21 results about "Digital organism" patented technology

The disclosed embodiments relate to system, method and apparatus for forming ML sessions so that an AI system can identify, track and diagnose a user through the user's bathroom sessions. As used herein, a session is a period of time during which a user is active in the toilet. The session may include certain inactivity if such inactivity lasts only for a predefined duration. In an exemplary embodiment, a system is configured to capture images of excreta and, using ML algorithms, classifies the toileting session using Digital Biomarkers (DBMs). The ability to create an excreta log to accurately deliver detailed information to doctors and healthcare providers can revolutionize healthcare by notifying when further urine or fecal screening is necessary. The disclosed embodiments of ML and image identification through AI requires no user behavior change and provides a link between medical records and specific excreta patterns.

Embodiments directed to a computer-implemented diagnostic system and method to determine various diagnoses for different users, as well as symptom severity and onset of conditions associated with such diagnoses are described. In one example, a computer-implemented method of diagnosing health conditions for users includes identifying a defined linguistic pattern in digital biomarker data of a user. The method further includes prompting the user to perform a defined screening test based at least in part on identifying the defined linguistic pattern. The method further includes monitoring linguistic performance of the user while the user performs the defined screening test. The method further includes determining a diagnosis for the user based at least in part on the defined linguistic pattern, the linguistic performance, and a test result for the user from the defined screening test.

Systems and methods in accordance with embodiments of the present disclosure include the creation and utilization of synthetic eye and head movement datasets that can be used as digital biomarkers for the screening, diagnosis or monitoring of neurologic diseases, including rare neurologic conditions. The synthetic datasets can be used to train deep learning models that can identify and phenotype neurologic diseases based on distinct eye movement patterns. The system and method create synthetic eye movement datasets using generative Al techniques. A pose-guided video generation framework is utilized to produce synthetic eye movement videos. A latent video diffusion mechanism translates segmented mask inputs — simplified representations of eye movements — into realistic visual sequences that mimic real eye movements associated with specific neurologic diseases.

A wearable device includes an analog-to-digital converter (ADC) configured to digitize biopotential signals received from biopotential-signal-sensing channels. The wearable device also includes a supplementary processor that samples the digital biopotential signals from the ADC. The supplementary processor sends the digital biopotential signals to a buffer until a determination is made that a particular number of the digital biopotential signals is stored in the buffer. Additionally, the supplementary processor transmits an indication to a primary processor that the particular number of the digital biopotential signals is stored in the buffer. In addition to the ADC and the supplementary processor, the wearable device includes the aforementioned primary processor. The primary processor is configured to operate in a low-power mode before receiving the indication and, after receiving the indication, process the particular number of digital biopotential signals from the buffer to detect in-air hand gestures performed by a user of the wearable device.

The present disclosure describes a digitized enabled platform that includes an animal (e.g., an animal) having an integrated camera device. The system includes a cage (e.g., a rodent) residence cage, a cloud-based infrastructure, a machine learning algorithm for generating digital biomarkers, and a user interface for collecting and visualizing continuous indicators of digital biomarkers in rats and mice. Metrics include movement, movement, wheel occupancy, food occupancy and water occupancy, loss of righting reflex, epileptic seizure, gait, upstanding and scratching. The sensor array continuously monitors experimental conditions, and the need for human intervention on a research object is reduced to the maximum extent.

Currently, assessing the severity and progression of symptoms in a subject diagnosed with a muscular disability, in particular SMA involves in-clinic monitoring and testing of the subject every 6 to 12 months. However, monitoring and testing a subject more frequently is preferred, but increasing the frequency of in-clinic monitoring and testing can be costly and inconvenient to the subject. Thus, assessing the severity and progression of symptoms via remote monitoring and testing of the subject outside of a clinic environment as described herein provides advantages in cost, ease of monitoring and convenience to the subject. Systems, methods and devices according to the present disclosure provide a diagnostic for assessing of the axial motor function of a subject having a muscular disability, in particular SMA by active testing of the subject.

Systems and methods for assessing schizophrenia, for example severity of schizophrenia, may use a trained machine learning model. An audio recording of a patient may be processed to determine acoustic data related to the audio recording. The system may determine, for example using the trained machine learning model, a digital biomarker. The digital biomarker may correspond to schizophrenia severity of the patient using the acoustic data. The digital biomarker may correspond to a positive and negative syndrome scale (PANSS) score. For example, the digital biomarker may include an indication of one or more symptoms and / or the severity of schizophrenia.

