Abstract: A system and method for processing a video stream captured during a clinical procedure. A method includes: receiving and forwarding a current frame to a display using a first processing thread; forwarding the current frame to a memory buffer; using a second processing thread to check for a new frame in the memory buffer; using the second processing thread to forward the new frame to a cloud service that detects lesions in image data; receiving a result from the cloud service; in response to the result being indicative of a new detected lesion in the new frame, performing object sizing using the second processing thread and forwarding new lesion information to the first processing thread; and graphically overlaying the new lesion information onto the current frame at the display manager.

Abstract: An AI based platform for processing information collected during a medical procedure. A method includes capturing images and speech during a medical procedure; processing the images using a trained classifier to identify image-based quality-of-care indicators (QIs); converting the speech into text; parsing the text into sentences; performing a search and replace on predefined text patterns in the sentences; identifying text-based QIs in the sentences; classifying sentences into sentence types using a trained model; updating sentences by integrating the image-based QIs with text-based QIs; and outputting structured data that includes sentences organized by sentence type.

Abstract: An AI based platform for processing information collected during a medical procedure. A method includes capturing images and speech during a medical procedure; processing the images using a trained classifier to identify image-based quality-of-care indicators (QIs); converting the speech into text; parsing the text into sentences; performing a search and replace on predefined text patterns in the sentences; identifying text-based QIs in the sentences; classifying sentences into sentence types using a trained model; updating sentences by integrating the image-based QIs with text-based QIs; and outputting structured data that includes sentences organized by sentence type.

Abstract: A method for processing content being collected during a gastrointestinal (GI) procedure. The method includes: displaying a live video feed from a scope within a GI tract of a patient; capturing and displaying an image from the live video feed in response to a natural language (NL) command recognized during the GI procedure with an artificial intelligence (AI) language processor; extracting a label and a GI location associated with the image from uttered NL using the AI language processor during the GI procedure; annotating the image with the label and GI location while the image is displayed during the GI procedure; and displaying a GI tract replica during the procedure, wherein the replica includes a visual indicator that maps the GI location of the image in the GI tract.

Abstract: An AI based platform for processing information collected during a medical procedure. A method includes capturing images and speech during a medical procedure; processing the images using a trained classifier to identify image-based quality-of-care indicators (QIs); converting the speech into text; parsing the text into sentences; performing a search and replace on predefined text patterns in the sentences; identifying text-based QIs in the sentences; classifying sentences into sentence types using a trained model; updating sentences by integrating the image-based QIs with text-based QIs; and outputting structured data that includes sentences organized by sentence type.

Abstract: An artificial intelligence (AI) platform, method and program product for detecting and sizing a lesion in real time during a clinical procedure. An AI platform is disclosed that includes: a trained classifier that includes a deep learning model trained to detect lesions and reference objects in image data; a real time video analysis system that receives a video feed during a clinical procedure, uses the trained classifier to determine if a video frame from the video feed has both a lesion and a reference object, calculates an actual size of the lesion based on a pixel size of both the lesion and the reference object, and outputs an indication that the lesion was detected and the actual size of the lesion.

Abstract: An artificial intelligence (AI) platform, method and program product for detecting and sizing a lesion in real time during a clinical procedure. An AI platform is disclosed that includes: a trained classifier that includes a deep learning model trained to detect lesions and reference objects in image data; a real time video analysis system that receives a video feed during a clinical procedure, uses the trained classifier to determine if a video frame from the video feed has both a lesion and a reference object, calculates an actual size of the lesion based on a pixel size of both the lesion and the reference object, and outputs an indication that the lesion was detected and the actual size of the lesion.