A tissue suturing guidance system includes an image capturing device, a display, and a processor in communication with the image capturing device and the display. The image capturing device is configured to capture a suture site. The display is configured to display an image of the suture site. The processor is configured to: determine, based on the image of the suture site, a geometric tissue representation of the suture site; access measured properties of the suture site; determine, based on the measured properties of the suture site, a biomechanical tissue representation of the suture site; and generate, based on the geometric tissue representation and biomechanical tissue representation of the suture site, a suturing configuration for the suture site.

A surgical robotic system has a tool drive coupled to a distal end of a robotic arm that has a plurality of actuators. The tool drive has a docking interface to receive a trocar. The system also includes one or more sensors that are operable to visually sense a surface feature of the trocar. One or more processors determine a position and orientation of the trocar, based on the visually sensed surface feature. In response, the processor controls the actuators to orient the docking interface to the determined orientation of the trocar and to guide the robotic arm toward the determined position of the trocar. Other aspects are also described and claimed.

A surgical robotic system has a tool drive coupled to a distal end of a robotic arm that has a plurality of actuators. The tool drive has a docking interface to receive a trocar. The system also includes one or more sensors that are operable to visually sense a surface feature of the trocar. One or more processors determine a position and orientation of the trocar, based on the visually sensed surface feature. In response, the processor controls the actuators to orient the docking interface to the determined orientation of the trocar and to guide the robotic arm toward the determined position of the trocar. Other aspects are also described and claimed.

Systems, methods, and devices are disclosed for end effector identification in robotic surgical systems. A surgical robot can be coupled to an end effector. The system can identify the end effector using data received from the end effector. The system can adjust operation of the surgical system, including the robot arm, based on the data received from the end effector. Data received from or regarding the end effector can include detected characteristics, retrieved characteristics, or data stored on the end effector and communicated to the system. Both the identification and the operation adjustments can be performed automatically such that the system experiences little to no lag or downtime when coupling with different end effectors.

Systems, methods, and devices are disclosed for end effector identification in robotic surgical systems. A surgical robot can be coupled to an end effector. The system can identify the end effector using data received from the end effector. The system can adjust operation of the surgical system, including the robot arm, based on the data received from the end effector. Data received from or regarding the end effector can include detected characteristics, retrieved characteristics, or data stored on the end effector and communicated to the system. Both the identification and the operation adjustments can be performed automatically such that the system experiences little to no lag or downtime when coupling with different end effectors.

An eye surgery apparatus includes an eye surgery tool, an imaging system, a robotic arm, and a processor. The eye surgery tool has a distal end for insertion into an eye of a patient through an incision in the eye. The imaging system is configured to acquire images showing the incision and at least part of the eye surgery tool. The robotic arm is coupled with the eye surgery tool, which is configured to move the distal end of the eye surgery tool inside the eye according to one or more commands issued during an eye surgery. The processor is configured to, during the eye surgery (i) receive the images from the imaging system, (ii) monitor the commands issued to the robotic arm, (iii) detect, by analyzing the images, that a monitored command is expected to enlarge the incision, and (iv) initiate responsive action with respect to the detected command.

An eye surgery apparatus includes an eye surgery tool, an imaging system, a robotic arm, and a processor. The eye surgery tool has a distal end for insertion into an eye of a patient through an incision in the eye. The imaging system is configured to acquire images showing the incision and at least part of the eye surgery tool. The robotic arm is coupled with the eye surgery tool, which is configured to move the distal end of the eye surgery tool inside the eye according to one or more commands issued during an eye surgery. The processor is configured to, during the eye surgery (i) receive the images from the imaging system, (ii) monitor the commands issued to the robotic arm, (iii) detect, by analyzing the images, that a monitored command is expected to enlarge the incision, and (iv) initiate responsive action with respect to the detected command.

There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output. The intermediate outputs are inputted into a single common decoding-expanding component for computing the indication of segmented 3D ROI(s).

There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output. The intermediate outputs are inputted into a single common decoding-expanding component for computing the indication of segmented 3D ROI(s).

A medical device has been disclosed. The medical device includes a parallel manipulator. The parallel manipulator having an end platform coupled to a surgical tool and a base platform coupled to machine module. The machine module is coupled to the surgical tool through a transmission shaft disposed between the end platform and the base platform.