Docbot, Inc.

Satisfai Health Inc.

Andrew Ninh

Peter Andrew Crosby

William E Karnes

Tyler Dao

John Cifarelli

Mohammad Fidaali

Gabriele Zingaretti

James Requa

Efren Rael

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.

Real-time deployment of machine learning systems

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.

System and methods for enhanced automated endoscopy procedure workflow

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.

Endoscopic system and methods having real-time medical imaging

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for real-time deployment of machine learning systems. 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 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.

Growing community of inventors

Fountain Valley, CA, United States of America

Andrew Ninh