520 results about "Therapy planning" patented technology

The invention relates to the field of navigation signal processing, and discloses a radiation therapy bolus dose feedback and tracking system based on image registration, the system comprises an image acquisition module, an image registration module, a bolus identification module, a dose recalculation module, a dose deviation analysis module and a feedback and adjustment module, space registration is carried out through an FBCT image acquired every day and an original plan CT, and the radiation therapy bolus dose feedback and tracking system based on image registration is obtained. The fitting state between the bolus and the skin is automatically identified, the position of an air gap is detected, and dose recalculation is carried out based on an actual structure. Through comparison with an original planned dose, the system can output a deviation index of a key dose parameter and provide visual feedback, if the deviation exceeds a set threshold value, a prompt is automatically sent out, and a radiotherapy technician and a physician are supported to carry out bolus adjustment or call an adaptive planning engine to reconstruct a treatment plan. Quantitative analysis of bolus fitting quality and closed-loop control of dose delivery consistency are realized, radiotherapy precision and safety are effectively improved, and the method is suitable for superficial radiotherapy scenes containing bolus design.

Systems and methods for generating a beam model for radiotherapy treatment planning are discussed. An exemplary system includes a memory to store a trained deep learning model, and a processor circuit to generate a beam model. The deep learning model can be trained to establish a relationship between machine scanning data and values of beam model parameters, and validated for accuracy. The processor circuit can receive machine scanning data indicative of a configuration or an operation status of the radiation therapy device, apply the machine scanning data to the trained deep learning model to determine values for the beam model parameters, and generate a beam model based on the determined values of the plurality of beam model parameters. The beam model may be provided to a user, or a treatment planning system.

The invention discloses an ultra-high dose rate radiotherapy plan optimization system based on compensator modulation, and relates to the technical field of radiotherapy. A data acquisition module is used for acquiring medical image data of a patient and receiving a clinical target; the clinical target comprises a target region prescription dose, an organ-endangering limit and a minimum dose rate threshold value required for triggering and maintaining a FLASH effect; and the FLASH biological effect evaluation module is used for calculating FLASH biological effective dose distribution corresponding to the physical dose distribution through an embedded biological effect model based on the input physical dose distribution and dose rate distribution. According to the method, the physical dose is converted into the accurate FLASH biological effective dose through the improved linear quadratic model embedded with the FLASH correction factor, the target tumor killing effect is guaranteed, normal tissue protection is enhanced with the help of the tissue differentiation correction factor, the industrial pain point that the physical dose reaches the standard but the biological effect does not reach the expectation is effectively solved, and the application prospect is wide. And the treatment plan better meets the clinical curative effect and safety core requirements.

The present disclosure provides a method for performing a treatment plan, wherein the method comprises: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan; receiving second patient data, wherein the second patient data includes a second patient identifier associated with the second patient and a second treatment plan; receiving first measurement data associated with a first performance level of the first treatment plan by the first patient; receiving second measurement data associated with a second performance level of the second treatment plan by the second patient; determining differential data, wherein the determining is based on a contrast of the first or the second measurement data or first or second patient data; and generating, based on the differential data, an instruction to modify an operating state of the treatment apparatus.

The invention discloses a multi-modal image registration processing method and system based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: calculating a mutual information loss value after registration through a mutual information loss method, optimizing a CNN model and UNet model parameters in combination with the total loss generated by the fusion of global and local deformation fields, and obtaining a registered sCT image. A CNN model and a UNet model are combined to generate global and local deformation fields, overall rigid transformation and local nonlinear deformation are effectively captured, the spatial alignment precision of sCT and reference CT is improved, model parameters are optimized through mutual information loss and total loss, the intensity distribution consistency is ensured, feature weights and treatment plan parameters are automatically adjusted through registration quality feedback, and the accuracy of the treatment plan is improved. The coverage precision and efficiency of the radiotherapy plan are remarkably improved, automatic and high-precision image registration and treatment plan optimization are realized, and the reliability and practicability of clinical image processing are enhanced.

The invention relates to the technical field of minimally invasive ablation, in particular to a liver tumor microwave ablation postoperative contraction deformation prediction method, which comprises the following steps: performing unified standardization processing on different periods of liver MRI images; accurately marking a preoperative liver tumor area, a postoperative ablation area and a postoperative liver tumor ghost area of the liver MRI image to obtain masks of the corresponding areas; designing a distance perception function as an attention parameter of different areas of the liver MRI image; constructing a multi-sequence distance guide complementary network model; selecting a loss function; selecting an elastic registration method to perform pre-operation and post-operation registration; and calculating an ablation safety boundary through the ablation area mask and the adjusted tumor mask. The liver tumor microwave ablation postoperative curative effect evaluation accuracy can be effectively improved, and a foundation is laid for formulating a subsequent treatment plan of a patient.

