Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: A system and method for modulating delivery of remote therapy to a patient having an implantable medical device (IMD). Upon establishing a remote care session between a patient controller device and a clinician programmer, wherein the clinician and the patient are remotely located with respect to each other, a determination may be made if a first triggering event is detected. Responsive to the determination, a remote therapy session used for programming the patient's IMD is paused and one or more remote care therapy setting controls provided at the clinician programmer device to facilitate one or more adjustments with respect to the patient's IMD are disabled. Subsequently, responsive to detecting a second triggering event, the remote therapy session with the patient may be resumed, wherein the one or more remote care therapy setting controls of the clinician programmer device are enabled.

Abstract: A system and method for facilitating DBS electrode trajectory planning using a machine learning (ML)-based feature identification scheme configured to identify and distinguish between various regions of interest (ROIs) and regions of avoidance (ROAs) in a patient's brain scan image. In one arrangement, standard orientation image slices as well as re-sliced images in non-standard orientations are provided in a labeled input dataset for training a CNN/ANN for distinguishing between ROIs and ROAs. Upon identification of the ROIs and ROAs in the patient's brain scan image, an optimal trajectory for implanting a DBS lead may be determined relative to a particular ROI while avoiding any ROAs.

Abstract: The present disclosure provides systems and methods for labeling data in an active implantable medical device system. The method includes capturing data associated with a remote therapy session between a patient device and a clinician device, prompting, via a user interface, a user to label the captured data in response to the user selecting, via the user interface, at least one of i) an image of a patient displayed on the user interface, ii) a programming setting displayed on the user interface, and iii) an affected body area displayed on the user interface, receiving, in response to the prompting, via the user interface, a user input associated with the captured data, generating, based on the user input, a label associated with the captured data, and storing the generated label in association with the captured data.

Abstract: The present disclosure provides systems and methods for improving quality of a remote therapy session. A method includes capturing, using a computing device, video data associated with a remote therapy session between a patient device and a clinician device, applying, using the computing device, one or more machine learning algorithms to the captured video data to detect a quality issue associated with the remote therapy session, the quality issue related to one of i) a field of view associated with the video data, ii) a luminance associated with the video data, and iii) a contrast associated with the video data, and performing, using the computing device, a remedial action to address the detected quality issue in the video data.

Abstract: The present disclosure provides systems and methods for estimating an orientation of an implanted deep brain stimulation (DBS) lead. Such methods include generating an initial image dataset, down-sampling a respective image or adding noise to images of the subset of the initial image dataset, and re-slicing at least a subset of the modified image dataset along an alternative primary imaging axis, to generate an integrated image dataset. The method also include partitioning the integrated image dataset into a preliminary training image dataset and a testing image dataset, and re-sizing at least a subset of the preliminary training image dataset with a localized field of view around a depicted DBS lead, to generate a training image dataset. The method further includes training a machine-learning model using the training image dataset, and executing the trained machine-learning model to estimate, during a DBS implantation procedure, an orientation of a subject implanted DBS lead.

Abstract: Provided herein is a computing system for optimizing a waveform, in communication with an implantable pulse generator, and including a computing device including a memory device and a processor communicatively coupled to the memory device. The processor is configured to: retrieve historical waveform data associated with a plurality of waveforms used in therapeutic sessions for a plurality of patients, the historical waveform data including a plurality of waveform parameters; analyzing the historical waveform data to determine preferred waveform parameters; determining that a patient is starting a new therapeutic session using the patient therapeutic device; displaying each of the preferred waveform parameters; prompting the user to accept or modify the displayed waveform parameters; optimizing the waveform parameters for the therapeutic session; and transmitting the optimized waveform parameters to the patient therapeutic device to start the therapeutic session.

Abstract: The present disclosure provides systems and methods for generating pulsed waveforms that approximate colored noise for use in a neurostimulation system. An implantable neurostimulation system includes an implantable stimulation lead including a plurality of contacts, and an implantable pulse generator communicatively coupled to the stimulation lead. The pulse generator is configured to generate a pulsed waveform that approximates colored noise using at least one of a thresholding method and an optimization method, and cause stimulation to be delivered by the stimulation lead based on the pulsed waveform.

Abstract: The present disclosure provides systems and methods for generating burst waveforms. An implantable neurostimulation system includes an implantable stimulation lead including a plurality of contacts, and an implantable pulse generator communicatively coupled to the stimulation lead. The pulse generator is configured to generate a waveform including a burst that includes a leading anodic pulse followed by alternating cathodic pulses and anodic pulses, each cathodic pulse in the burst having a greater amplitude than the previous cathodic pulse.

Abstract: A system and method for modulating delivery of remote therapy to a patient having an implantable medical device (IMD). Upon establishing a remote care session between a patient controller device and a clinician programmer, wherein the clinician and the patient are remotely located with respect to each other, a determination may be made if a first triggering event is detected. Responsive to the determination, a remote therapy session used for programming the patient's IMD is paused and one or more remote care therapy setting controls provided at the clinician programmer device to facilitate one or more adjustments with respect to the patient's IMD are disabled. Subsequently, responsive to detecting a second triggering event, the remote therapy session with the patient may be resumed, wherein the one or more remote care therapy setting controls of the clinician programmer device are enabled.