895 results about "Therapy planning" patented technology

The invention relates to the technical field of health assessment, in particular to a big data-based breathing health assessment system, which comprises a breathing parameter analysis module, an interaction relationship assessment module, a breathing anomaly detection module and a breathing condition assessment module. According to the method, accurate extraction of breathing parameters and evaluation of breathing periodic changes are achieved by analyzing thoracic pressure waveform data, the breathing mode of a user is accurately recognized, physiological parameters are collected, the mutual relation between the physiological parameters and the breathing mode is analyzed, the influence of various physiological factors on breathing health is revealed, the dynamic monitoring capacity on the breathing health is enhanced, and the user experience is improved. According to the method, by combining detection and identification of abnormal breathing parameters, identification and classification of abnormal breathing reasons are achieved, medical professionals are helped to make treatment plans and health management strategies, diagnosis efficiency is improved, the misdiagnosis rate is reduced, and powerful support is provided for management of various chronic diseases by combining continuous trend analysis.

Described are systems and methods for artificial intelligence (AI)-based clinical decision support. Systems can include a platform configured with a user input processing module, a context matching module, a retrieval-augmented generation (RAG) module, and an output generation module. Outputs of the platform can include a differential diagnosis, an assessment and treatment plan, or a clinical reference. The platform can further include an AI-copilot module and an AI-notebook module.

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.

The invention discloses a multi-angle particle therapy system without a rotating rack and a using method thereof, and relates to the technical field of particle therapy equipment, and the treatment system comprises an accelerator, an irradiation system, a control system, a patient supporting system and an imaging system. The patient supporting system comprises a containing and fixing device, a two-dimensional translation system, a rotating system and a multi-degree-of-freedom mechanical positioning system, the containing and fixing device is connected with the two-dimensional translation system, the two-dimensional translation system is connected with the rotating system, and the two-dimensional translation system controls the relative position of a patient and a rotating center; the rotating system rotates a patient to an appointed angle of a treatment plan or cooperates with the imaging system to obtain a three-dimensional image of the patient, and the irradiation system irradiates a tumor target area; the use method comprises the following steps: generating a treatment plan by reconstructing a three-dimensional CT image, and completing irradiation at different angles according to the treatment plan. According to the treatment system, multi-angle irradiation on a patient can be realized under the condition that a rotating rack is not used, and the manufacturing period and cost of the particle treatment system can be reduced.

Various systems and methods are provided for generating and editing of medical reports and records using artificial intelligence (AI) assistance. An intelligent medical reporting tool may edit medical materials such as electronic medical records, medical reports, and treatment plans. The intelligent medical reporting tool can provide edit recommendations and suggested additions to medical materials to a user of the tool. Edit recommendations and suggested additions may be related to contradictions, incompleteness, clarity, and clinical guidelines.

The present invention relates generally to methods and systems that can be used to analyse the mouth condition of an individual. The methods and systems can be used in the identification of an oral condition or disease to provide a personalised holistic treatment care plan for the individual. More particularly, the present invention relates to methods and systems that employ artificial intelligence capabilities for generating a treatment plan for an identified oral condition or disease.

A method is provided for optimizing a dental treatment plan. In some cases, the method can include receiving patient data comprising one or more progress indicators associated with an dental treatment plan, processing the patient data to determine a level of progression associated with the dental treatment plan based on the one or more progress indicators, modifying the dental treatment plan in response to the determined level of progression, and generating a notification of the modified dental treatment plan. In some cases, modifying the dental treatment plan in response to the determined level of progression includes advancing a patient associated with the patient data to a subsequent stage of the dental treatment plan or retaining the patient within a current stage of the dental treatment plan.

A computer-based adaptive decision support system for radiotherapy, including: • a patient data acquisition module configured to acquire patient-specific clinical data, including an anatomical image, a physiological signal, and genomic data; • a preprocessing and feature extraction modality compatible with normalizing, preprocessing and extracting statistical features from the acquired data; • a status estimator used to provide a dynamic representation of the patient's treatment evolution status based on radiation, biological, dosimetric characteristics; • customized action rescaler to define a series of clinically meaningful treatment adjustments depending on the patient's current condition; • a reward function engine used to calculate therapeutic outcome scores based on the probability of tumor control, the probability of complications in normal tissue, and other predetermined factors; • a reinforcement learning agent that can learn and update treatment adaptation policies based on deep reinforcement learning techniques; • a clinical decision dashboard that provides recommended treatment adjustments and personalized interaction with the physician; and • a clinical integration interface adapted for exporting the customized treatment plan to an external treatment planning or delivery system.

