317 results about "Treatment outcome" patented technology

The invention provides a rectal cancer neoadjuvant chemoradiotherapy curative effect prediction method and system based on magnetic resonance image analysis, and aims to improve accurate prediction of curative effects of patients with local advanced rectal cancer (LARC). According to the method, multi-modal magnetic resonance images (T1WI, T2WI, DWI and DCE-MRI) are obtained, qualitative and quantitative analysis is combined, tumor morphology, diffusion characteristics and microcirculation parameters are extracted, clinical and pathological data of a patient are integrated, and a multi-dimensional feature set is constructed. Curative effect prediction is performed through machine learning and a deep learning model, and the model can predict treatment results of complete remission, partial remission, ineffectiveness and the like in a classified manner. The system accurately predicts the reaction of a patient to neoadjuvant radiotherapy and chemotherapy through a non-invasive method, supports formulation of a personalized treatment scheme, reduces excessive treatment, optimizes the treatment effect, and has good clinical application value.

Electroporation-based therapies (EBTs) employ high voltage pulsed electric fields (PEFs) to permeabilize tumor tissue, resulting in changes in passive electrical properties detectable using electrical impedance spectroscopy (EIS). Currently, commercial potentiostats for EIS are limited by impedance spectrum acquisition time (˜10 s); this timeframe is much larger than pulse periods used with EBTs (˜1 s). Fourier Analysis SpecTroscopy (FAST) is introduced as a methodology for monitoring tissue inter-burst impedance (diagnostic FAST) and intra-burst impedance (therapeutic FAST) during EBTs. FAST is a rapid-capture (<<1 s) technique which enables monitoring of inter-burst and intra-burst impedance during EBTs in real-time. FAST identified a frequency which delineates thermal effects from electroporation effects in measured impedance. Significance: FAST demonstrates the potential to perform EIS, in addition to intra-burst impedance spectroscopy, using existing pulse generator topologies.

Systems and methods for designing and implementing patient-specific surgical procedures and / or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

Electroporation-based therapies (EBTs) employ high voltage pulsed electric fields (PEFs) to permeabilize tumor tissue, resulting in changes in passive electrical properties detectable using electrical impedance spectroscopy (EIS). Currently, commercial potentiostats for EIS are limited by impedance spectrum acquisition time (˜10 s); this timeframe is much larger than pulse periods used with EBTs (˜1 s). Fourier Analysis SpecTroscopy (FAST) is introduced as a methodology for monitoring tissue inter-burst impedance (diagnostic FAST) and intra-burst impedance (therapeutic FAST) during EBTs. FAST is a rapid-capture (<<1 s) technique which enables monitoring of inter-burst and intra-burst impedance during EBTs in real-time. FAST identified a frequency which delineates thermal effects from electroporation effects in measured impedance. Significance: FAST demonstrates the potential to perform EIS, in addition to intra-burst impedance spectroscopy, using existing pulse generator topologies.

Likely outcomes of a treatment of an adolescent patient who has major depressive disorder (“MDD”) using machine learning. Symptoms reported at one or two points in time are input to a suitably trained machine learning model, generating output that indicate a prediction of the most likely outcome of a particular treatment at a third point in time, a future severity of symptoms, or another clinical course of action. The treatment outcome can include the likelihood of remission, response to drug, selection of the drug most likely to give positive outcomes, and so on. The machine learning model can implement a probabilistic graphical model, as an example.

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.

Provided herein are imaging systems for a patient comprising an imaging probe and an imaging assembly. The imaging probe comprises: an elongate shaft comprising a proximal end, a distal portion, and a lumen extending between the proximal end and the distal portion; a rotatable optical core comprising a proximal end and a distal end, wherein at least a portion of the rotatable optical core is positioned within the lumen of the elongate shaft; and an optical assembly positioned proximate the distal end of the rotatable optical core, the optical assembly configured to direct light to tissue and collect reflected light from the tissue. The imaging assembly is constructed and arranged to optically couple to the imaging probe. The imaging assembly is configured to emit light into the imaging probe and receive the reflected light collected by the optical assembly. The system is configured to provide treatment information, wherein the treatment information is used by an operator to plan a treatment and / or predict a treatment outcome.

