Embodiments described relate to techniques for identifying and characterizing biological structures using machine learning techniques. These techniques may be employed to enable a device to identify the particular type of tissue and/or cells (e.g., platelets, smooth muscle cells, or endothelial cells) in, for example, a biological structure, which may be a tissue or a lesion of a duct (e.g., vasculature) in an animal (e.g., a human or non-human animal), among other structures. The machine learning techniques may use raw impedance spectroscopy measurement data in addition to values derived from that raw data. In addition, the machine learning techniques may be used to select frequencies at which to measure impedance and select features to extract from the measured impedance at the selected frequencies to arrive at a small set of frequencies that allow for reliable differentiation.

An automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence, comprising: processing a received original electrocardiogram digital signal to obtain heartbeat time sequence data and lead heartbeat data; cutting the lead heartbeat data according to the heartbeat time sequence data to generate lead heartbeat analysis data; performing data combination on the lead heartbeat analysis data to obtain a one-dimensional heartbeat analysis array; performing data dimension amplification and conversion according to the one-dimensional heartbeat analysis array to obtain four-dimensional tensor data; and inputting the four-dimensional tensor data to a trained LepuEcgCatNet heartbeat classification model, to obtain heartbeat classification information. The method overcomes the defect that the conventional method only depends on single lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.

One or more embodiments described herein relate to predicting, using adaptive artificial intelligence techniques, typical and aberrant physiological reactions of a patient to psychiatric counseling. Treatment plans can be determined and calculated based on previously-gathered demographic and/or biometric data, and/or modifications to treatment plans can be determined and/or implemented based on emergent recognition of reaction types, such as reclassifying reactions that would previously have been deemed typical as aberrant (or vice versa).

A method includes receiving multimodal data collected using at least one wearable device during an assessment window. The method also includes extracting biomarker features from the multimodal data, based on changes in the extracted biomarker features. The method also includes detecting that a stress event occurred during the assessment window. The method also includes accessing a plurality of templates of patterns in biomarker features, wherein a first subset of the templates is associated with unhealthy response to stress and a second subset of the templates is associated with healthy response to stress. The method also includes determining whether the stress event corresponds to a healthy response or an unhealthy response based on similarities between a pattern in the extracted biomarker features and the plurality of templates. The method also includes responsive to the stress event corresponding to an unhealthy response, providing a stress management recommendation.

The present disclose generally relates to systems and methods for active titration of one or more cranial or peripheral nerve stimulators to treat obstructive sleep apnea. The active titration can be accomplished in an automated fashion by a closed-loop process. The closed-loop process can be executed by a computing device that includes a non-transitory memory storing instructions and a processor to execute the instructions to perform operations. The operations can include defining initial parameters for the one or more cranial or peripheral nerve stimulators for a patient; receiving sensor data from sensors associated with the patient based on a stimulation with the one or more cranial or peripheral stimulators programmed according to the initial parameters; and adjusting the initial parameters based on the sensor data.

A state assessment system, a diagnosis and treatment system and a method for operating the diagnosis and treatment system are disclosed. An oscillator model converts a physiological signal of a subject into a defined feature image. A classification model analyzes state information of the subject based on the feature image. An analysis model outputs a treatment suggestion for the subject based on the state information of the subject. An AR projection device projects acupoint positions of a human body onto the subject, for the subject to be treated based on the treatment suggestion.

An apparatus for generating a metabolism model may include a processor configured to obtain a predetermined number of bio-information profiles from a bio-sensor, extract a representative bio-information profile from the obtained predetermined number of bio-information profiles, and generate the metabolism model for correcting an error of the bio-sensor by using the extracted representative bio-information profile.

A system for Magnetic Resonance Imaging (MRI) is provided. The system may obtain at least one training sample each of which includes full MRI data. The system may also obtain a preliminary subsampling model and a preliminary MRI reconstruction model. The system may further generate a subsampling model corresponding to an MRI reconstruction model by jointly training the preliminary subsampling model and the preliminary MRI reconstruction model using the at least one training sample. The subsampling model may be the trained preliminary subsampling model, and the MRI reconstruction model may be at least a portion of the trained preliminary MRI reconstruction model.

A powered communication system comprises an improved layout of keys configured to treat, mitigate, or delay the onset and reduce the severity of symptoms of carpal tunnel syndrome (CTS) and other pathologies by reducing movement of a user's fingers during typing. A layout of the powered communication system comprises the most-used letters on a home or center row while retaining a plurality of keys in the same placement as the QWERTY keyboard. The powered communication system further comprises customizable function keys to further reduce finger movement and at least one sensor to monitor a user's health while typing.

Training a machine learning neural network (MLNN) in radiowave based monitoring of fall characteristics in diagnosing injury. The method comprises receiving, in a first set of input layers of the MLNN, from a millimeter wave (mmWave) radar sensing device, a set of mmWave radar point cloud data representing fall attributes associated with a subject, each of the first set associated with a respective fall attribute; receiving, at a second set of input layers of the MLNN, a set of personal attributes of the subject, training a MLNN classifier based on supervised training that establishes a correlation between an injury condition of the subject as generated at the output layer, the mmWave point cloud data, and personal attributes; and adjusting an initial matrix of weights by backpropagation to increase correlation between the injury condition, the mmWave point cloud data, and the personal attributes.