14 results about "Plan treatment" patented technology

A system to treat a patient comprises a user interface that allows a physician to view an image of tissue to be treated in order to develop a treatment plan to resect tissue with a predefined removal profile. The image may comprise a plurality of images, and the planned treatment is shown on the images. The treatment probe may comprise an anchor, and the image shown on the screen may have a reference image marker shown on the screen corresponding to the anchor. The planned tissue removal profile can be displayed and scaled to the image of the target tissue of an organ such as the prostate, and the physician can adjust the treatment profile based on the scaled images to provide a treatment profile in three dimensions. The images shown on the display may comprise segmented images of the patient with treatment plan overlaid on the images.

This invention provides a method for planning treatment pathways in energy-focused surgery for complex lesion morphologies, belonging to the field of medical technology. The method includes: acquiring geometric feature data of the complex lesion morphology; matching a predetermined path traversal function based on the geometric feature data, wherein the path traversal function covers all surgical operation points of the complex lesion morphology; obtaining treatment paths for the energy-focused surgical operation points based on a set speed or acceleration according to the path traversal function; and generating a path set based on the treatment paths to determine the treatment path. This invention also discloses a surgical treatment pathway planning device and corresponding electronic equipment for complex lesion morphologies.

The control circuitry is able to access 400 patient-specific multidimensional information and subsequently, at least in part, automatically determine 406 supplementary boundaries for at least a portion of the patient (such as treatment targets and / or one or more organs at risk) based on the multidimensional information. The latter may include determining margins added to the boundaries of at least a portion of the patient. The control circuitry can then, for example, determine 407 the planned treatment volume based at least in part on the supplementary boundaries. These teachings will then allow for the optimization 408 of a patient-specific, radiotherapy-based treatment plan, for example, based on various motion dimensions derived at least in part from the aforementioned multidimensional information.

To plan tumor treating field (TTField) therapy, a model of the patient's head is typically used to determine where the transducer arrays will be positioned during treatment. The accuracy of this model depends largely on accurate segmentation of the MRI images. The quality of the segmentation can be improved by presenting it to a previously trained machine learning system. The machine learning system generates a quality score for the segmentation. Revisions to the segmentation are accepted, and the machine learning system scores the revised segmentation. The quality score is optionally used to determine which segmentation provides better results by running simulations of the model corresponding to each segmentation for multiple different transducer array layouts.

The present disclosure provides methods for planning, predictively modeling, and monitoring treatments for lung disease. In some embodiments, a method for planning treatment of a patient having a lung disease includes receiving patient data including computed tomography (CT) data of a lung of the patient. The method may include generating a set of lung metrics by inputting the patient data into a first machine learning algorithm. The method may also include predicting a response of the patient to treatment of the lung disease by inputting the set of lung metrics into a second machine learning algorithm. The method may further include evaluating whether the patient is a candidate for the treatment of the pulmonary disease based on the predicted response. The method may further include evaluating whether the patient is benefited after treatment and whether additional treatment is needed.

An intelligent system for optimizing radiotherapy for personalized radiotherapy, consisting of: • a genome data acquisition module configured to acquire and process a patient's genomic, molecular, and biomarker information; • a multimodal medical imaging module configured to acquire medical image data from one or more imaging modalities; • an image processing and automatic segmentation module configured to preprocess the acquired medical images, register multimodal images, segment tumors and organs at risk, and extract quantitative image features; • an artificial intelligence and data integration module configured to integrate genome data, imaging data, radiomic features, and clinical information to generate a patient-specific predictive model;• A treatment planning and dose optimization module configured to automatically generate and optimize a personalized treatment plan based on the integrated patient-specific model; • A digital twin and adaptive therapy module configured to simulate the patient-specific treatment response and continuously adjust the treatment plan during therapy; • A treatment monitoring and outcome prediction module configured to predict treatment response, disease progression, and radiation-induced toxicity based on longitudinal patient data; • A clinical decision support module configured to generate personalized treatment recommendations and support clinical decision-making;• A communication and data management module configured for the secure exchange, synchronization, and storage of clinical, imaging, genomic, and treatment-related information; and • An autonomous learning and systems management module configured to continuously improve predictive models and treatment optimization algorithms based on collected treatment outcomes, with the modules working together to generate, optimize, monitor, and continuously adapt personalized radiotherapy based on integrated genomic and multimodal medical imaging information.

Methods for planning, predictive modeling, and monitoring therapies for pulmonary diseases are provided. In some embodiments, a method for planning a treatment for a patient having a pulmonary disease includes receiving patient data including computed tomography (CT) data of a lung of the patient. The method can include generating a set of lung metrics by inputting the patient data into a first machine learning algorithm. The method can also include predicting a response of the patient to treatment for the pulmonary disease by inputting the set of lung metrics into a second machine learning algorithm. The method can further include evaluating whether the patient is a candidate for the treatment for the pulmonary disease, based on the predicted response. The method can further include evaluating whether the patient has benefited following treatment and whether additional treatment is warranted.

