Systems and methods are provided for determining acceptable ranges of pressures for use by a robotic arm on a surgical instrument, robotic systems and methods that are limited to using the acceptable ranges of pressures, and the medical devices for use in the robotic surgery. Learning software is included in the methods and systems for correlating manually-performed procedures with pressure sensors as a tactile gauge for qualifying the acceptable ranges of pressures for use by a robotic system. Robotic systems and methods are provided for (i) locating tissue borders of a surgical site, (ii) identifying a preferred pressure, and (iii) transmitting the data to the computer to avoid violating the integrity of tissue surrounding the surgical site.

Provided are robotic systems and methods for navigation of luminal network that detect physiological noise. In one aspect, the system includes a set of one or more processors configured to receive first and second image data from an image sensor located on an instrument, detect a set of one or more points of interest the first image data, and identify a set of first locations and a set of second location respectively corresponding to the set of points in the first and second image data. The set of processors are further configured to, based on the set of first locations and the set of second locations, detect a change of location of the instrument within a luminal network caused by movement of the luminal network relative to the instrument based on the set of first locations and the set of second locations.

Disclosed herein are techniques for implementing an intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem for soft tissue luminal applications. In various embodiments, a computing system analyzes first layer input data (indicating movement, position, and/or relative distance for a person(s) and object(s) in a room) and second layer input data. The second layer input data includes sensor and/or imaging data of a patient. Based on the analysis, the computing system generates one or more recommendations for guiding a medical professional in navigating a surgical device(s) with respect to one or more soft tissue luminal portions of the patient. The recommendation(s) include at least one mapped guide toward, in, and/or around the one or more soft tissue luminal portions. The mapped guide can include data corresponding to at least three dimensions, e.g., a 3D image/video. The computing system can present the recommendation(s) as image-based output, using a user experience device.

A system and a method are disclosed for forming an anastomosis between a first layer of tissue and a second layer of tissue of a patient's body. The system includes a first anastomosis device component and a second anastomosis device component configured to interact with the first anastomosis device component. The first anastomosis device component is configured to be delivered to a first lumen inside the patient's body. The second anastomosis device component is configured to be delivered to a second lumen inside the patient's body. The second anastomosis device includes one or more sensors configured to capture sensor data for determining an alignment of the second anastomosis device component relative to the first anastomosis device component, or for characterizing the position or orientation of the second anastomosis device component in three-dimensional space.

An instrument having a flexible and elongated body includes at least a lumen and a flex member disposed within the lumen. The flex member may be capable of providing steering control to a first portion of the elongate body while providing load bearing support to a second portion of the elongate body. A pull wire may be disposed within the flex member, and at least a distal portion of the pull wire may be coupled to the elongate body and a proximal portion of the pull wire may be operatively coupled to a control unit. The control unit may be coupled to a proximal portion of the elongate body. In addition, a control member may be operatively coupled to the control unit such that a distal portion of the control member may be positioned near a proximal portion of the flex member. The control member may be configured to support the flex member and control the movement or displacement of the flex member. Furthermore, the flex member may be configured to selectively decouple articulation or steering forces of a first portion of the elongate body away from a second portion of the elongate body; thereby, preventing compression of the second portion of the elongate body while maintaining elasticity or flexibility of the second portion of the elongate body.

A method for training, by a processor, a machine learning model for determining an operation of a medical tool control device may comprise the steps of: obtaining an operation command to move a medical tool of the medical tool control device on the basis of the machine learning model from guide data generated using a blood vessel image; generating evaluation data of a position to which the distal end of the medical tool has been moved according to the operation command in a blood vessel image; and updating a parameter of the machine learning model by using the evaluation data, so as to train the machine learning model.

An endoluminal traversing system and tissue crossing system are described wherein the access systems are controlled by actuators that allow for robotic control of system functions. The robotic system can be configured for full manual control over the actuators, full computerized control, or a combination of human and computer (AI, neural net, rule set) guidance.

A magnetic robot system is provided. The magnetic robot system comprises: a catheter having a first magnet coupling part provided at the front end thereof; and a mobile robot having a second magnet coupling part provided at the rear end thereof, and having a driving magnet, wherein the mobile robot is coupled to the catheter by means of magnetic force between the first magnet coupling part and the second magnet coupling part, and the magnetic force coupling of the first magnet coupling part and the second magnet coupling part can be released by rotating magnetic torque generated by the driving magnet because of the application of external rotating magnetic force.

Certain aspects relate to systems and techniques for docking medical instruments. For example, a medical system can include an instrument drive mechanism having a drive output that rotates and engages a corresponding drive input on a robotic medical instrument, a motor configured to rotate the drive output, and a torque sensor configured to measure torque imparted on the drive output. The robotic medical instrument can include a pre-tensioned pull wire actuated by the drive input. The system can activate the motor associated with the drive output to rotate the drive output in response to a torque signal from the torque sensor associated with the drive output in order to align the drive output with the drive input.

Systems and methods can comprise or involve predicting future movement information for a medical articulating arm using a learned model generated based on learned previous movement information from a prior non-autonomous trajectory of the medical articulating arm performed in response to operator input and using current movement information for the medical articulating arm, generating control signaling to autonomously control movement of the medical articulating arm in accordance with the predicted future movement information for the medical articulating arm, and autonomously controlling the movement of the medical articulating arm in accordance with the predicted future movement information for the medical articulating arm based on the generated control signaling.