Markerless navigation using ai computer vision
AssigneeHealthcare Outcomes Performance Company Limited
InventorsThomas HARTE, Huy Quoc PHAN, Axel SYLVAN
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Abstract
Provided herein are devices, systems, and methods for a three-dimensional registering, tracking, and/or guiding an object of interest, such a body part, a surgical tool, or an implant, during a surgical procedure. Such devices, systems, and methods may offer minimally invasive, high precision registering, tracking, and/or guiding of the object of interest using a patterned light beam and data processing using artificial intelligence. The methods, devices, and systems disclosed herein may be compatible with a simple marker or markers placed by a minimally invasive method on the object of interest.
Claims23
A system for tracking an object during a surgery, the system comprising a memory and a processor in communication with the memory and configured to:
- a) receive a plurality of images of a light beam projected onto the object;
- b) input the plurality of images into a machine learning model to generate an output of a location, orientation, or rotation of the object in three-dimensional space;
- and
- c) generate a three-dimensional visual representation of the object based at least in part on the location, orientation, or rotation of the object in three-dimensional space.
A non-transitory computer-readable storage medium, having a computer executable instruction executable by one or more processors to:
- a) receive a plurality of images of a light beam projected onto an object;
- b) input the plurality of images into a machine learning model to output a location, orientation, or rotation of the object in three-dimensional space;
- and
- c) generate a three-dimensional visual representation of the object based at least in part on the location, orientation, or rotation of the object in three-dimensional space.
A computer-implemented method for tracking an object during surgery, the method comprising:
- a) receiving a plurality of images of a light beam projected onto the object;
- b) inputting the plurality of images into a machine learning model to output a location, orientation, or rotation of the object in three-dimensional space;
- and
- c) generating a three-dimensional visual representation of the object based at least in part on the location, orientation, or rotation of the object in three-dimensional space.
Dependencies are inferred from the claim text.