System and methods are described for developing a delivery plan for a pulsed field ablation device for treating an arrhythmia. A method runs a lesion development simulation based on cardiac characteristics and a treatment plan that specifies a delivery plan and a target location to generate a simulated lesion having lesion characteristics. The method initializes a three-dimensional (3D) mesh representing a heart based on cardiac characteristics. The vertices of the 3D mesh are associated with cardiac tissue characteristics with some of the associated with cardiac tissue characteristics representing an arrhythmia source. The method adjusts the cardiac tissue characteristics of vertices of the 3D mesh to reflect the effect of an ablation resulting in formation of the simulated lesion. The method runs a lesion evaluation simulation based on the 3D mesh with the adjusted cardiac tissue characteristics to determine whether the simulated lesion would be effective at treating the arrhythmia.

The invention discloses an AI-based child nebulization treatment management system and method, and relates to the field of treatment scheme management, and the system comprises a management and control module which is used for obtaining the medical general archive access and read-write authority of a target medical place, carrying out the editing and sending of a control instruction for each functional module, and sending the control instruction to the medical general archive access and read-write authority to the medical general archive access and read-write authority to the medical general archive access and read-write authority; summarizing and storing working results and analysis data of the functional modules; the data acquisition module is used for acquiring physique data, illness state data and treatment plans of the patient in the current period based on a medical general archive; the feature selection module is used for performing feature recognition on the data acquired by the data acquisition module by adopting a multi-modal feature coupling algorithm; by collecting and analyzing physical characteristics, disease progression, historical treatment response and feedback information of a patient in real time, a personalized treatment scheme is formulated according to specific conditions of each child, a general treatment mode is avoided, unnecessary side effects are reduced, and treatment effectiveness and safety are improved.

Disclosed are a radiotherapy system and a treatment plan generation method therefor. The radiotherapy system includes a beam irradiation device, a treatment planning module and a control module. The beam irradiation device generates a beam for treatment and irradiates same to a body to be irradiated to form an irradiated site, the treatment planning module generates a treatment plan on the basis of parameters of the beam for treatment and medical image data of the irradiated site, and the control module retrieves a treatment plan corresponding to said body from the treatment planning module and controls the beam irradiation device to sequentially irradiate said body according to at least two irradiation angles determined according to the treatment plan generation method and the irradiation time corresponding to each irradiation angle.

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.

A machine learning-based method of generating a radiotherapy treatment plan for a patient, comprises dose prediction and dose mimicking, wherein the dose prediction step involves using a machine learning system that has been trained to consider at least one optimality criterion related to physical or technical restrictions that will affect the delivery of the treatment plan. Thus, at least one of the factors that are normally taken into account in the dose mimicking step is introduced in the dose prediction step. The invention also relates to a method of training such a machine learning system for use in radiotherapy treatment planning, a computer program product and a computer system.

A system for closed-loop control of sub-perception spinal cord stimulation therapy is provided. The system includes an implantable pulse generator (IPG) with an evoked compound action potential (ECAP) recording module and accelerometer. A regression model maps accelerometer data to optimal stimulation parameters for consistent nerve activation. This enables closed-loop therapy in the sub-perception domain where ECAP features are not visually detectable. A clinician programmer applies the model and sub-perception dose to therapy programs deployed to the IPG. The model can be customized to each patient by collecting ECAP and accelerometer data. The mapping reconciles ECAP -based control between perception and sub-perception domains by supplementing unavailable ECAP data with accelerometer data. This improves stimulation therapies for patients receiving sub-perception and multi-sensor paresthesia treatments.

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.

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.

Embodiments described herein provide for radiotherapy treatment plan generation using a machine learning language processing model. A processor can present a user interface providing an interaction interface between a user and a machine learning language processing model. The processor can receive a first input comprising a first patient attribute of a patient. The processor can execute the machine learning language processing model using the first patient attribute as an input to generate a response requesting a second patient attribute. The processor can present the response requesting the second patient attribute of the patient. The processor can receive a second input comprising the second patient attribute of the patient. The processor can transmit the first patient attribute of the patient and the second patient attribute of the patient to a radiotherapy plan optimizer. The radiotherapy plan optimizer can be configured to generate a radiotherapy treatment plan for the patient.

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.

