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: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for real-time deployment of machine learning networks. One of the operations is performed by the system receiving video data from a video image capturing device. The received video data is converted into multiple video frames. These video frames are encoded into a particular color space format. The system renders a first display output depicting imagery from the multiple encoded video frames. The system performs an inference on the video frames using a machine learning network in order to determine the occurrence of one or more objects in the video frames. The system renders a second display output depicting graphical information corresponding to the determined one or more objects from the multiple encoded video frames. The system then generates a composite display output including the imagery of the first display output overlaid with the graphical information of the second display output.

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.