Impending disruptive behavior in an individual is predicted using a machine learning model that processes measurement recorded with a wearable device. Measurement data are received from a wearable device and input to a trained machine learning model, generating predictive feature data as an output. The predictive feature data may include predictive scores, classifications, or the like, of a likelihood of a subject having a disruptive behavior.

The invention provides a procambarus clarkii individual identification method and system based on biological characteristics, and relates to the technical field of deep learning. Extracting local digital biological characteristics of each slice image and the target image and global digital biological characteristics of the target image, and fusing the local digital biological characteristics and the global digital biological characteristics to obtain a digital biological characteristic matrix; inputting the digital biological characteristic matrix into an improved graph neural network model, respectively generating a query and key through an independent multi-layer perceptron, calculating an edge weight through a zoom dot product of the query and key, and carrying out multi-head attention splicing to obtain a plurality of node characteristics; splicing different node features, generating an attention score for each node based on an aggregation module of a gating mechanism, and outputting a global representation vector of each group of point sets after global pooling operation; and mapping the global representation vector into a fixed dimension through a linear network, embedding the fixed dimension as a final point set, and finally outputting to obtain a procambarus clarkii individual identification result.

The present disclosure provides a method and system for identifying individual procambarus clarkii of biological characteristics, relating to the technical field of deep learning, comprising: extracting local and global digital biological characteristics of each slice image and a target image, and fusing the local and global digital biological characteristics to obtain a digital biological characteristic matrix; inputting the digital biological characteristic matrix into an improved graph neural network model, generating a query and a key through an independent multi-layer perceptron, calculating edge weights through a scaling dot product of the query-key, and obtaining multiple node features after multi-head attention splicing; splicing different node features, generating an attention score for each node based on a gating mechanism aggregation module, and outputting a global representation vector of each group of point sets after global pooling operation; mapping the global representation vector to a fixed dimension through a linear network, and taking it as a final point set embedding, and finally outputting a procambarus clarkii individual identification result.

In one aspect, the disclosure relates to a system comprising a plurality of microposts protruding from a membrane opposite a solid substrate, wherein the system can be pneumatically actuated to bring the microposts closer to the solid substrate. Also disclosed herein are methods of constructing the system, methods of performing digital bioassays using the disclosed system, and noninvasive methods for detecting diseases and for monitoring the progress of disease treatments using the disclosed system. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present disclosure.

Herein is described kinetic assay, in which individual binding events are detected and monitored during sample incubation. This method uses interferometric reflectance imaging to detect thousands of individual binding events across a multiplex solid phase sensor with a large area. A dynamic tracking procedure is used to measure the duration of each event. From this, the total rates of binding and de-binding as well as the distribution of binding event durations are determined. Systems and components for performing the kinetic assay are also described.

The invention provides a distributed node re-evolution system for virtual organism multi-modal data. The system is composed of a data source supply and initial issuing end (A end), a distributed recombination and secondary training node (B end), a server end (S end), a database, a property electronic account book and the like. The end A is responsible for collecting user multi-modal biological data and setting a distributed node mode and weight distribution; the B end executes distributed recombination and secondary training; and the S end processes data, trains a model and manages resources. According to the system, firstly, a first-level user creates a project, organism multi-modal data is uploaded to a server, and an initial weight ratio of distributed nodes is set; and the server records the information, reconstructs a three-dimensional model module and generates a voice clone and dialogue model. And the secondary user performs distributed recombination and continuous evolution through the B end to generate an iterable digital human or digital biological bifurcation. The system supports secondary modification and re-evolution of digital persons or digital organisms in ownership, inheritance and transaction of the ownership and continuous evolution of organism data are achieved, and diversified evolution of virtual organisms is promoted through deconstruction, reconstruction, distributed recombination, feedback and retraining.

