202 results about "Disease status" patented technology

A computer-implemented method for processing medical images may comprise: receiving image data for a patient; based on the received image data, determining: a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric, and at least one other patient-specific metric, and using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

A computer-implemented method for processing medical images may comprise: receiving image data for a patient; based on the received image data, determining: a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric, and at least one other patient-specific metric, and using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and / or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and / or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and / or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Provided herein are methods and systems for diagnosing pathological conditions using machine learning and artificial intelligence models. An exemplary method can include loading an abundance matrix that represents mass spectrometry reads of a sample of a plurality of volatile organic compounds (VOCs) extracted from a biological sample. The abundance matrix can represent each of the plurality of VOCs in a mass-to-charge ratio dimension, an abundance dimension, and a retention time dimension. The method can include providing the abundance matrix to a machine-learning model. The machine- learning model can be trained on abundance matrixes of biomarkers of VOCs collected from healthy and diseased subjects. The method can include receiving, from the machine-learning model, a prediction of a state of a disease state.

The invention relates to the technical field of image detection, in particular to a pest and disease identification method and system based on unmanned aerial vehicle remote sensing. The method comprises the following steps: acquiring health indexes of each plant; marking suspicious points; spraying preset hormones to the suspicious points; after a first preset time interval, obtaining the health index of the hormone-induced plant again, and distinguishing whether the plant is pathological abnormality or physiological abnormality according to the change of the health index; on the basis of the pathology and physiology distinguishing result, a definite diagnosis threshold value in a preset range of the suspicious point is adjusted, so that when the plant at the suspicious point is pathologically abnormal and the health indexes of other plants in the preset range adjacent to the suspicious point are abnormal, the condition that the other plants are pathologically abnormal can be detected more sensitively; and using the adjusted pest and disease damage early warning threshold value to identify pest and disease damage conditions of plants of which suspicious points are adjacent to a preset range. According to the invention, the pest and disease states of crops can be detected more accurately.

A method for classifying neurological disease status is described. The method includes acquiring, by a data preprocessor logic, patient image data. The method further includes generating, by a trained artificial neural network (ANN), a classification output based, at least in part, on the patient image data. The classification output corresponds to a neurological disease status of the patient. The trained ANN is trained based, at least in part, on longitudinal source data.

Systems and methods are disclosed herein for classifying one or more disease conditions. In some embodiments, an application stores a common extraction model, the common extraction model trained using training examples for a plurality of diseases. The application stores a plurality of disease classifiers, each disease classifier configured to output whether or not its respective disease is present, each disease classifier trained using training examples for its respective disease. The application receives a selection of a disease and selects a disease classifier from the plurality of disease classifiers corresponding to the disease. The application inputs an image into the common extraction model and receives, as output from the common extraction model, a set of biomarkers extracted from the image. The application inputs the set of biomarkers into the selected disease classifier, the selected disease classifier configured to output whether or not the disease is present in the image.

A method comprising obtaining a bodily fluid from a subject; contacting the bodily fluid with an adsorbent material comprising a synthetic carbon particle (SCP) to produce a first filtrate having a level of disease mediators (y); contacting the first filtrate with an adsorbent material comprising the SCP and an anion exchange resin where the ratio of SCP to anion exchange resin is in a range from about 0.1:100 to 100:0.1 to produce a second filtrate; contacting the second filtrate with an adsorbent material comprising the SCP and a cation exchange resin where the ratio of SCP to cation exchange resin is in a range from about 0.1:100 to 100:0.1 to produce a third filtrate.

Devices, systems and methods are provided to assist with the monitoring or management of a patient's medical condition, which have one or more sensors sensing individual patient data on or near the patient. This individual patient data corresponds to at least one physiological parameter of the patient and includes a sensor that does not require the patient to apply it or activate it. The data is then transmitted to a processor for computing a risk or status signal that is based on comparison from a baseline related to a patient or related population and an alert or alarm can be generated based on the result of the signal.

Described herein are methods for screening for a disease state. The method may include obtaining multiple data sets and identifying the disease state based on a combination of the data sets. The data sets may include biomolecule measurements obtained by multiple methods, such as through the use of particles and reference biomolecules.

The invention provides an experimental dyeing result image processing and calculating method. The method comprises the following steps: identifying a dyeing positive region in a preprocessed tissue sample digital image by using a first identification model, and carrying out area quantification on the identified dyeing positive region to obtain positive region area data; using a second recognition model to recognize and extract the intracellular cavitation region in the preprocessed tissue sample digital image, and performing area quantification on the extracted intracellular cavitation region to obtain cavitation region area data; calculating a dyeing positive region area proportion based on the positive region area data, and calculating a cavitation region area proportion based on the cavitation region area data; and inputting the area proportion of the dyeing positive region and the area proportion of the cavitation region into a preset disease evaluation model, and outputting a disease state evaluation result. According to the method, the accuracy and comprehensiveness of experimental dyeing image analysis are improved, and the automation degree and result credibility of pathological analysis can be remarkably improved.

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.

