64 results about "Preoperative imaging" patented technology

A robotic surgical system includes one or more robotic actuators configured to interact with biological tissue during a surgical procedure. A plurality of sensors include at least one of fiber Bragg grating sensors, piezoelectric strain sensors, or magnetostrictive sensors to capture real-time mechanical, elasticity, or deformation data from biological tissues. deep learning engine trained on a dataset comprising tissue mechanical responses across multiple tissue types, pathological states, and patient demographics. Pre-contact predictive adjustment profiles are generated for anticipated tissue interactions using preoperative imaging data registered to intraoperative coordinates. Intraoperative deviations are detected from predicted mechanical behavior and autonomously recalibrate actuator forces. Upcoming surgical maneuvers are anticipated based on prior task sequences and adjust actuator stiffness or damping properties in preparation for anticipated contact. An emergency override of actuator forces is provided via an anomaly detection module when real-time sensor data deviates beyond a threshold from the predicted safe mechanical response range. A feedback loop iteratively refines the deep learning engine during the procedure using supervised learning updates, anomaly detection, and reinforcement learning strategies. The reinforcement learning model is optionally shared across procedures to optimize distributed actuator force patterns for minimizing localized and cumulative tissue stress.

The invention discloses a three-dimensional visualization head and neck tumor preoperative imaging system based on deep learning. The method comprises the following steps: image analysis and preprocessing; carrying out image denoising and spatial-temporal feature extraction based on a deep learning convolutional neural network architecture, and introducing a u-Net model to realize image segmentation and semantic annotation; performing three-dimensional reconstruction and surface rendering by adopting a Marking Cubes algorithm, and realizing visual interaction image presentation and personalized operation by combining volume rendering and surface rendering; and data is output through multiple ports, and data viewing and interactive operation of a web terminal and a mobile terminal are supported. The method has the advantages that according to the technical scheme, through fusion of a three-dimensional attention mechanism and multi-phase data, the tumor segmentation Dice coefficient reaches 0.92 + / -0.03, and the method is superior to a traditional threshold segmentation method.

The invention provides an intraoperative path optimization method and system based on solid tumor preoperative image analysis, and the method comprises the steps: constructing a three-dimensional model of a tumor and peripheral blood vessels and nerves according to a preoperative image, and carrying out the dissection risk marking of the three-dimensional model through a deep learning model, generating an anatomical risk image with risk gradient value distribution; taking the minimum intraoperative bleeding amount and the highest tumor excision rate as a reward function, combining the three-dimensional model, an anatomical risk image and historical similar case data, and simulating multiple operation paths based on an adversarial reinforcement learning algorithm to obtain an optimal operation path; the method comprises the following steps: acquiring the pose, ultrasonic image and fluorescence image of a surgical instrument in real time during surgery, matching the preoperative image with the intraoperative image based on a biomechanical model driven non-rigid registration algorithm, predicting organ displacement, and correcting an optimal surgical path, thereby avoiding accidental injury of key tissues, ensuring that an infiltration area is brought into the surgical path in time, and improving the surgical efficiency. Residues are reduced, and static planning failure is avoided.

The invention provides a spinal pedicle screw implantation system based on a multi-modal image and MR navigation, and relates to the technical field of screw implantation, the system comprises a preoperative image processing module used for identifying bony characteristics of a patient and delimiting a screw implantation safety boundary according to the bony characteristics; and the bone image fusion navigation module is used for analyzing to obtain anatomical data of the patient, and calculating a screw implantation path according to a crown porcupine optimization algorithm in combination with bony characteristics. And the body position correction module is used for calculating the body position deviation value and synchronously adjusting the screw implantation path according to the body position deviation value and the screw implantation safety boundary. And the path risk early warning module is used for calculating the distance between the surgical tool and the key tissue in real time, and performing collision risk assessment and early warning. And the postoperative evaluation module is used for quantitatively evaluating the screw implantation precision and generating a postoperative report. According to the method, more accurate positioning can be provided for screw implantation, the accuracy of a screw implantation path is ensured, early warning is performed through extraction, a doctor is reminded to take measures in time, and operation safety is ensured.

