8496results about "Mechanical/radiation/invasive therapies" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels / Deep learning algorithms of the AI system distinguish different anatomical objects from the images.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

A modular surgical system for use in a surgical procedure is disclosed. The modular surgical system includes a control module including a power supply and surgical modules including a first surgical module and a second surgical module. The first surgical module is arranged in a stack configuration with the control module and the second surgical module. The power supply is configured to output power to the first surgical module and the second surgical module. A power backplane is configured to deliver the power to the first surgical module and the second surgical module through the first surgical module. A control circuit is configured to adaptively adjust power allocations to the first surgical module and the second surgical module.

Systems, methods, and instrumentalities are disclosed for displaying a magnitude and location of a resulting tissue tension from an applied force to an instrument and a resulting tissue reaction. A surgical stapling device for monitoring and adapting to tissue tension during a surgical stapling procedure may measure strain on at least one component of the surgical stapling device resultant from one or more of an opposed force applied by a user and a force resisted by tissue associated with the tissue tension. The device may determine a location of the tissue tension based on historical surgical data, the opposed force, and the resisted force. The device may highlight the location of the tissue tension to aid in real-time surgical decision-making.

The invention relates to the technical field of medical rehabilitation, in particular to an artificial intelligence-based orthopedic full-cycle rehabilitation management system, which comprises a rehabilitation demand evaluation module, a three-dimensional pathological modeling module, a multi-modal training generation module, a rehabilitation effect prediction module, a dynamic parameter adjustment module and a full-cycle management module. Multi-dimensional data such as bone mineral density, joint motion range, gait mechanics and psychological state of a patient are collected, an artificial intelligence algorithm is combined for analysis, a personalized rehabilitation scheme is generated, and rehabilitation parameters are predicted and adjusted in real time. The system can dynamically correct the training intensity, the nutritional supplement dosage, the psychological intervention mode and the rehabilitation schedule, and the safety and reliability of data are guaranteed through the block chain technology; according to the system, biomechanics, nutriology, psychology and environmental factors are comprehensively fused, the system has strong adaptive ability and full-period management ability, real-time optimization can be performed according to the rehabilitation process of a patient, and the rehabilitation effect of the orthopedics department and the satisfaction degree of the patient are remarkably improved.

The invention discloses a multi-modal large model assisted laparoscope soft tissue registration surgical navigation method and system. The method comprises the following steps: acquiring a preoperative CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) image and an intraoperative laparoscope image, and converting to obtain a multi-modal feature sequence; inputting the obtained multi-modal feature sequence into a large model for fusion to obtain a cross-modal matching matrix; random sampling is carried out by using an RANSAC random sampling consensus algorithm, an error between corresponding point sets of two data is measured by using an Euclidean distance, an analytical solution is obtained through SVD decomposition, and a feature matching error is introduced to obtain an optimal rigid body transformation pose; performing non-rigid deformation local fitting adjustment on the optimal rigid body transformation pose residual error by adopting a parameterized deformation model in combination with learning prediction deformation, and performing loop execution to obtain a soft tissue organ real-time registration image; automatic, high-timeliness and high-precision real-time registration of soft tissues is realized under the multi-mode and multi-variable conditions, and reliable navigation and positioning support is ensured to be provided in a complex operation environment.

Surgical systems and related computer-implemented methods are provided, including during performance of a surgical procedure on a patient, receiving, at a first surgical system, a first dataflow from a second surgical system, the first dataflow including first data regarding a measured patient parameter that the first surgical system is configured to use in performing a function during the performance of the surgical procedure. The computer-implemented methods including determining that a trigger event occurred during the performance of the surgical procedure such that a sum of a first bandwidth of the first dataflow and a second bandwidth of a second dataflow exceeds an available bandwidth, and in response to determining that the trigger event occurred, and during the performance of the surgical procedure, adjusting at least one of the first and second dataflows such that the sum of the first and second bandwidths does not exceed the available bandwidth.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

The invention relates to the technical field of data processing, and discloses a preoperative risk comprehensive assessment method and system for breast cancer and a storage medium. The method comprises the following steps: collecting clinical information and carrying out digital processing; segmenting the multi-modal image by using deep learning; analyzing a pathological section based on the image features; carrying out multi-level fusion modeling to obtain a risk heat map; performing digital twinborn simulation in combination with a knowledge graph; and dynamically processing the follow-up data to form a risk adjustment mechanism. According to the method, accurate quantification and individualized treatment decision making of the preoperative risk of the breast cancer patient are realized, and the prediction accuracy is improved.