The invention is suitable for the field of smart medical treatment, and provides a smart medical platform based on big data, and the platform comprises a data collection module, a data analysis module, a referral aid decision-making module, a comprehensive data sharing module, a health management monitoring module, an autonomous participation management module, and a library learning updating module. Data analysis and artificial intelligence technologies are utilized to optimize the referral process of the patient, it is ensured that the patient can be treated in the most suitable medical institution, and efficient and accurate referral decision support is achieved by combining the individual disease development characteristics of the patient, the current treatment effect and real-time data of medical resources in the area. The efficiency and the accuracy of the medical referral process are remarkably improved, meanwhile, the medical experience and the treatment result of the patient are improved, the problems that medical resources are distributed unevenly, the referral process is complex and the like are effectively solved, and the method has important significance on continuous optimization and development of a modern medical system.

Surgical treatment of glaucoma involves creation of a sclerocorneal drainage channel, ensuring long-lasting controlled fluid flow and reduced intraocular pressure. Limitations relate to collateral damage to adjacent structures and outcomes being heavily reliant on the surgeon's expertise. The device described herein addresses such shortcomings. The device comprises a grasping body having a longitudinal axis, and a piercing member coupled to the grasping body, including an elongate body and a beveled piercing tip including a long side and a short side, wherein the piercing member includes a proximal segment, a distal segment, and an aperture nested between the proximal segment and the distal segment, wherein the short side and the aperture are coaxially disposed along a central longitudinal axis of the piercing member, and wherein the piercing member is configured to reciprocate along the longitudinal axis relative to the grasping body.

A system for the treatment of chronic diseases, consisting of: a data acquisition unit configured to capture patient health information in real time from wearable or networked health monitoring devices, electronic health records, and patient-reported outcomes; a central processing unit configured to preprocess the captured data by normalizing, aggregating, and coding the data for further analysis; a feature extractor operationally connected to the central processing unit, the feature extractor being capable of extracting key features from preprocessed data, including demographic data, clinical history, biomarker levels, and lifestyle;a predictive modeling engine communicating with the feature extraction module, wherein the predictive modeling engine is configured to use machine learning techniques to generate recognition models from historical patient data, the models predicting disease progression, treatment outcomes, and potential complications; a visualization interface connected to the predictive modeling engine, wherein the visualization interface is configured to display the results of the predictive analytics to healthcare providers and patients to enable actionable insights for the management of chronic diseases; and a feedback mechanism integrated into the visualization interface, configured to allow users to input real-time feedback on the predicted outcomes, the feedback being used to refine the predictive models over time.

A system for automated programming of stimulation devices uses machine learning with multimodal medical imaging data to determine optimal patient-specific parameters. The system stores voxel-level intensity and functional imaging data from patients who previously underwent stimulation therapy. Similarity metrics calculated directly between the raw imaging data are used to cluster patients into phenotypic groups. Known therapeutic outcomes for each patient are linked to the associated stimulation parameters. For a new patient, biomarkers are extracted from their medical imaging data and used to identify phenotypically similar groups. The stimulation parameters with beneficial outcomes in those groups are analyzed to determine target recommended settings or discard suboptimal settings for the new patient. This data-driven approach leverages machine learning on multimodal medical imaging to automate patient-specific programming of stimulation therapy devices for optimal therapeutic benefit without requiring spatial normalization or atlas registration.

The present invention discloses an artificial intelligence-based (or AI-based) system and method for providing personalized treatment plans to individuals suffering from mental health disorders. By leveraging EEG measurements, patient data, and AI algorithms, this system addresses the unique needs of each patient and enhances the overall quality of mental health care. The AI-based personalized treatment system has the potential to revolutionize mental health care. By amalgamating EEG measurements and an extensive range of patient-specific factors, it enables a tailored, dynamic approach to mental health treatment that takes into account the complete patient profile. The integration of this system into existing electronic health record (EHR) systems or patient monitoring platforms creates a patient-centered experience that enhances treatment outcomes, patient satisfaction, and reduces the strain on healthcare professionals.