Devices, systems, and methods for predicting palatal geometry are provided. In some embodiments, a system for predicting an anatomy of a patient's palate includes one or more processors and a memory operably coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations including accessing a first digital representation including an initial geometry of soft tissue corresponding to a patient's palate, determining an initial geometry of hard tissue of the patient's palate, predicting a change in the hard tissue during a treatment, using the predicted change in the hard tissue to predict a change in the soft tissue during the treatment, and outputting a second digital representation including a predicted geometry of the patient's palate at a future treatment stage of a treatment plan, based on the predicted change in the soft tissue.

A computer-implemented method including receiving, at a computing device, a first treatment plan designed to treat a sexual performance health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the sexual performance health issue, enable the user to perform an exercise at different exertion levels. While the user uses a treatment apparatus to perform the first treatment plan for the user, the computing device receives cardiovascular data from one or more sensors configured to measure the cardiovascular data associated with the user, and transmits the cardiovascular data. A machine learning model is used to generate a second treatment plan that modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the cardiovascular data, and the cardiovascular health issue of the user. The method includes receiving the second treatment plan.

The invention relates to the field of radiotherapy plan design, and discloses a radiotherapy parameter intelligent recommendation system based on disease species, target area volume and dose characteristics, and the system comprises the steps: constructing a response surface model of machine physical parameters and plan quality indexes, generating a Pareto optimal solution set through a non-dominated sorting genetic algorithm, and building an optimization rule base; extracting anatomical and geometric features of historical cases, and training a CART decision tree to establish a mapping relation between the features and clinical scenes; analyzing to-be-planned case features, matching a target scene through the decision tree, and retrieving an optimal machine physical parameter combination in the rule base according to the user preference weight. According to the method, the width, the screw pitch and the modulation factor of the collimator can be automatically recommended, the problem that manual trial and error efficiency is low and depends on experience is solved, and balance between treatment plan quality and machine execution efficiency is achieved.

A medical imaging system including an optical identifier camera for imaging a medical device, a medical device packaging, a patient, or a technician. The system further includes an imaging probe for imaging of a target location, and a console with optical identifier logic to extract identifier markers or characteristics from the image of the medical device, packaging, patient or technician. A parameter logic determines the medical imaging parameters based on the identifiers or characteristics, and a medical imaging logic captures medical images of the medical device. The system enables efficient and accurate imaging of medical devices on the specific patient, by a specific technician, enhancing diagnostic capabilities and treatment planning in medical parameters.

Systems and methods are provided for modeling and for providing a graphical representation of tissue heating and electric field distributions for medical treatment devices that apply electrical treatment energy through one or a plurality of electrodes. In embodiments, methods comprise: providing one or more parameters of a treatment protocol for delivering one or more electrical pulses to tissue through a plurality of electrodes; modeling electric and heat distribution in the tissue based on the parameters; and displaying a graphical representation of the modeled electric and heat distribution. In another embodiment, a treatment planning module is adapted to generate an estimated target ablation zone based on a combination of one or more parameters for an irreversible electroporation protocol and one or more tissue-specific conductivity parameters.

The invention discloses a close-range radiotherapy plan optimization method and device based on a cumulative dose target. The method comprises the following steps: S1, importing current treatment data; s2, leading in a preorder treatment image and performing deformation registration to the current image; s3, mapping the preorder treatment dose to the current treatment image to serve as the background dose of the current treatment; s4, converting the dose data mapped to the current treatment image by the preorder treatment into a bioequivalent dose; s5, on the basis of the sketching data of the current treatment image and with reference to the clinical endangered organ dose limit value requirement, key area generation is carried out on each endangered organ; s6, based on the preorder treatment dosage, generating a corresponding organ-endangered high-risk dosage area and a target area insufficient area according to each organ-endangered dosage limit value and the target area prescription dosage; and S7, optimizing the treatment plan, and optimizing the treatment plan by taking the preorder treated dose and the sum of the planned dose to be optimized at the time to meet the clinical requirement as a target.

A computer-implemented method is disclosed. The method includes receiving, from one or more sensors, one or more measurements associated with a user, where the one or more measurements are received while the user performs a treatment plan, and an electromechanical machine is configured to be manipulated by the user while the user is performing the treatment plan. The method also includes determining, via one or more machine learning models, one or more content items to present to the user, where the determining is based on the one or more measurements and one or more characteristics of the user. A presentation of the one or more content items is provided on an interface while the user performs the treatment plan using the electromechanical machine.