A system for administering transcranial neuromodulation (TNM) treatment with a medical device capable of delivering a TNM treatment to a patient including an electronic computing device has a processor, computer-readable memory, and a power supply, in communication with the TNM device. A patient interface is in communication with the electronic computing device and the TNM device. The electronic computing device is configured to: receive an initial TNM treatment protocol; communicate same to the TNM device to enable initial TNM treatment; receive patient input through the patient interface; receive an updated TNM treatment protocol based on patient input; and communicate updated TNM treatment protocol to the TNM device to enable updated TNM treatment of the patient.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.

The invention relates to the technical field of traditional Chinese medicine acupuncture and moxibustion teaching, in particular to a multi-view fused traditional Chinese medicine acupuncture and moxibustion visual teaching system which comprises a basic data construction module, an immersive environment building module, a multi-view interactive teaching module, a collaborative annotation conflict processing module, an annotation result auditing module and a model and data optimization module. Compared with the prior art, the traditional Chinese medicine acupuncture and moxibustion visual teaching method has the advantages that a new traditional Chinese medicine acupuncture and moxibustion visual teaching paradigm of data-driven cognition, interactive verification logic and intelligent optimization iteration is constructed through a closed loop of'problem definition-technical breakthrough-effect guidance ', space and modal limitations of traditional teaching are broken through, and the traditional Chinese medicine acupuncture and moxibustion visual teaching method is suitable for popularization and application. In addition, deep fusion of traditional Chinese medicine inheritance and modern medical education is achieved, and a reproducible digital solution is provided for standardized and precise acupuncture and moxibustion teaching.

The invention discloses an intelligent auxiliary acupuncture system based on the AR technology, and the system comprises the following modules: a multi-modal sensing module which is used for collecting a multi-modal sensing data set containing the surface information of a patient; the visual SLAM guiding module is used for outputting a relative pose between the camera and the skin of the patient; the three-dimensional reconstruction module is used for generating a three-dimensional skin grid model; the candidate acupoint generation module is used for generating a candidate acupoint set under the condition that the density threshold value and the minimum spacing are met; the stable matching module is used for outputting a stable matching result set; the trajectory planning module is used for binding the needle insertion trajectory to the three-dimensional skin grid model to form a needle insertion guide set; and the augmented reality rendering module is used for carrying out real-time visual rendering on the virtual acupuncture point target and the needle inserting track through an augmented reality display terminal. The invention constructs an intelligent acupuncture guide system which is oriented to a real clinical scene and has a real-time correction capability and a medical significance interpretation capability.

The invention provides an intelligent cooperative control method and system for a multi-mode phototherapy device, and the method comprises the steps: obtaining physiological parameters of a plurality of users to form a data set, and constructing a spectrum feedback model in combination with historical phototherapy data and environment data; on the basis of the model and historical phototherapy target data, a multi-mode spectrum parameter model is generated through a preset neural network, and cooperative control over multi-light-source equipment is achieved; physiological parameter changes of a target user are monitored in real time, and monitoring data are sent to an edge computing node; the edge node dynamically adjusts a spectral parameter combination scheme according to the monitoring data and a safety threshold, and generates a treatment effect evaluation index; and establishing a user spectrum feedback feature library based on the evaluation indexes, and continuously optimizing a spectrum feedback model through incremental learning. According to the invention, intelligent cooperative control of the multi-mode phototherapy equipment can be realized, the energy utilization efficiency of the phototherapy equipment is improved, the spectrum feedback difference influence among different users is reduced, and the accuracy and safety of the phototherapy effect are enhanced.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.