The invention provides a training method of a CT and DSA real-time automatic fusion registration system for structural heart disease intervention and a storage medium. The training method comprises the following steps: establishing a registration system data set; establishing an initial registration system; based on the registration system data set, training an initial registration system to obtain a trained CT and DSA intraoperative registration system; and inputting the preoperative CT image data and the intraoperative real-time DSA image data of the person to be identified into the trained CT and DSA intraoperative registration system to obtain an intraoperative real-time fusion navigation image and intraoperative risk graphical prompt information. According to the method, cross-modal and cross-time-point dynamic registration can be realized, higher-precision intraoperative navigation support is provided for structural heart disease intervention, the operation safety is improved, and radiation exposure is reduced.

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

The invention discloses a complication prediction method and system based on an SHAP-random forest, and relates to the technical field of medical data mining. The method comprises the following steps: acquiring preoperative CT images and puncture path planning data, and calculating risk factors; performing numerical value standardization, nonlinear transformation and interactive feature generation on the risk factors to obtain feature vectors, calculating mutual information scores of feature values in the feature vectors, if the mutual information scores are greater than an experience threshold, retaining the feature values, and after traversal is finished, obtaining updated feature vectors; on the basis of a random forest model, taking the updated feature vector as an input value, and calculating a complication probability; quantizing the contribution degree of the characteristic value based on a Shapley value; according to clinical indexes, the risk threshold is dynamically corrected, the complication risk level is divided, and complication prediction is completed, the problems that a static threshold ignores the blood coagulation state difference of a patient and a black box model cannot provide a decision basis are solved, and the complication misjudgment probability is reduced.

The invention provides a method, a device and equipment for identifying path deviation in a screw implanting process, and belongs to the field of data processing. The method comprises the steps that target stress information and pose information of the tail end of a target robot used for implanting a target screw are obtained, and the tail end of the target robot holds the target screw; determining a current implantation path of the target screw according to the target stress information and the pose information; obtaining a target implantation path, wherein the target implantation path is generated based on preoperative image data of a patient; and determining a path offset condition according to the current implantation path and the target implantation path, wherein the path offset condition is used for indicating whether path offset exists or not. The method and the device are used for recognizing path deviation in the nail implanting process based on a robot, and the path precision judgment capability and the operation safety of a robot operation system are improved. And meanwhile, additional visual navigation equipment is not needed, so that the system complexity and cost are reduced.

A guide for placing a landmark(s) in a patient tissue comprises a central portion defining at least one guiding bore configured to receive and guide a tool for placing the at least one landmark in the patient tissue. Extended portions project away from the central portion, each of the extended portions having respective lower base surfaces contoured to mate with the patient tissue, at least the respective lower base surfaces being customized as a function of preoperative imaging of the patient tissue, whereby the guide is specific to the patient.

The application discloses a risk prediction method for subsidence of an intervertebral fusion cage after lumbar interbody fusion, and comprises the following steps: acquiring preoperative image data and postoperative image data within one week after operation of a target patient, wherein the preoperative image data and the postoperative image data comprise multiple image features reflecting local anatomical morphology, sagittal balance and bone conditions of the spine; inputting the multiple image features into a pre-trained subsidence risk prediction model to obtain a risk probability of the target patient suffering from the subsidence of the intervertebral fusion cage; wherein the subsidence risk prediction model is obtained by screening a core prediction feature set through a "funnel type" feature screening process of fusing preoperative native anatomical parameters and postoperative early image parameters, and by training the core prediction feature set by using a machine learning algorithm based on a historical patient data set containing subsidence samples and non-subsidence samples; and the application reduces the occurrence of postoperative complications.

The invention relates to a calibration method, device and equipment, a medium and a program product. A first part of the image acquisition equipment is provided with a first tool, the fixing position of the first tool and the relative position of the rotation center of the image acquisition equipment are not changed, and a second tool is arranged in the imaging range of the image acquisition equipment. Based on a marker used for calibrating the position of the second tool and a marker used for imaging under the image acquisition equipment in the second tool; the method comprises the following steps: acquiring a conversion relation between a preoperative image coordinate system and a calibration navigator coordinate system; and obtaining a calibration relation between the preoperative image coordinate system and a first tool coordinate system on the image acquisition equipment based on the first tool and a conversion relation between the preoperative image coordinate system and a calibration navigator coordinate system. By adopting the method, the registration efficiency of multiple medical images can be improved.