Systems and methods for designing and implementing patient-specific surgical procedures and / or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

A computer-implemented method includes producing medical information that characterizes a group of individuals from a set of private data representing pre or post-encounter characteristics of the individuals, wherein the individuals have had encounters with a healthcare facility. The identity of the individuals is unattainable from the produced medical information. The method also includes providing the produced medical information to report the pre or post-encounter characteristics of the group.

The invention provides an auxiliary system for a rheumatoid arthritis medication scheme. The auxiliary system is used for matching similar historical medication schemes, related medication costs and treatment results used for treating historical rheumatoid arthritis patients and judging whether transaminase abnormity occurs or not based on the treatment conditions of current patients, analyzing each historical medication scheme, and determining whether the transaminase abnormity occurs or not according to the analyzed historical medication schemes and the analyzed transaminase abnormity. And obtaining scores or grades of three dimensions of safety, effectiveness and economy of each historical medication scheme. And based on the requirements or tendencies of the patient on the medication schemes in three dimensions of safety, effectiveness and economy, screening out corresponding candidate medication schemes from the historical medication schemes and recommending the candidate medication schemes. According to the technical scheme, by means of the computer technology, the corresponding medication scheme is automatically recommended based on the medication tendency of the rheumatoid arthritis patient, decision assistance is provided, the time for adapting the medication scheme for the patient is saved, and the satisfaction degree of the rheumatoid arthritis patient to the medication scheme is improved.

In some aspects, the present disclosure describes a method of predicting an expected treatment outcome of a subject, comprising administering a gamma oscillation-inducing non-invasive sensory stimulus to the subject, measuring a response from the subject, and predicting, using a machine learning algorithm, the expected treatment outcome of the subject based at least partially on the measured response. In some aspects, the present disclosure also provides methods for personalizing gamma therapy treatment by adjusting parameters associated with the gamma oscillation-inducing non-invasive sensory stimulus based on a subject's response to the gamma oscillation-inducing non-invasive sensory stimulus.

The invention is applicable to the technical field of biological medicines, and provides application of Paquinimod in preparation of a medicine for treating secondary lymphedema. According to the application, the S100A8 / A9 dimer small-molecule inhibitor Paquinimod is used for treating mice with lymphedema, and a result shows that the Paquinimod can be used for remarkably inhibiting secretion of inflammatory factors of the model mice, effectively relieving the edema condition and inhibiting activation of an S100A8 / A9-TLR4-CCL2 signal channel. Therefore, the inhibitor Paquinimod of the S100A8 / A9-TLR4 signal channel is applied to preparation of the medicine for treating the secondary lymphedema (SL), a new thought and a new method are developed for treatment of the SL, and the problem that the SL lacks an effective treatment means is effectively solved.

The invention relates to an AI large model-based tentative and anti-syndrome traditional Chinese medicine thinking medicine auxiliary system of a space agent technology. The system shows the characteristics of diversification and multi-level through the cognition and thinking method of traditional Chinese medicine; comprising eight traditional Chinese medicine thinking modules: observation and speculation (an outside and inside compulsory module and an image thinking module), comparison and analogy (an object-to-thing ratio module and an image-taking ratio module), reflection and pause (a heart law and pause module), empirical and adjustment (a tentative and anti-empirical module), logical reasoning (an induction and deduction module) and an overall and change (a system thinking module). The system deploys a human body area network AI algorithm in a physical medical space, intelligently speculates internal viscera changes of a patient, has an interactive feedback function, and provides dynamic diagnosis suggestions and treatment scheme adjustment; recording a diagnosis process and a treatment result of a traditional Chinese medicine doctor, forming a traditional Chinese medicine thinking and medicine knowledge base, and creating a traditional Chinese medicine thinking and medicine targeted medical new prescription; and traditional Chinese medicine thinking is promoted to construct a new world of international development and artificial intelligence.

An Integrated Virtual Patient Framework (IVPF) for providing decision support that evolves with increasing data collected on a given patient. Support decisions for patients with limited data is made using statistical prediction tools derived from historical trajectories of similar patients. As patient histories grow, decision support is provided by mathematical models that constrain the possible dynamics of the patient to more detailed predictive models. Weights for each model are assigned depending on uncertainties arising from data fitting and model properties. As new data are entered into a patient record, the models are recalibrated and the weights are adjusted, leading to updated decision support information. The framework also suggests the benefit of additional follow-up data collection events, optimizing the data collection as well as how the IVPF generates predictions. The framework also may present scenarios to patients in a way that informs them of treatment outcomes, given various strategies.