A method is disclosed of processing a set of images. Each image in the set has an associated counterpart image. One or more regions of interest (ROIs) are identified in one or more of the images in the set of images. For ROI identified, a reference region is identified in the associated counterpart image. ROIs and associated reference regions are cropped out, thereby forming cropped pairs of images 1 . . . n1, that are fed to a deep learning model trained to make a prediction of probability of a state of the ROI, e.g., disease state, which generates a prediction Pi-, (i=1 . . . n) for each cropped pair. The model generates an overall prediction P from each of the predictions Pi. A visualization of the set of medical images and the associated counterpart images including the cropped pair of images is generated.

A method for processing a sparsely populated data source, method for generating a training set for training a model to predict mitral regurgitation from echocardiograph data, and method of predicting heart failure from echocardiograph data including the steps of: retrieving echocardiograph measurement data from a plurality of patient records comprising echocardiography reports; analysing the echocardiograph data to determine unpopulated data fields; populating the unpopulated data fields with imputed echocardiograph data determined by a machine learning model; calculating a probability output from a trained model; analysing echocardiograph measurement data of individual patient records from the echocardiograph data to determine a prediction of the presence of a disease state in the patient on the basis of the calculated probability output; and associating the presence of the disease state to a prediction of heart failure in the patient.

The invention discloses a co-disease collaborative identification and risk early warning method based on multi-modal data and an adaptive large model. The method comprises the following steps: collecting multi-modal medical data; carrying out preprocessing and feature extraction on the multi-modal medical data; based on the fine-tuned adaptive large model, deep interactive fusion is carried out on the extracted multi-modal features, potential association among different data sources and mutual influence and synergistic effect among various diseases are learned, and combined judgment and future onset risk prediction of the current common disease state of the patient are output; determining a specific co-disease combination and a co-disease risk level based on the combined judgment of the current co-disease state of the patient and a future onset risk prediction result; and when the determined co-disease risk level is equal to or higher than an early warning threshold, generating a collaborative early warning and intervention suggestion. According to the invention, a cross-modal and cross-disease feature fusion technology can be utilized to realize conjoint analysis and comprehensive risk early warning of multiple disease states of the patient, and the co-disease management efficiency and the clinical auxiliary decision-making level are significantly improved.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific organs and / or tissue. In certain embodiments, the accurate identification of one or more such volumes can be used to determine quantitative metrics that measure uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Alzheimer's disease, the most common cause of dementia in older individuals, is a debilitating neurodegenerative disease for which there is currently no cure. In the past, AD could only be definitively diagnosed by brain biopsy or upon autopsy after a patient died. These methods, which demonstrate the presence of the characteristic plaque and tangle lesions in the brain, are still considered the gold standard for the pathological diagnoses of AD. However, in the clinical setting brain biopsy is rarely performed and diagnosis depends on a battery of neurological, psychometric and biochemical tests, including the measurement of biochemical markers such as the ApoE and tau proteins or the beta-amyloid peptide in cerebrospinal fluid and blood. The present invention discloses and describes panels of makers that are differentially expressed in the disease state relative to their expression in the normal state and, in particular, identifies and describes panels of makers associated with neurocognitive disorders. Such biomarker panel might have considerable value in triaging patients with early memory disorders to yet more specific but more invasive and costly approaches such as molecular markers in CSF and on PET imaging in clinical trials and possibly in clinical practice.

The invention belongs to the technical field of osteoarthritis management, and particularly relates to an osteoarthritis management system based on artificial intelligence and a computer storage medium. The invention provides an osteoarthritis management system based on artificial intelligence. The system integrates an LLM-DL hybrid architecture, a multi-specialist module cooperation framework and an RAG knowledge base system, and can cover a complete clinical process from patient preliminary diagnosis to treatment scheme formulation. The system can comprehensively and accurately evaluate the disease state of a patient, provides personalized disease progress prediction, not only predicts 2-year and 4-year functional results and iconography progress, but also can identify specific risk factors of the patient, and provides a basis for accurate intervention. Through the design of the whole system, the clinical working efficiency and the prediction accuracy are remarkably improved, the dependence on specialist doctors and high-end equipment is reduced, high-quality KOA management can also be implemented in resource limited areas, and therefore the overall medical resource requirement is effectively reduced.

The invention discloses a disease-related anomaly localization protein prediction method based on deep learning, and the method comprises the steps: carrying out the protein prediction based on the proteomics expression data of normal and disease samples and a known protein interaction network in a normal state; respectively constructing a protein interaction network under the activity characteristics of the sample pathway and the disease state; the proteomics expression data and the pathway activity characteristics are fused through a cross attention mechanism, and protein characterization characteristics with pathway perception ability are constructed; respectively predicting protein subcellular localization under the normal and disease states by using a graph attention network model based on the protein characterization characteristics and the protein interaction network corresponding to the normal and disease states; disease-related abnormal localization proteins are identified by comparing predicted protein subcellular localization in normal and disease states. The method can efficiently and accurately identify the abnormal localization protein related to the disease, and has important scientific research value and application prospect.

