accurate deformation prediction is achieved; after optimization and updating, it is adapted to clinical scenarios, assisting in abutment design and treatment.