A method of treating a patient's dentition may include generating, from a first 3D model of a first physical impression of a first arch of a patient and a second 3D model of a second physical impression of a first arch of the patient, a reconstructed 3D model of the first arch of the patient by incorporating one or more portions of the first 3D model with one or more portions of the second 3D model and creating a treatment plan for the patient's dentition using the first 3D model. The method may also include fabricating a set of dental alignment devices configured to implement the treatment plan when the dental alignment devices in the set of dental alignment devices are sequentially worn on the patient's dentition.

A computer-implemented system includes one or more processing devices configured to receive comorbidity information that includes a plurality of comorbidities or comorbidity-related conditions associated with a user, generate a selected set of the comorbidity information, determine, based on the selected set of the comorbidity information, respective probabilities of a plurality of different outcomes related to the comorbidity information, and generate, based on the respective probabilities and the selected set of the comorbidity information, a treatment plan comprising one or more exercises directed to changing the respective probabilities. A treatment apparatus is configured to implement the treatment plan while the treatment apparatus is being manipulated by the user.

A method for diagnosing and recommending treatment to a patient that may or may not present with occlusal disease includes acquiring 3D scans of the patient's maxillary and mandibular arches, and acquiring occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically generating a treatment plan recommendation, and a recommended treatment plan projection and a non-treatment projection based, at least in part, on the acquired 3D scans of the patient's maxillary and mandibular arches, and the acquired occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically displaying a rendering of the patient's simulated dental arch N years in the future based on the automatically generated recommended treatment plan projection, and a rendering of the patient's simulated dental arch N years in the future based on the automatically generated non-treatment projection, where N is greater than 1.

Disclosed herein are systems, methods, and devices for an image-based, computer vision approach for anthropometric measurement of a user using an automatically generated three-dimensional model of the user. Also disclosed herein are systems, methods, and devices associated with a telehealth proctoring platform that can be used to remotely proctor, monitor, and manage patients over the course of a medical treatment plan. The telehealth proctoring platform may be used to collect and retrieve various kinds of data associated with a patient, such as at-home diagnostic test data, the anthropometric measurements of the patient, or the generated three-dimensional models in order to remotely monitor and track changes to the body of a patient over time and make dynamic adjustments to the patient's medical treatment plan.

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

Apparatus and associated methods relate to controlling electrical power of an electrotherapeutic signal that is provided to a biological tissue engaged by an electrosurgical instrument during a medical procedure. Electrical power—a product of a voltage difference across and an electrical current conducted by the engaged biological tissue—is controlled according to a therapeutic schedule. The electrotherapeutic schedule can be reduced or terminated in response to a termination criterion being met. In some examples, the termination criterion is a current characteristic, such as, for example, a decrease in current conducted by the engaged biological tissue. In some examples, the termination criterion is a biological tissue resistance characteristic, such as, for example, an increase in the biological tissue resistance that exceeds a predetermined delta resistance value.

The invention relates to the technical field of medical treatment, in particular to a deformation registration method and system, a self-adaptive treatment planning method and a treatment planning system which are used for improving the accuracy of image deformation registration, and the method comprises the steps: obtaining a plan image, a fractional image and dose distribution data corresponding to an initial treatment plan of a patient; inputting the plan image, the graded image and the dose distribution data corresponding to the initial treatment plan of the patient into a trained deformation registration model, performing deformation registration on the graded image and the plan image, and outputting a deformation field; wherein the trained deformation registration model is obtained based on dose distribution consistency loss training between dose distribution data corresponding to the initial treatment plan and deformation dose distribution data corresponding to the deformation field.

A system for real-time generation of therapeutic plans based on predictive analytics of patient profile data including a processor of a therapeutic plan server (TPS) node configured to host a machine learning (ML) module and connected to at least one patient-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive the patient profile data including patient nutrients intake data and medications intake data from the at least one patient-entity node; parse the patient profile data to derive a plurality of key classifying features; query a local database to retrieve local historical patients-related data based on the plurality of key classifying features; generate at least one classifier feature vector based on the plurality of key classifying features and the local historical patients-related data; provide the at least one feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of nutrients-medications correlation parameters from a therapeutic plan predictive model generated by the ML module using outputs of the ANN based on the at least one feature vector; and generate a therapeutic plan for the at least one patient-entity node based on the nutrients-medications correlation parameters.