Systems and methods are disclosed for analyzing data from oncology treatments such as immune checkpoint inhibitor or radiotherapy therapies, including predicting adverse events of the oncology therapies, predicting objective response of the oncology therapies, predicting symptoms from the oncology therapies, and use of such predictions by technological implementations to achieve improved system and medical outcomes. An example technique for generating a predicted treatment outcome includes: receiving patient data for a human subject, which provides patient-reported outcomes collected from the human subject relating to a particular oncology treatment; processing the patient data with a trained artificial intelligence (AI) prediction model, which receives the patient data as input and produces a prediction of a treatment outcome as output; and outputting data to modify a treatment workflow of an oncology treatment for the human subject, based on the prediction of the treatment outcome.

Systems and techniques are disclosed to identify and monitor side effects and treatment outcomes relating to neurostimulation programming. An example technique to identify a side effect relating to neurostimulation programming includes: obtaining neurostimulation programming parameters to be used in a neurostimulation device of a patient; obtaining source data that defines effects of neurostimulation treatment in an anatomical area of the patient; modeling use of the neurostimulation programming parameters in the anatomical area of the neurostimulation treatment, based on the source data, to determine a predicted side effect; and outputting information that identifies characteristics of the predicted side effect.

The invention relates to a diagnosis and treatment result prediction method fusing a time sequence and traditional Chinese medicine multi-stage diagnosis and treatment, belongs to the technical field of traditional Chinese medicine diagnosis and treatment prediction, and solves the problem of lack of accurate whole-process prediction in the prior art. The method comprises the following steps: acquiring the current doctor-seeing symptom and historical doctor-seeing time sequence data of a to-be-predicted patient; the historical treatment time sequence data comprises symptom, syndrome, therapy and prescription data of each time step; constructing a graph structure corresponding to each time step of the historical doctor-seeing time sequence data; extracting a symptom feature sequence, a syndrome feature sequence, a therapy feature sequence and a prescription feature sequence by adopting a trained graph neural network model based on the graph structure and the current treatment symptom; and based on the symptom feature sequence, the syndrome feature sequence, the therapy feature sequence and the prescription feature sequence, performing multi-stage diagnosis and treatment result prediction by adopting a trained recurrent neural network model to obtain the prediction results of the syndrome, the therapy and the prescription of the current doctor seeing of the to-be-predicted patient. And accurate whole-process prediction is realized.

Methods and systems for analyzing and selecting therapy configurations for use in stimulating neural tissue. Probability shells relating to a likelihood that electrical stimulation issued to a given volume of neural tissue will cause a therapeutic outcome are integrated in a system for analyzing anatomical and other data, including lead position relative to neural anatomy. Metrics for analyzing therapy configurations can then be calculated, and the process of identifying likely beneficial therapy configurations is enhanced.

To provide methods for administering RTU injectable preparations of aripiprazole that allows administration to a population of patients considered of having a potential risk for adherence-related relapse or suboptimal treatment outcomes, and allows achievement of therapeutic plasma concentrations without reliance on daily dosing of oral tablets.SOLUTION: The present disclosure is directed to a method for treating a patient with schizophrenia or bipolar disorder Type I by administering to the patient an injectable preparation of aripiprazole, the patient receiving the injectable preparation approximately once every two months.SELECTED DRAWING: None

A system and method for enhanced aerosol drug delivery during high flow nasal cannula (HFNC) therapy. The system comprises a breath enhanced jet nebulizer (BEJN) connected to a high flow medical breathing gas supply supplying breathing gas to the BEJN at flow rates of 5 L / min to 60 L / min, a nebulization gas supply, a drug infusion device that supplies a drug solution to the nebulizer for the delivery of nebulized drug to a patient. All the breathing gas and nebulized drug solution delivered to the patient passes through the BEJN. The rate of drug delivery increases with increases in the flow rate of the medical breathing gas supply and increases in infusion pump flow rates. The drug solution infusion rate is between 5 mL / hour and 50 mL / hour. This system and method enable efficient aerosol drug delivery to patients during HFNC therapy for prolonged periods, improving treatment outcomes.