The invention discloses an anesthesia depth monitoring system and method based on multiple physiological parameters, and the system comprises a data collection unit which is used for obtaining a perfusion index value, an electrocardiogram signal, a brain wave signal, a blood pressure signal of a patient and the individual feature information of the patient; the signal processing unit is electrically connected to the data acquisition unit and used for processing the signals acquired by the data acquisition unit and calculating the perfusion index change rate, the heart rate variability, the anesthesia consciousness index and the blood pressure fluctuation rate; the parameter fusion unit is electrically connected to the signal processing unit and used for combining the parameters calculated by the signal processing unit with the individual feature information of the patient; the individualized correction unit is electrically connected to the parameter fusion unit and is used for adjusting the comprehensive anesthesia depth score generated by the parameter fusion unit; the method has the advantages that multi-dimensional physiological data and individual characteristics can be integrated, precise evaluation and individualized regulation and control of the anesthesia depth are realized, and the anesthesia safety is improved.

Systems and methods are disclosed for using vessel reactivity to guide diagnosis or treatment for cardiovascular disease. One method includes receiving a patient-specific vascular model of a patient's anatomy, including at least one vessel of the patient; determining, by measurement or estimation, a first vessel size at one or more locations of a vessel of the patient-specific vascular model at a first physiological state; determining a second vessel size at the one or more locations of the vessel of the patient-specific vascular model at a second physiological state using a simulation or learned information; comparing the first vessel size to the corresponding second vessel size; and estimating a characteristic of the vessel of the patient-specific vascular model based on the comparison.

The invention discloses a plastic effect simulation display method and system based on a 3D simulation technology, and relates to the technical field of medical information, and the method comprises the steps: collecting the facial data of a patient, and constructing a multi-level facial structure initial model; constructing a hybrid deformation model; individual characteristic parameters of a patient are extracted, and an individual biomechanical parameter set is generated through the biomechanical parameter prediction network; applying the personalized biomechanical parameter set to the mixed deformation model; operation input parameters of plastic surgery are received, and the deformation result of each layer of structure is calculated and displayed in real time based on a GPU parallel computing framework; constructing a tissue healing and recovery kinetic model, and generating appearance prediction data at different postoperative time points in combination with the deformation result; and performing three-dimensional visualization processing on the appearance prediction data. According to the method, CT / MRI, 3D structured light scanning and expression dynamic video sequences are creatively integrated, the limitation of traditional single-mode data modeling is broken through, and the soft tissue boundary recognition precision is remarkably improved.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence assisted intraoperative imaging method, which comprises the following steps: S1, preprocessing a multi-modal medical image, segmenting and recognizing an anatomical structure by a deep learning model according to the preprocessed image, measuring anatomical parameters based on a segmentation and recognition result, and generating an operation planning path by artificial intelligence according to the anatomical parameters; s2, collecting a C-shaped arm perspective image stream in real time, dynamically tracking space coordinates of a surgical instrument, comparing the position of the instrument with a surgical planned path, calculating offset, and when the offset is greater than an offset threshold, outputting correction guidance through an AR superposition layer; and S3, monitoring an image quality index in real time, dynamically adjusting exposure parameters through a reinforcement learning model, and when a metal implant is detected, switching a dual-energy-spectrum mode and executing an artifact suppression algorithm. According to the method, preoperative precise planning and intraoperative assistance are realized through artificial intelligence, the problems of poor image quality and high radiation risk are solved through technical optimization, and the method has important clinical application value.

An apparatus and method for generating clinical decision support is disclosed. The apparatus includes at least a processor and a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive user data, generate a fused feature vector correlating the user data to a plurality of clinical outcomes by training a plurality of deep neural networks (DNNs) to output a first set of feature vectors, a second set of feature vectors and a third set of feature vectors, fusing the first, second, and third set of features vectors to form the fused feature vector, generate a procedural output using the fused feature vector, and display the procedural output through a user interface.

Systems and methods are disclosed for simulation of occluded arteries and / or optimization of occlusion-based treatments. One method includes obtaining a patient-specific anatomic model of a patient's vasculature; obtaining an initial computational model of blood flow through the patient's vasculature based on the patient-specific anatomic model; obtaining a post-treatment computational model by modifying portions of the initial computational model based on an occlusion-based treatment; generating a pre-treatment blood flow characteristic using the initial computational model or computing a post-treatment blood flow using the post-treatment computational model; and outputting a representation of the pre-treatment blood flow characteristic or the post-treatment blood flow characteristic.

