Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include electrodes coupled to an implantable pulse generator (IPG), a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include one or more electrode leads coupled to an implantable pulse generator (IPG) and a tunneler system for subcutaneously implanting a proximal portion of the lead(s). The system may also include a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include electrodes coupled to an implantable pulse generator (IPG), a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: Systems and methods for improving endoscopy procedures are described that provide not only a conventional real time image of the view obtained by an endoscope, but in addition, a near real time 3D model and/or a 2D flattened image of an interior surface of an organ, which model and image may be processed using AI software to highlight potential tissue abnormalities for closer examination and/or biopsy during the procedure. A navigation module interacts with other system outputs to further assist the endoscopist with navigational indicia, e.g., landmarks and/or directional arrows, that enhance the endoscopists' spatial orientation, and/or may provide navigational guidance to the endoscopist to assist manipulation of the endoscope.

Abstract: Systems and methods are provided for delivering consistent high quality, cost efficient results in fixed or mobile endoscopy facilities, without requiring the continuous real-time involvement of a fellowship trained gastroenterologist, by integrating patient specific information into decision support systems and AI/machine learning systems employed during the planning and examination phases of the endoscopy procedure.

Abstract: A system and methods are provided in which an artificial intelligence inference module identifies targeted information in large-scale unlabeled data, wherein the artificial intelligence inference module autonomously learns hierarchical representations from large-scale unlabeled data and continually self-improves from self-labeled data points using a teacher model trained to detect known targets from combined inputs of a small hand labeled curated dataset prepared by a domain expert together with self-generated intermediate and global context features derived from the unlabeled dataset by unsupervised and self-supervised processes. The trained teacher model processes further unlabeled data to self-generate new weakly-supervised training samples that are self-refined and self-corrected, without human supervision, and then used as inputs to a noisy student model trained in a semi-supervised learning process on a combination of the teacher model training set and new weakly-supervised training samples.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include electrodes coupled to an implantable pulse generator (IPG), a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include one or more electrode leads coupled to an implantable pulse generator (IPG) and a tunneler system for subcutaneously implanting a proximal portion of the lead(s). The system may also include a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include electrodes coupled to an implantable pulse generator (IPG), a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: Systems and methods are provided for delivering consistent high quality, cost efficient results in fixed or mobile endoscopy facilities, without requiring the continuous real-time involvement of a fellowship trained gastroenterologist, by integrating patient specific information into decision support systems and AI/machine learning systems employed during the planning and examination phases of the endoscopy procedure.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include one or more electrode leads coupled to an implantable pulse generator (IPG) and a tunneler system for subcutaneously implanting a proximal portion of the lead(s). The system may also include a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: A system and methods are provided in which an artificial intelligence inference module identifies targeted information in large-scale unlabeled data, wherein the artificial intelligence inference module autonomously learns hierarchical representations from large-scale unlabeled data and continually self-improves from self-labeled data points using a teacher model trained to detect known targets from combined inputs of a small hand labeled curated dataset prepared by a domain expert together with self-generated intermediate and global context features derived from the unlabeled dataset by unsupervised and self-supervised processes. The trained teacher model processes further unlabeled data to self-generate new weakly-supervised training samples that are self-refined and self-corrected, without human supervision, and then used as inputs to a noisy student model trained in a semi-supervised learning process on a combination of the teacher model training set and new weakly-supervised training samples.

Abstract: Systems and methods are provided for delivering consistent high quality, cost efficient results in fixed or mobile endoscopy facilities, without requiring the continuous real-time involvement of a fellowship trained gastroenterologist, by integrating patient specific information into decision support systems and AI/machine learning systems employed during the planning and examination phases of the endoscopy procedure.

Abstract: Systems and methods for improving endoscopy procedures are described that provide not only a conventional real time image of the view obtained by an endoscope, but in addition, a near real time 3D model and/or a 2D flattened image of an interior surface of an organ, which model and image may be processed using AI software to highlight potential tissue abnormalities for closer examination and/or biopsy during the procedure. A navigation module interacts with other system outputs to further assist the endoscopist with navigational indicia, e.g., landmarks and/or directional arrows, that enhance the endoscopists' spatial orientation, and/or may provide navigational guidance to the endoscopist to assist manipulation of the endoscope.

Abstract: A system and methods are provided in which an artificial intelligence inference module identifies targeted information in large-scale unlabeled data, wherein the artificial intelligence inference module autonomously learns hierarchical representations from large-scale unlabeled data and continually self-improves from self-labeled data points using a teacher model trained to detect known targets from combined inputs of a small hand labeled curated dataset prepared by a domain expert together with self-generated intermediate and global context features derived from the unlabeled dataset by unsupervised and self-supervised processes. The trained teacher model processes further unlabeled data to self-generate new weakly-supervised training samples that are self-refined and self-corrected, without human supervision, and then used as inputs to a noisy student model trained in a semi-supervised learning process on a combination of the teacher model training set and new weakly-supervised training samples.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include electrodes coupled to an implantable pulse generator (IPG), a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include one or more electrode leads coupled to an implantable pulse generator (IPG) and a tunneler system for subcutaneously implanting a proximal portion of the lead(s). The system may also include a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: System and method for rehabilitating a muscle and monitoring such rehabilitation are provided, the system including a user input receiver for receiving stimulation parameter inputs from a user, a stimulator for generating stimulations to be applied to a patient's body based on the stimulation parameter inputs, a signal receiver to receive, detect, and record a signal containing an evoked potential generated by the body in response to the stimulations, a signal processor for processing the recorded signal, for example, by amplifying, filtering, digitizing and temporal averaging the recorded signal, a trigger detector to alert the signal processor module when stimulations are generated to enable synchronization of the response signal with the stimulus for accurate temporal averaging, and an output display for providing data representative of the evoked potential to the user.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include one or more electrode leads coupled to an implantable pulse generator (IPG) and a tunneler system for subcutaneously implanting a proximal portion of the lead(s). The system may also include a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Abstract: A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include one or more electrode leads coupled to an implantable pulse generator (IPG) and a tunneler system for subcutaneously implanting a proximal portion of the lead(s). The system may also include a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.