The present disclosure relates to digital therapeutics (DTx) for the purpose of myopia treatment including inhibiting myopia progression. The system may comprise: a measurement module for detecting eye movement of a user who is a myopia treatment subject; a collection module for collecting eye movement data related to the eye movement of the user recognized by the measurement module; a processing module for calculating a first biomarker on the basis of the collected eye movement data; and a digital task generation module for generating a first digital task on the basis of the first biomarker and providing the first digital task to the user in order to inhibit myopia progression and treating myopia in the user.

To provide systems and methods suitable for delivery of digital biomarkers and genomic panels.SOLUTION: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, a related case, a patient, and / or a plurality of pieces of clinical information, determining one or more of a prediction, a recommendation, and / or a plurality of pieces of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements, and determining, based on the prediction, the recommendation, and / or the plurality of pieces of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.SELECTED DRAWING: None

The disclosed embodiments relate to system, method and apparatus for forming ML sessions so that an AI system can identify, track and diagnose a user through the user's bathroom sessions. As used herein, a session is a period of time during which a user is active in the toilet. The session may include certain inactivity if such inactivity lasts only for a predefined duration. In an exemplary embodiment, a system is configured to capture images of excreta and, using ML algorithms, classifies the toileting session using Digital Biomarkers (DBMs). The ability to create an excreta log to accurately deliver detailed information to doctors and healthcare providers can revolutionize healthcare by notifying when further urine or fecal screening is necessary. The disclosed embodiments of ML and image identification through AI requires no user behavior change and provides a link between medical records and specific excreta patterns.

This document relates to a computing module that is configured to generate a cognitive score for a user. The score is determined by a trained machine learning model, which is configured to process inputs generated by the user as the user completes a plurality of digital tasks that are accessible through a graphical user interface module communicatively connected to the computing module. The machine learning model used in the computing module was trained based on training data that had been validated with corresponding near-infrared spectroscopy (fNIRS) data.

To provide non-invasive and inexpensive digital biomarkers. [Solution] The server of the disease state estimation system stores a database containing data about the user, and as a function, it repeatedly executes the following steps at predetermined intervals for a predetermined number of times: acquiring user identification data (S101), presenting predetermined questions to the user to visualize the state of a predetermined disease (S102), receiving input of answers to the questions from the user (S103), and storing answer data indicating the content of the received answers in the database linked to the user identification data (S104). After a predetermined number of times, it executes the following steps: acquiring answer data stored in the database for a predetermined number of times or more, estimating a predetermined disease state based on the answer data for a predetermined number of times or more, and outputting the estimated result of the predetermined disease state.

Provided herein are systems and methods for creating and measuring digital biomarkers for knee and / or hip osteoarthritis, as well as systems and methods for using such biomarkers in the diagnosis, monitoring, and treatment of knee and / or hip osteoarthritis. In particular aspects, the systems and methods involve using at least one primary device comprising one or more capacitor-pressure sensors configured to generate raw gait data, and a remote processing device configured to generate one or more gait parameters for an individual based on the gait data, and apply one or more trained machine learning models to the gait parameters. In further aspects, systems and methods for training said machine learning models configured to generate a digital biomarker are also disclosed.

The present disclosure provides a system for generating a multi-component senescence index for an individual companion animal, the system including digital biomarkers, biological biomarkers, and subjective assessment methods to predict the phenotypic age and phenotypic age acceleration / deceleration (phenotypic age higher or lower than actual age) of dogs and cats. The disclosure also provides a method for slowing phenotypic aging in a companion animal in need thereof. The method includes determining an index according to one aspect of the present disclosure and providing personalized health, diet, and nutritional measures for a companion animal according to the index of the companion animal. The method can also be considered to be a method for improving accelerated aging of animals. In some embodiments, personalized health measures include diets that decelerate phenotypic aging and / or improve accelerated phenotypic aging.

Aspects described herein relate to the field of disease tracking and diagnostics. Specifically, they relate to a method of assessing a muscular disability and, in particular, spinal muscular atrophy (SMA) in a subject comprising the steps of determining at least one parameter from a dataset of sensor measurements of the subject using a mobile device, and comparing the determined at least one parameter to a reference, whereby the muscular disability and, in particular, SMA will be assessed. Aspects described herein also relate to a mobile device comprising a processor, at least one pressure sensor and a database as well as software which is tangibly embedded to said device and, when running on said device, carries out the method of the invention as well as the use of such a device for assessing a muscular disability and, in particular, SMA.