The invention discloses a PCI (Peripheral Component Interconnect) drug balloon postoperative effect prediction method, device and equipment based on a multi-modal image and a storage medium. The method comprises the following steps: firstly, collecting historical preoperative digital subtraction angiography (DSA) images of a plurality of patients, corresponding intracavitary image data and postoperative effect label data; further extracting vascular morphological parameters of a target lesion segment from the DSA image, extracting plaque microstructure features from the intracavity image, combining the vascular morphological parameters, the plaque microstructure features, clinical data of a patient and parameters of a drug balloon instrument into an input feature vector, and taking postoperative effect label data as a regression target; training an ensemble learning regression model; and finally, for a to-be-evaluated patient, acquiring a preoperative DSA image and an intracavity image, extracting features in the same way, inputting the features into the trained model, and outputting an immediate and / or long-term vasodilation gain prediction value of the patient after the PCI drug balloon operation so as to improve objectivity and reliability of a DCB preoperative decision.

The application relates to the technical field of image processing, and provides a preoperative image registration processing method, device, system and storage medium, which can provide navigation information for intraoperative navigation in the scene of preoperative image guided puncture and lack of intraoperative real-time scanning image. According to each group in the respiratory phase sequence from the respiratory phase change of the preoperative image to the intraoperative respiratory phase, the image statistical shape model and the deformation field statistical shape model are determined according to the two respiratory phases of the group; the image weight of the group is obtained according to the image statistical shape model of the group and the image corresponding to the preceding respiratory phase; the weight proportion coefficient target value of the group is obtained based on the joint of the image statistical shape model and the deformation field statistical shape model of the group, and then the deformation field weight of the group is obtained in combination with the image weight; the deformation field of the group is obtained according to the deformation field weight and the deformation field statistical shape model of the group; and the preoperative image is registered and deformed according to the deformation fields of the groups.

This invention relates to a markerless real-time registration and tracking method and system for mandibular surgery. The method includes the following steps: the patient performs multiple opening and closing movements of the upper and lower jaws while the robotic arm is fixed, and the system synchronously records the changes in the angles of each joint of the robotic arm; based on the forward kinematics of the robotic arm, the continuous motion trajectory of the distal end of the jaw in space is calculated to form an actual jawbone trajectory point cloud; a preoperative image model is formed using the actual jawbone trajectory point cloud, a simulated jawbone trajectory is constructed in the preoperative image model, and the actual trajectory is aligned using the ICP registration algorithm to achieve automatic registration; the system establishes a mapping between angle changes and the spatial posture of the jawbone to achieve dynamic tracking and visual navigation during surgery. This invention solves the problems of large errors, high complexity, many limitations, and low efficiency in preoperative image data registration technology during maxillofacial surgery, avoids the need for preoperative implantation of markers, and achieves high-precision registration, high automation, and strong real-time performance under natural conditions.

The application provides an intraoperative low-field magnetic resonance image processing method, and belongs to the technical field of intraoperative magnetic resonance, and comprises the following steps: after preoperative information is preprocessed, the preoperative information is input into a parameter recommendation model, and a scanning parameter recommendation combination is output; sequence acquisition magnetic resonance imaging signals are constructed to obtain intraoperative images; a spliced image of the preoperative image and a first frame of the intraoperative image is input into an anatomical change prediction model to output a significant displacement area; the preoperative image and the intraoperative image are input into a feature mapping model to output a matching feature vector pair; the significant displacement area is used as a constraint to initialize registration parameters; the registration model is optimized; parameters are optimized; and the significant displacement area and other areas are given differentiated weights to complete registration, and an intraoperative image after registration is output. The method has the beneficial effects that: the scanning efficiency and scene adaptability are improved by recommending scanning parameters based on preoperative information, and the image quality is guaranteed; and prior information is provided for dynamic registration by predicting anatomical changes, and the adaptability of registration to intraoperative anatomical changes is improved.