A system for closed-loop control of sub-perception spinal cord stimulation therapy is provided. The system includes an implantable pulse generator (IPG) with an evoked compound action potential (ECAP) recording module and accelerometer. A regression model maps accelerometer data to optimal stimulation parameters for consistent nerve activation. This enables closed-loop therapy in the sub-perception domain where ECAP features are not visually detectable. A clinician programmer applies the model and sub-perception dose to therapy programs deployed to the IPG. The model can be customized to each patient by collecting ECAP and accelerometer data. The mapping reconciles ECAP-based control between perception and sub-perception domains by supplementing unavailable ECAP data with accelerometer data. This improves stimulation therapies for patients receiving sub-perception and multi-sensor paresthesia treatments.

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.

Described herein are methods and systems of performing immunotherapy on a subject and / or determining if a subject will be responsive to ablation immunotherapy.

Disclosed herein are systems and methods for adapting and / or updating radiotherapy treatment plans based on biological and / or physiological data and / or anatomical data extracted or calculated from imaging data acquired in real-time (e.g., during a treatment session). Functional imaging data acquired at the time of radiation treatment is used to modify a treatment plan and / or dose delivery instructions to provide a prescribed dose distribution to patient target regions. Also disclosed herein are methods for evaluating treatment plans based on imaging data acquired in real-time.

The invention relates to the technical field of medical equipment, provides a non-intrusive closed-loop ear vagus nerve stimulator for improving cerebral apoplexy and a use method, and aims to improve the symptoms of a patient by stimulating the ear vagus nerve in a conchae area. The device integrates an electrical stimulation electrode, a host and a closed-loop feedback module, monitors brain blood flow changes and other physiological parameters of a patient in real time, and automatically adjusts stimulation parameters through an intelligent algorithm to realize personalized treatment. A microprocessor, a multi-mode monitoring unit and a data fusion analysis unit are arranged in the host, and accurate and efficient treatment is ensured. In addition, the equipment supports remote intelligent monitoring and guidance, a treatment plan can be remotely adjusted, and the treatment effect is improved. A user interaction interface is friendly, and operation of patients and medical staff is facilitated. The stimulator is light in design, the conductive silica gel electrode is adopted, through real-time monitoring and intelligent adjustment, accurate and efficient treatment is provided for a patient, the comfort level and treatment convenience of the patient are ensured, and a safe and effective rehabilitation scheme is provided for the stroke patient.

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.

An aspect includes examining a dental defect in an oral cavity, performing a minimally invasive procedure to detect or remove a portion of the dental defect, scanning the oral cavity including a residual tooth substance remaining after the performing, to obtain an intraoral scan, computing one or more parameters of the dental defect or residual tooth substance, and generating a number of treatment plans for one or more restoration materials based on a triangulation of the dental defect and an enforcement of constraints of the one or more restoration materials. Each treatment plan includes a geometry of a restoration option, and an optimal restoration option is selected from the plurality of treatment plans.

The present application provides a plan adjustment method for radiotherapy, a radiotherapy system, and a related apparatus. The method comprises the following steps: S1, acquiring simulated CT information and treatment plan information of a patient, the simulated CT information comprising a simulated CT image and region division information corresponding thereto; S2, measuring, in the current session of radiotherapy corresponding to the treatment plan information, Cerenkov radiation emitted by the patient due to particle radiation exposure; S3, acquiring irradiation exposure dose information of an irradiation target region and an organ at risk in the treated region; S4, determining, according to the dose information of the target region and the organ at risk and the treatment plan, whether the treatment plan for subsequent irradiation fractions needs adjustment; and S5, if so, adjusting the treatment plan for subsequent fractions according to dose distribution of an irradiation exposure region and a therapeutic prescription corresponding to the patient. In the present application, the Cerenkov radiation emitted by the patient is detected to judge the quality of the treatment plan, which improves the accuracy of plan adjustments in radiotherapy and the safety of radiotherapy.

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.

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.

In one embodiment, a computer-implemented system includes an electromechanical machine configured to be manipulated by a user while the user performs a treatment plan. The treatment plan includes a high-intensity interval training (HIIT) session. A processing device is configured to initiate, using the electromechanical machine, the HIIT session, receive, via one or more sensors, one or more measurements pertaining to the electromechanical machine, determine whether the one or more measurements exceed one or more of one or more corresponding thresholds, and in response to determining that the one or more measurements exceed one or more of the one or more corresponding thresholds, modify the treatment plan to cause operation of the electromechanical machine to be modified.