Abstract: A process includes graphically guiding a surgical workflow of a surgical procedure. The graphically guiding includes tracking object(s) by obtaining point cloud data based on imaging field(s) of view during the surgical procedure, and applying an artificial intelligence model to the point cloud data, and recognizing (i) object(s) in the field(s) of view and (ii) positioning of the object(s) in the field(s) of view. The object(s) include an object to be placed as part of the surgical procedure. The process displays graphical element(s) corresponding to the object on a display device and in a desired position relative to patient anatomy displayed as a live view to the patient anatomy and/or as a digital model of the patient anatomy on the display device, and updates properties of the graphical element(s), where the updating is based on detected positioning of the object in the field(s) of view, as informed by the tracking.

Abstract: Embodiments related to a method is described. The method comprises receiving an image file of a region of interest of an anatomy of a living organism, analyzing the image file and extracting image coordinates and density information of a plurality of points in the image file, training a neural network using collective information available in a database, registering the region of interest of the anatomy as a virtual version using an input from the neural network, and subsequently training the neural network using a user input from a user and the collective information available in the database. The collective information is recorded in the database with respect to a plurality of clusters of different physiological states of the living organism. The method further comprises performing a treatment on the region of interest. In an embodiment, the treatment is performed using the input from the neural network.

Abstract: A method of patient specific treatment planning is described herein. The method includes receiving an image file of a region of interest of an anatomy; extracting co-ordinates information and density information of a plurality of points of an image of the image file; pre-training a neural network based on the co-ordinates information, the density information and collective information of a database; performing at least one of a virtual action, and a treatment, via a user, on the region of interest based on collective information in the database; and training the neural network based on a user input, and the collective information from the database. The collective information comprises a plurality of clusters of different physiological states. The plurality of clusters comprises a plurality of sub-clusters. The virtual action and the treatment are performed through at least one of a virtual reality and an augmented reality.