The application discloses a heart soft tissue model correction method and device based on a physical information neural network, and comprises the following steps: acquiring preoperative images to construct a personalized heart biomechanical model and an active motion priori library; constructing a double-flow coupled physical information neural network, which comprises an active deformation prediction branch for predicting heart autonomous beats and a passive residual correction branch for correcting deformations caused by external contact forces; defining a loss function comprising a data matching loss and a biomechanical control equation loss, and training the network; receiving in-surgery collected cardiac phase data and sparse surface displacement data, inputting the data into the trained network, and outputting a global three-dimensional deformation field to update a preoperative three-dimensional model. The application effectively decouples the active and passive mixed deformation of the heart through a double-flow coupled architecture, and can infer the full-field volume deformation by only using sparse surface observation points by embedding physical control equations as strong constraints, thereby overcoming the data sparsity problem.

The invention relates to a flexible needle three-dimensional path planning method for multi-target puncture, and the method comprises the steps: obtaining a preoperative image, building a three-dimensional environment model through the preoperative image, and enabling the three-dimensional environment model to be used for recognizing target coordinates and marking obstacles; establishing a three-dimensional flexible needle kinematics model under the puncture base coordinates for determining the reachable domain section circle radius in the projection depth of the target spot along the needle axis; performing attitude judgment on the target spot by using the reachable domain cross section circle radius, defining a center deviation ratio, obtaining an average target spot centrality screening cost, and generating an effective candidate attitude in combination with an obstacle intrusion screening cost; screening effective candidate attitudes, determining a local coordinate system based on a unit needle inserting direction vector in the screened effective candidate attitudes in combination with a first target spot in the target spot access sequence, and performing path planning on a two-dimensional plane of the local coordinate system by adopting a fast extension random tree path planning algorithm to obtain a first path; and executing a retraction-rotation-repropulsion strategy to generate an optimal scheme.

The invention discloses a multi-modal image analysis method and system for cancer diagnosis, and the method comprises the following steps: 1, repairing an original projection sequence through a pre-trained self-supervised U-Net network, and generating a repaired projection sequence; 2, carrying out multi-modal image space-time alignment and metabolic dynamics modeling based on the restored projection sequence and the preoperative image, and generating a registration image and an intraoperative metabolism distribution diagram; 3, generating an initial boundary diagram with a biological reasonable topological structure and a corresponding confidence map and uncertainty map; step 4, positioning a high-uncertainty region by integrating the uncertainty map, and feeding back to the repair process in the step 1 and the joint optimization network in the step 3 for targeted adjustment until the uncertainty meets a preset condition; and 5, carrying out topology integrity verification on the initial boundary diagram, and generating a diagnosis report containing spatial positioning excision suggestions and confidence level grading.

The invention provides a glioma image segmentation method and system based on multi-feature fusion, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the preprocessing of multi-modal image data, and obtaining the spatial alignment multi-modal image data; based on the spatially aligned multi-modal image data, respectively extracting texture and metabolism characteristics of a preoperative image, gray gradient and morphological characteristics of an intra-operative cone beam CT image and micro texture characteristics of an intra-operative optical coherence tomography image; fusing the morphological characteristics of the intra-operative cone beam CT image and the microscopic texture characteristics of the intra-operative optical coherence tomography image, and constructing a multi-modal spatial characteristic field; based on the multi-modal spatial feature field, determining a spatial assessment origin, a main assessment axis and an auxiliary assessment axis, and defining a dynamic spatial assessment sector accordingly; and arranging a series of spatial feature observation points in the dynamic space evaluation sector. According to the method, feature reliability quantification can be realized, so that adaptive precise feature fusion is completed, and the glioma subregion segmentation precision is improved.

The application provides a training method of a CT and DSA real-time automatic fusion registration system for structural heart disease intervention and a storage medium, and comprises the following steps: establishing a registration system data set; establishing an initial registration system; training the initial registration system based on the registration system data set to obtain a trained CT and DSA intraoperative registration system; inputting preoperative CT image data and real-time DSA image data of a to-be-identified person into the trained CT and DSA intraoperative registration system to obtain real-time fusion navigation images and intraoperative risk graphical prompt information. The application can realize dynamic registration across modalities and time points, provide higher-precision intraoperative navigation support for structural heart disease intervention, improve the safety of surgery, and reduce radiation exposure.