The present disclosure provides a method of automatically determining orthodontic treatment plan scores and performance scores using three-dimensional (3D) virtual models. The associated computer implemented software system includes an input module configured to obtain, in a computer-implemented software system, at least one of virtual stage models or dental mesh models. Embodiments may also include a processing module configured to process the virtual stage models and / or the dental mesh models to generate one or more composite stage models. Additionally, a measurement module is configured to determine virtual measurement values from named objects within virtual stage models and / or composite stage models. Embodiments of the present disclosure may also include a module configured to apply index classifiers to determined virtual measurement values and calculate total performance scores and standardized performance scores. An output module is included to output, from the computer-implemented software system, planned treatment scores and treatment performance scores.

The present disclosure provides a method of automatically determining orthodontic treatment plan scores and performance scores using three-dimensional (3D) virtual models. The associated computer implemented software system includes an input module configured to obtain, in a computer-implemented software system, at least one of virtual stage models or dental mesh models. Embodiments may also include a processing module configured to process the virtual stage models and / or the dental mesh models to generate one or more composite stage models. Additionally, a measurement module is configured to determine virtual measurement values from named objects within virtual stage models and / or composite stage models. Embodiments of the present disclosure may also include a module configured to apply index classifiers to determined virtual measurement values and calculate total performance scores and standardized performance scores. An output module is included to output, from the computer-implemented software system, planned treatment scores and treatment performance scores.

The invention relates to the technical field of medical beauty medical instruments, and particularly discloses an intelligent treatment system based on path planning, which comprises a treatment control module used for generating and storing treatment data in real time in a teaching process; the path planning module is used for generating and storing treatment path data according to the teaching information collected in real time; the treatment device is used for outputting corresponding treatment energy according to the treatment data; the path planning module comprises an execution device, the tail end of the execution device is connected with the treatment device, and the execution device drives the treatment device to move according to a planned treatment path according to the treatment path data. The purpose that the intelligent treatment system automatically plans the treatment path through one-time teaching and automatically completes treatment according to the planned treatment path is achieved. And the labor intensity of doctors during treatment is greatly reduced. The labor intensity of doctors during treatment is greatly relieved, and the automation degree and the intelligence degree of precise treatment are improved.

The user interface presents a 3D view of the tissue and treatment plan. The 3D view includes multiple transverse images arranged along one or more longitudinal images. The user adjusts the treatment profile through inputs to the user interface, and the updated treatment profile is shown in the other views. The user overlays the treatment profile on the 3D view and one or more of zooming, panning, or rotating the 3D view, and the treatment profile moves with and maintains alignment with the 3D view. In some embodiments, the 3D treatment plan is generated according to multiple angles between the treatment probe and one or more tissue structures. AI algorithms can be used to identify tissue structures and plan treatment angles and energy delivery according to the tissue structures, thereby providing a more customized treatment.

The user interface presents a 3D view of the tissue and a treatment plan. The 3D view includes a plurality of lateral images arranged along one or more longitudinal images. The user adjusts the treatment profile through input to the user interface, and the updated treatment profile is shown on other views. The user performs one or more operations of scaling, translation, or rotation on the 3D view (where the treatment profile is superimposed on the 3D view), and the treatment profile moves with the 3D view to maintain registration with the 3D view. In some embodiments, a 3D treatment plan is generated based on multiple angles between a treatment probe and one or more tissue structures. An AI algorithm may be used to identify tissue structures and to plan treatment angles and energy delivery according to the tissue structures, thereby providing more personalized treatment.

Methods for planning, predictive modeling, and monitoring therapies for pulmonary diseases are provided. In some embodiments, a method for planning a treatment for a patient having a pulmonary disease includes receiving patient data including computed tomography (CT) data of a lung of the patient. The method can include generating a set of lung metrics by inputting the patient data into a first machine learning algorithm. The method can also include predicting a response of the patient to treatment for the pulmonary disease by inputting the set of lung metrics into a second machine learning algorithm. The method can further include evaluating whether the patient is a candidate for the treatment for the pulmonary disease, based on the predicted response. The method can further include evaluating whether the patient has benefited following treatment and whether additional treatment is warranted.

This invention discloses a pulse sequence-based method and system for controlling the voltage of electroporation ablation, involving key technologies such as intelligent target region identification, electric field modeling, dose control, real-time monitoring, and feedback. AI technology is used to accurately identify tumors and plan treatment, optimizing the electric field distribution to ensure precise application of the electroporation pulses. Closed-loop control dynamically adjusts pulse parameters, and sensor fusion technology provides treatment efficacy evaluation. The system integrates data analysis and machine learning to intelligently adjust treatment strategies, enabling personalized treatment and complication prediction, thereby improving treatment effectiveness and patient safety. This invention is applicable to precision tumor treatment, ensuring efficacy while protecting healthy tissues.