A method for determining an abnormality in a medical device from a medical image is provided. The method for determining an abnormality in a medical device comprises receiving a medical image, and detecting information on at least a part of a target medical device included in the received medical image.

Methods and apparatuses for training a magnetic resonance imaging model, electronic devices and computer readable storage media are provided. A method may include: acquiring a magnetic resonance image data set; constructing a ring deep neural network to be trained; inputting an under-sampled magnetic resonance image and a full-sampled magnetic resonance image respectively to two neural networks included in the ring deep neural network, to generate respective simulated magnetic resonance images; inputting a first simulated full-sampled magnetic resonance image and the full-sampled magnetic resonance image to a pre-constructed first simulated magnetic resonance image class discrimination model, to obtain a first discrimination result indicating whether or not the first simulated full-sampled magnetic resonance image is of a simulated magnetic resonance image class; and adjusting a network parameter of the ring deep neural network based on a preset loss function, to obtain a trained magnetic resonance imaging model.

The present invention relates to an apparatus for task determination during brain activity analysis. The apparatus comprises an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to provide the processing unit with measurement data of the brain of a patient performing a task during brain activity analysis. The processing unit is configured to determine a measure of brain activity based on the measurement data of the brain. The processing unit is configured to determine: that the patient should perform a different task to the task they are currently performing and select the different task; or that the patient should continue performing the task that they are currently performing; or that the patient should stop performing the task; The determination comprises utilization of information relating to the task and the determined measure of brain activity and information relating to a plurality of reference tasks and associated plurality of reference measures of brain activity. The output unit is configured to output an indication that the patient should perform the different task, that the patient should continue performing the task, or that the patient should stop performing the task.

A system and method for promoting, tracking, and assessing mental wellness. The method includes receiving an entry from a subject user, the entry including an input and a mood indicator, storing the entry in within a set of entries, the set including at least two entries received over a period of time, and determining a presence of at least one marker in the input of each entry within the set. The method further includes analyzing the set of entries for occurrences of markers or sequences of markers and alerting a supervisory user if the occurrences of markers or sequences of markers exceed a predetermined threshold. The method further includes associating contextual content from a supervisory user to an entry, the contextual content including a note, an attachment, a form, and/or a flag. The system includes a platform for accessing, managing, and storing data and analytics for implementing the method.

The present invention relates to a breath analyzing system and method. In particular the invention relates to a breath analyzing system and method arranged to provide tracer-aided classification of the presence of a breath intoxicating substance above a limit concentration and providing status to a user about the progression of the classification. The method/system detects a peak in the tracer signal and defines an evaluation period corresponding to the duration of the peak. Measurements classification of the concentration of the intoxicating substance is used for the evaluation period, and if required to achieve a result, for a plurality of evaluation periods.

Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

A system and method for generating a fusion of volumetric images and surface scan images said system comprising: a processor configuring the system to: receive both a volumetric image tooth mesh and surface scan image tooth crown mesh from a same patient, registered to a similar coordinate system; segment by anatomical structure each of the registered meshes that are in common between each of the registered volumetric image tooth mesh and the surface scan tooth crown mesh; and recognize a fusion vertices for each of the segmented volumetric image tooth mesh and segmented surface scan tooth crown mesh for matching the recognized meshes; remove a surface fragment from the matched volumetric image mesh in common with the matched surface scan image mesh for removal from the volumetric image mesh; and fuse the meshes by triangulating the recognized fusion vertices.

A medical device is configured to detect an atrial tachyarrhythmia episode. The device senses a cardiac signal, identifies R-waves in the cardiac signal attendant ventricular depolarizations and determines classification factors from the R-waves identified over a predetermined time period. The device classifies the predetermined time period as one of unclassified, atrial tachyarrhythmia and non-atrial tachyarrhythmia by comparing the determined classification factors to classification criteria. A classification criterion is adjusted from a first classification criterion to a second classification criterion after at least one time period being classified as atrial tachyarrhythmia. An atrial tachyarrhythmia episode is detected by the device in response to at least one subsequent time period being classified as atrial tachyarrhythmia based on the adjusted classification criterion.

A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.

A wireless, patient-worn, physiological sensor configured to, among other things, help manage a patient that is at risk of forming one or more pressure ulcers is disclosed. According to an embodiment, the sensor includes a base having a top surface and a bottom surface. The sensor also includes a substrate layer including conductive tracks and connection pads, a top side, and a bottom side, where the bottom side of the substrate layer is disposed above the top side of the base. Mounted on the substrate layer are a processor, a data storage device, a wireless transceiver, an accelerometer, and a battery. In use, the sensor senses a patient's motion and wirelessly transmits information indicative of the sensed motion to, for example, a patient monitor. The patient monitor receives, stores, and processes the transmitted information.