The invention discloses a multi-modal image positioning and calibration analysis method and system based on a magnetic induction array, relates to the technical field of data analysis, and provides a magnetic induction array formed by magnetic sensors. Preoperative images are collected, a three-dimensional model of an operative area is constructed based on the images, a stimulation target area and a navigation path are planned, and a preoperative planning point set is generated; a datum point is selected, rigid registration with an actual datum point in the operation is completed, and an initial coordinate mapping relation is established; based on the boundary of a contact operation target area, a probe magnetic excitation coil excites a magnetic field, an array sensor collects three-axis magnetic field data according to a preset sampling period, and an intraoperative actual measurement point set is generated; inputting an actual measurement point set acquired by a sensor into an LM solver, and outputting probe coordinates in real time; and performing coordinate correction based on a deformation compensation mechanism, and performing real-time calibration by the system according to the corrected coordinates. According to the method, the non-rigid deformation deviation of the tissue in the operation can be effectively corrected by combining an affine transformation matrix and a least square optimized deformation compensation mechanism.

The invention discloses a PCI drug balloon postoperative effect prediction method, device and equipment based on coronary DSA and plaque property judgment and a storage medium. The PCI drug balloon postoperative effect prediction method comprises the steps that firstly, a PCI preoperative DSA image sequence of a patient to be evaluated is obtained; reconstructing a three-dimensional blood vessel center line of the target lesion segment based on the DSA image sequence, calculating lumen geometric features, inputting the lumen geometric features into a pre-trained plaque property judgment model, and outputting a plaque property judgment result of the target lesion; further combining a plaque property judgment result, DSA-derived vascular morphological parameters, clinical information of a patient and drug balloon instrument parameters to construct a comprehensive feature set; and finally, inputting the comprehensive feature set into a pre-trained regression prediction model, and outputting a PCI drug balloon postoperative prediction effect. According to the method, the plaque type is inversed through the geometrical characteristics of the lumen, the defect that conventional DSA evaluation lacks plaque histological information is overcome, and the reliability of drug balloon postoperative effect prediction is improved.

A surgical robotic system and method are provided for preoperative planning and precise navigation during minimally invasive surgical procedures. The system integrates various navigation technologies, such as electromagnetic and optical tracking, to monitor the position and orientation of robotic arms and surgical instruments in real time. A 3D model of the patient's anatomy, generated from preoperative imaging, is used to develop a preoperative plan, which includes critical structures and planned trajectories. During surgery, the system continuously registers the instruments' positions with the 3D model, compensating for tissue movement and deformation. This ensures accurate execution of the surgical plan, enhancing precision and control. The system provides real-time feedback through visual, haptic, and / or auditory cues, aiding the surgeon in navigating and manipulating the instruments effectively.

This invention discloses a robot-assisted electrocoagulation hemostasis system, comprising: an image processing module for acquiring preoperative and intraoperative three-dimensional images of the patient's affected area and performing registration; an optical tracking module for acquiring the poses of the patient's optical array, the registered optical array, and the tool's optical array; a navigation registration module for performing navigation registration based on the registration and the poses of each optical array to obtain the positional relationship between the preoperative image and the patient's optical array; an image processing module for acquiring images obtained by endoscopy, identifying and acquiring bleeding points and their corresponding bleeding times; and a planning and execution module for planning the target pose of the robotic arm and the electrocoagulation tool mounted at its end based on telecentric fixed point constraints, and executing accordingly. This invention, through automatic robot navigation control, quickly and accurately guides the system to the bleeding point, reducing the surgeon's workload and avoiding efficiency issues caused by unfamiliar human operation.