A method of automatically diagnosing pneumonia in a patient includes obtaining values of two or more diagnostic parameters of the patient from a caregiver of the patient using an input / output interface device. The method includes applying, using a processor connected to an input / output interface, two or more diagnostic parameters to an electronic memory storing a plurality of pre-compiled pneumonia diagnostic models to identify an optimal diagnostic model for performing the diagnosis. The values of the two or more diagnostic markers are applied to the identified optimal diagnostic model to generate a diagnostic output. An input / output interface device is operated according to the diagnostic output to indicate to the caregiver the presence or absence of pneumonia in the patient. The caregiver may use the diagnostics to provide appropriate care for the patient. A pneumonia diagnostic model is from investigations of pneumonia-positive and non-pneumonia population of subjects.

A system and method for rendering a diseased organ implant eligible, the method including acquiring an image data set of a potential donor organ, identifying one or more disease states in the potential donor organ, wherein the one or more disease states render the organ ineligible for transplant, harvesting the potential donor organ, applying histotripsy therapy, and confirming elimination / reduction of the disease state in the potential donor organ.

To provide a new method, model, program or the like for acquiring information usable for a disease diagnosis or prognosis prediction by using a sample taken from lungs or exhaled breath.SOLUTION: A method for assisting disease diagnosis or prognosis prediction including: acquiring (A) extracellular particle information on a population of extracellular particles collected from lungs or exhaled breath of a subject; the extracellular particle information including scattered light information on scattered light of irradiated light about each extracellular particle of the population of extracellular particles, and light emission information on a plurality of light emission whose peak wavelengths are mutually different, caused from a component of the extracellular particles or a labelling substance connected to the component due to the irradiated light; and (B) disease information on a state of a disease in the subject from each subject included in a group of subjects; and acquiring information for assisting the disease diagnosis or the prognosis prediction related to the extracellular particles collected from the lungs or the exhaled breath by machine learning on the basis of a data set including the extracellular particle information and the disease information.SELECTED DRAWING: Figure 1

Some embodiments relate to nanoparticle probes for the detection of disease states in a patient or for tissue engineering. In some embodiments, the nanoparticle probe comprises one or more slip bonds that bind to a cell surface structure. In some embodiments, the binding of the nanoparticle probe is selective. In some embodiments, the nanoparticle probe binds to cells having a certain maximum glycocalyx thickness.

The invention relates to a multi-parameter transcranial magnetic stimulation quantitative analysis method, which comprises the following steps of: acquiring neuron activity index data, and performing standardization processing on the neuron activity index data to generate a first data set F1; identifying the acquired data by using a DBSCAN clustering algorithm and removing noise points to obtain a second data set F2 only containing core points and boundary points; and S3, calculating a distance metric and a link distance based on the second data set F2 obtained in the step S2, applying a hierarchical clustering algorithm to obtain a clustering tree diagram, and determining a classification threshold and performing classification according to the form of the clustering tree diagram and the requirements of a five-level quantitative classification system. According to the quantitative analysis method, the parameters such as the stimulation intensity, the frequency and the pulse mode are quantitatively graded, so that a clinician can more accurately select a proper treatment scheme according to the specific conditions such as the age, the gender and the disease state of a patient.

Disclosed herein are systems and methods for determining a disease state of an individual. The systems can be configured to perform the methods disclosed herein, and these methods can include receiving sequencing data for one or more cell-free nucleic acid fragments obtained or derived from a biological sample of the individual. These methods can also include determining a methylation profile for the individual based on identifying abnormal patterns of methylation features. A trained machine learning model can be used to generate an indication of the disease state.

A method and system for diagnosing a subject with respect to a nasopharyngeal carcinoma (NPC) disease state. Peptide structure data corresponding to a biological sample obtained from the subject is received. The peptide structure data is analyzed using a supervised machine learning model to generate a disease indicator that indicates whether biological sample evidences the NPC disease state based on at least 3 peptide structures selected from a group of peptide structures identified in Table 1A and / or 1B. The group of peptide structures in Table 1A and / or 1B comprises a group of peptide structures associated with the NPC disease state. The group of peptide structures is listed in Table 1A and / or 1B with respect to relative significance to the disease indicator. A diagnosis output is generated based on the disease indicator.

The invention provides a prawn disease-oriented intestinal tract key metabolite design method and device, and relates to the technical field of strategy optimizing.The prawn disease-oriented intestinal tract key metabolite design method comprises the steps that prawn intestinal tract metabolite data are obtained, a random forest model distinguishes prawn health conditions through intestinal tract metabolite, and indication intestinal tract metabolite for distinguishing the health conditions is determined; the importance of the distinguishing metabolites is quantified through an MDA algorithm, and the most important distinguishing metabolite combination is screened; and distinguishing the metabolite and converting the metabolite concentration into a weighted undirected network, obtaining the feature vector centrality of each metabolite in the molecular ecological network, and determining a key metabolite. According to the invention, the random forest and the molecular ecological network are combined to identify key metabolites in a disease state, and the relative importance of each variable to a classification result is quantified. Meanwhile, the proportion of each key metabolite is determined by tracing back to healthy prawn intestinal tracts, and diseases are prevented and controlled symptomatically by compensating the combination of the inhibited key metabolite in the disease prawns.