Disclosed herein are methods and systems to evaluate cost values for different radiotherapy treatment plans using external AI models. A method comprises presenting a user interface providing an interaction interface between a plurality of medical professionals communicating regarding a radiation therapy treatment of a patient during a tumor board meeting or a radiotherapy treatment planning process; receiving, from the interaction interface, a first input comprising a first patient attribute of the patient and a second input corresponding to the radiation therapy treatment of the patient; executing a machine learning language processing model using the first input and the second input to predict a task associated with generating a treatment plan for the patient; receiving, from the interaction interface, a third input; and when the third input does not correspond to the predicted task, presenting an indication of the predicted task.

The invention relates to the technical field of surgical intervention, in particular to a catheter system and method for neurosurgery surgical intervention, in the invention, through accurate path prediction, real-time monitoring and dynamic adjustment, the accuracy of catheter operation is remarkably improved, and through analysis of historical surgical data, information such as path, speed and angle in the surgical process is accurately extracted, so that the accuracy of catheter operation is improved. A more accurate neural structure position can be provided by timely adjusting the movement direction of the catheter, spatial positioning and form calculation, the spatial deviation between the current position of the catheter and a target neural structure is calculated in real time through a support vector machine algorithm, a path is adjusted according to a set threshold value, historical operation data is learned through a convolutional neural network, and the operation accuracy is improved. The catheter operation strategy is optimized, operation data and deviation are analyzed in real time, the operation precision is automatically adjusted, catheter operation in the operation process is more accurate, patient wounds are smaller, the recovery time is shorter, the treatment effect is better, and the recovery speed of patients is increased.

The invention discloses a man-machine cooperation type high-precision acupuncture method and system based on digital twinning, and relates to the technical field of acupuncture, and the method comprises the steps: obtaining user body surface image data, local subcutaneous tissue structure data, standard meridian model data and real-time electrophysiological data; generating a three-dimensional point cloud model based on the body surface image data, and mapping the standard meridian model data to the three-dimensional point cloud model to generate initial acupuncture point coordinates; dynamically correcting the initial acupuncture point coordinates based on the real-time electrophysiological data to obtain individualized acupuncture point coordinates; constructing a hierarchical tissue model containing neural distribution based on the local subcutaneous tissue structure data; the method has the advantages that dynamic acupuncture point correction is achieved by fusing three-dimensional point cloud modeling and real-time electrophysiological feedback, a precise acupuncture path is generated in combination with nerve activation probability prediction, real-time path adjustment is conducted based on physiological feedback, and the method has the advantages of improving acupuncture point positioning precision, achieving dynamic optimization of the acupuncture path and improving treatment safety.

The invention discloses a preoperative and postoperative full-process optimization nursing system for the thoracic surgery department, relates to the technical field of medical nursing, and aims to solve the problem that physical execution nursing and information management decision are separated in the prior art; and the nursing intervention is lack of real-time response and closed-loop regulation and control capability to the highly dynamic physiological state of the thoracic surgery patient in the perioperative period. The nursing system is characterized by comprising a multi-mode physiological and environmental perception layer, a dynamic nursing decision engine and a closed-loop physical intervention execution layer. Wherein the sensing layer is responsible for data acquisition, state model construction, risk prediction and optimal intervention planning by a decision engine, and the execution layer accurately implements intervention and feeds back. By the adoption of the technical scheme, individualized, dynamic and prospective management of perioperative nursing of the patient in the thoracic surgery department can be achieved, a refined, individualized and intelligent management means is provided, and the clinical value is remarkable.

The invention discloses a rehabilitation training method and system based on transcranial time domain interference electrical stimulation. Comprising a physiological signal acquisition module used for acquiring a multi-modal physiological signal of a user; the software processing module is used for determining a target stimulation target of a user based on the multi-modal physiological signal and generating an electrical stimulation parameter scheme of the target stimulation target; and the electrical stimulation module is used for performing transcranial time domain interference electrical stimulation on the user based on the electrical stimulation parameter scheme. According to the rehabilitation training method and system, the characteristics of the deep nuclei can be accurately regulated and controlled by using the transcranial time domain interference electrical stimulation technology, and the deep focus is accurately stimulated. Meanwhile, a more effective individualized stimulation scheme is provided for the user through the guidance of the multi-modal physiological signals. The system can be applied to dyskinesia rehabilitation in hospitals, communities and homes, and a non-invasive, convenient and efficient solution is provided.