We provide a treatment planning, treatment evaluation, and treatment instruction creation support system that uses additional learning from case data for which diagnosis and treatment have been completed in a large-scale language model. [Solution] The system includes a case data recording means for recording case data containing case feature data and treatment content data for patients whose diagnosis has been confirmed and whose treatment has been completed, a case learning means for adding pairs of case feature data and treatment content data for each case to a large-scale language model (LLM) as supervised learning data, and a treatment content data suggestion means for suggesting treatment content data from the case feature data of patients for whom treatment is to be initiated.

The present disclosure relates to the field of radiation therapy and methods, software and systems for treatment planning.

The invention relates to the technical field of medical equipment, and provides a focused ultrasound system with multiple treatment modes. A power driving unit; the treatment head comprises a first ultrasonic transducer which is connected with the power driving unit and is used for responding to the excitation signal to form focused ultrasound and outputting the focused ultrasound to a focus; the control unit is suitable for determining a treatment plan according to the tissue characteristic information, and the treatment plan comprises the following steps: performing focused ultrasound irradiation on the target tissue by adopting one or more cooperative treatment modes adaptive to the tissue characteristic information; and controlling the power supply unit and the power driving unit to perform corresponding signal output based on the treatment plan, wherein the plurality of treatment modes comprise a heat effect mode and a histotripsy mode. The focused ultrasound system integrating multiple treatment modes is achieved, one or more matched modes can be selected for cooperative treatment based on tissue characteristics, the treatment efficiency and effect are effectively improved, and operation is convenient.

Systems, methods, and computer-readable media for and improvement rehabilitation infrastructure. The method includes receiving data associated with a user that uses a electromechanical machine to perform a treatment plan. The method also includes generating, using an artificial intelligence engine, a unique data signature associated with a regression of the user's condition. The unique data signal is generated when an indicator in the data satisfies a threshold indicator level. The method further includes generating, using the artificial engine and based on the unique data signature associated with the regression of the user's condition, a modified treatment plan that modifies a parameters or activity associated with at least one of the pain measurement, the measurement of revolutions per minute, and the session pedaling time. The method also includes controlling, while the user uses the electromechanical machine and using the modified treatment plan, the electromechanical machine.

A method may include obtaining input data relating to a target treatment plan for performing radiotherapy on a lesion using a radiation device. The input data may include a first target image of the lesion. The method may also include obtaining a segment shape estimation model. The method may also include estimating, based on the segment shape estimation model and the input data, a plurality of target location combinations of the target treatment plan and a plurality of target segment shapes of a collimator of the radiation device. One of the plurality of target location combinations may indicate a location of the collimator relative to the lesion. Each of the plurality of target segment shapes may correspond to one of the plurality of target location combinations.

A system and method, including an ultrasound imaging transducer, an ultrasound therapy transducer, a robotic arm configured to orient the ultrasound imaging transducer and the ultrasound therapy transducer about a patient, a display connected to imaging transducer, and a memory, storing instructions that when executed by a processor receive ultrasound images from the imaging transducer, present ultrasound images, receive an input to alter a shape of a contour line, present the contour line, receive an input of a size of a margin around the treatment area, present the margin, determine end points at locations where X, Y, and Z axes bisect the contour line in an XZ plane and a YZ plane, drive the ultrasound therapy transducer to a location where a focal point of the ultrasound therapy transducer is at the end point, and determining whether a resistance to movement exceeds a threshold.

Methods of representing user edits or modifications of a treatment plan for a dental apparatus (e.g., one or a series of aligners) by managing an original clinical data set and a set of modifications of the clinical data set. Also described herein are methods of displaying, condensing, storing and reconstructing collision regions between two or more teeth.

A method provides a palatal expansion management tool. The method includes receiving scan data of a craniofacial structure of a patient, providing the scan data as input to an artificial intelligence (AI) model trained to provide a recommended palatal expansion treatment plan for the patient; receiving, as output from the AI model, the recommended palatal expansion treatment plan; and providing, to a user device, the recommended palatal expansion treatment plan.

Embodiments of the present invention provide a method, system, devices and software which provide customized, clinically relevant designs, e.g. treatment plans / solutions for at least single tooth replacement therapy in real-time as needed to enable immediate confirmation of the validity and execution of patient-specific treatment solutions.A Machine Learning algorithm is used in a method or system for computer implementation of automatic restoration design. The restoration design includes the design of restorative elements such as crowns and abutments. Software is provided for carrying out the methods when executed on a digital processor. Manufacturing of the restorative elements is also included.