The invention relates to the technical field of medical clinic, in particular to a deep learning-based lung tumor ablation intelligent evaluation regulation and control method and a deep learning-based lung tumor ablation intelligent evaluation regulation and control system. The method comprises the following steps that a tumor area in a preoperative CT image and an ablation area in an intraoperative CT image are obtained, and a tumor mask and an ablation mask are generated respectively after the preoperative CT image and the intraoperative CT image are subjected to spatial registration; the coverage ratio of the volume of the ablation mask to the volume of the tumor mask is calculated, and the mass center offset distance of the tumor mask and the ablation mask is calculated; dividing the centroid offset distance by the coverage ratio to obtain an offset index; and identifying each risk structure in the ablation mask boundary expansion region, and measuring the nearest distance from each risk structure to the ablation mask boundary. According to the method, a quantitative mechanism between dynamic geometric behaviors and anatomical risks in the ablation process is constructed, an intelligent model capable of predicting complication risks is formed based on the quantitative mechanism, and the accuracy of microwave ablation is improved.

The application relates to an ablation evaluation method, an ablation evaluation system and a storage medium. The method comprises the following steps: determining a lesion area from a preoperative image of a target object, and determining an ablation area from a postoperative ablation image of the target object; performing difference analysis according to the lesion area and the ablation area to determine an ablation rate; the ablation rate represents the ablation degree of the lesion; inputting related information of the lesion corresponding to the lesion area and the ablation rate into a preset recurrence prediction model to perform evaluation, and obtaining an ablation evaluation result; the ablation evaluation result comprises recurrence information and / or supplementary ablation reference information determined based on the recurrence information. In one aspect, the ablation effect is evaluated based on the lesion area and the ablation area obtained immediately after the operation, and supplementary ablation is performed in time, so that the ablation effect is improved, and the number of ablation times is reduced. In another aspect, the related information of the lesion and the ablation rate are evaluated and analyzed based on a recurrence prediction model, so that more accurate evaluation results can be obtained, and the accuracy of recurrence prediction is improved.

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

This invention relates to the field of biometric recognition software and image pattern analysis technology, and discloses an automatic extraction method for preoperative image features of uterine fibroids using focused ultrasound ablation for re-pregnancy assessment. The method includes: acquiring image voxel data of the target entity and calculating its geometric center; traversing candidate anchor points in a three-dimensional neighborhood space and identifying gradient maxima to determine the optimal sampling origin; emitting a spatial sampling vector from the optimal sampling origin to establish a radial sampling field; calculating the gradient and energy characteristics at interface attribute transitions; and fusing them after noise component stripping to generate a descriptor vector. This invention utilizes dynamic optimization of the sampling origin to achieve orthogonal alignment between the sampling path and the tissue interface, suppressing non-pathological diffusion of signal steps at the mechanistic level, and improving the accuracy and uniqueness of biometric descriptors in representing the physical integrity of tissues.

This invention belongs to the field of medical device and medical image processing technology, and discloses a preoperative planning and guidance system for spinal fusion surgery based on three-dimensional image modeling. The system includes a medical image access terminal, a planning server, an intraoperative positioning workstation, a patient reference frame, instrument marking components, and a record management module. The planning server performs voxel spacing unification and coordinate normalization on preoperative images, reconstructs the target fusion segment model, and obtains a feasible insertion domain based on the difference between the candidate domain and the forbidden envelope domain. It jointly solves for screw and fusion device parameters and connecting component paths. During surgery, it calculates the entry point deviation, angle deviation, and depth deviation at a fixed sampling frequency, and triggers local re-registration and local replanning of affected segments using dual thresholds, achieving continuous guidance updates and process traceability.

The invention provides an intervention consumable advancing control method and system, and the method comprises the steps: planning a first path of an intervention consumable in a vascular intervention operation in a preoperative image, the first path comprising a starting point and an ending point of an advancing path, a central line of a passing blood vessel, the diameter of the blood vessel, a bifurcation point of the blood vessel, and a vascular lesion position; registering a first path in the preoperative image in the intraoperative image to obtain a planned second path displayed in the intraoperative image displayed in real time; in the advancing process, advancing information of the intervention consumable is obtained; automatically controlling the interventional consumable to advance along a second path based on the advancing information of the interventional consumable; the advancing information of the interventional consumables comprises at least one of the following items: the deviation angle of the interventional consumables, the advancing speed of the interventional consumables, the advancing direction of the interventional consumables and the arriving position of the interventional ends of the interventional consumables. Based on the technical scheme provided by the invention, the safety of automatically controlling the intervention consumables to advance in the operation can be improved.