Anatomical measurement in a surgical system
AssigneeAuris Health, Inc.
InventorsRami Azmi Nimer Abukhalil, Kenneth Fernandez Prada, Amer G. Ghanem, Mary Lynn Dwyer-Gaddis
Life of this patent
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Abstract
For anatomy measurement in a surgical system, a machine-learned model is used to determine the location or length. For example, in RYGB, the machine-learned model indicates how much of the bowel has been pulled or run. As another RYGB example, the machine-learned model indicates a length along the bowel to a current location. In yet another RYGB example, the machine-learned model indicates a location for incision based on a desired length. Using an image or images, such as a video stream from an endoscope, the artificial intelligence provides the measurement, avoiding running with estimated steps size or use an inserted ruler.
Claims15
A method for loop measurement for a surgical system, the method comprising:
- capturing a video stream from an endoscope held by a first robotic arm of a surgical robotic system, the video captured intra-operatively during minimally invasive surgery where the surgical robotic system pulls a biliary loop or an alimentary loop with a surgical tool connected to a second robotic arm;
- measuring, by a machine-learned model in response to input of the video stream, a length along the biliary loop or the alimentary loop from a start location to a grasp point of the surgical tool held by the second robotic arm, the machine-learned model configured to output the length in response to the input of the video stream to the machine-learned model;
- displaying the length or a location based on the length during the minimally invasive surgery.
A surgical system for measurement during surgery, the surgical system comprising:
- a medical imager configured to capture an image of anatomy during surgery for the anatomy;
- an image processor configured to determine, by a machine-learned model in response to input of the image, a distance along the anatomy from a start location to a surgical tool grasp location, the machine-learned model configured by training where the training data includes ground truth measurements from an inserted tape measure and/or an optical sensor, the machine-learned model configured to output the distance;
- and
- a display configured to display a location of the anatomy during the surgery based on the distance determined by the machine-learned model.
Dependencies are inferred from the claim text.