Methods for visualizing treatment outcomes are provided. In some embodiments, a method includes obtaining a first 2D image of a patient's teeth from a mobile device, and obtaining an arch model representing an outcome for the patient's teeth after orthodontic treatment. The method can include matching the patient's teeth in the first 2D image to corresponding teeth in the arch model, using a parametric model. The method can also include determining a target position for the patient's teeth, based on a position of the matched corresponding teeth in the arch model. The method can further include generating a second 2D image depicting the patient's teeth in the target position.

System (100) for personalized condition-based matching of health networks using artificial intelligence and population health data, comprising: (a) a patient profiling module configured to collect and analyse individual health data from electronic health records, wearable devices and patient inputs to identify current medical conditions and generate dynamic health profiles; (b) a population health intelligence engine configured to aggregate and analyze population-level health data to identify regional disease trends, treatment outcomes, and provider performance; c) an AI-based network matching algorithm configured to compare individual health conditions with provider capabilities, historical outcomes, availability, and other contextual parameters to generate rated recommendations for healthcare providers; (d) a provider profiling and network indexing module configured to store, index and update provider and network information, including specialization, certifications, outcomes, availability and treatment capacities; (e) a decision support interface configured to present patients or clinicians with personalized, explainable recommendations for healthcare providers based on clinical relevance, patient preferences and system intelligence; (f) a feedback and learning optimization module configured to continuously collect patient outcomes, satisfaction, and treatment effectiveness to refine and retrain AI models; and g) a data security and compliance layer configured to ensure the secure handling of patient data, enforce access control, and ensure compliance with data protection standards such as HIPAA and GDPR.

Provided herein are imaging systems for a patient comprising an imaging probe and an imaging assembly. The imaging probe comprises: an elongate shaft comprising a proximal end, a distal portion, and a lumen extending between the proximal end and the distal portion; a rotatable optical core comprising a proximal end and a distal end, wherein at least a portion of the rotatable optical core is positioned within the lumen of the elongate shaft; and an optical assembly positioned proximate the distal end of the rotatable optical core, the optical assembly configured to direct light to tissue and collect reflected light from the tissue. The imaging assembly is constructed and arranged to optically couple to the imaging probe. The imaging assembly is configured to emit light into the imaging probe and receive the reflected light collected by the optical assembly. The system is configured to provide treatment information, wherein the treatment information is used by an operator to plan a treatment and / or predict a treatment outcome.

A device for predicting drug reactions in cancer patients through machine learning optimization, wherein the device comprises the following: a data acquisition unit that can capture patient-specific information from various sources, including genomic, proteomic, and clinical information; a feature extraction module that computationally processes the acquired data to determine relevant biomarkers for the drug response; an integrated machine learning processing unit that uses a range of algorithms optimized for pattern recognition in complex biological data based on historical treatment outcomes; an interface for prognostic output, intended to provide oncologists with clinically relevant interpretations and patient-specific treatment recommendations, including a feedback loop for continuous learning that contributes to refining prognostic models for new patients; and a communication subsystem configured for the secure transmission of generated patient data and treatment prognoses to medical professionals.

The invention discloses a lung cancer targeted drug treatment result prediction method based on multi-modal characteristics, and belongs to the technical field of medical image analysis, deep learning and precision medical treatment. According to the method, effective feature extraction is performed on pathological images, CT images and clinical text data by using different encoders, and deep fusion and information complementation of different modal data are realized by designing a multi-modal collaborative modeling module and a cross-modal feature sensing module. Meanwhile, a multi-instance learning method is introduced to better process heterogeneity among different modal data, contribution weights of different instances to an overall prediction result are automatically learned through an attention mechanism, and the capture and expression ability of the model to important features is enhanced. According to the method disclosed by the invention, the accuracy and the stability of prediction of the targeted drug treatment effect of the lung cancer patient are remarkably improved, meanwhile, the calculation complexity of multi-modal data processing is effectively reduced, and the method has important clinical application value and technical innovation.