A computer-implemented method to improve the point collection process during registration of a bone for a computer-assisted surgical procedure is provided. Based on bone digitization data, a simulation is performed to confirm the accuracy of the registration for different digitization regions. Results are tested to identify which digitization regions meet a predefined accuracy requirement. The resulting information is used to perform a computer-assisted surgical procedure. A computerized simulation method for registration of a bone for a computer-assisted surgical procedure is also provided based on processor executing random stroking an expected exposed surface of a bone model with multiple of stroke curves to cover most of the bone model surface with uniform noise and a random sample consensus is applied to remove outlying point to yield the best registration results, to find the top subset as to overlap. A method to perform computer-assisted surgery is also provided.

An in-situ additive-manufacturing system for growing an implant in-situ for a patient. The system has a multi-nozzle dispensing subsystem and a distal control arm. The multi-nozzle dispensing subsystem in one embodiment includes first and second dispensing nozzles. The first and second nozzles include first and second printing-material delivery channels, respectively. In another embodiment, the in-situ additive-manufacturing system includes a multi-material subsystem having a dispensing nozzle including first and second printing material delivery channels. Controlling computing and robotics componentry are provided. In various aspects, respective storage for first and second printing materials, and one or more pumping structures, are provided.

There is provided a computer implemented method of providing a surgical controller with instructions to restrict a manipulation of an endoscopic tool during an endoscopic medical procedure in an intrabody cavity, comprising: inputting into classifier(s), indication(s) of instructions for manipulation of the endoscopic tool outputted by sensor(s), classifying by the classifier(s), the indication of instructions into an envelope defining a volume that restricts therein manipulation of the endoscopic tool, wherein the classifier(s) classifies the envelope based on previously obtained indications of instructions for manipulation of endoscopic tools during other endoscopic medical procedures performed in intrabody cavities of other patients, analyzing the indication of instructions for manipulation of the endoscopic tool according to the envelope to determine when the instructions are for manipulation of the endoscopic tool externally to the envelope, and providing the surgical controller with an indication of inappropriate manipulation according to the analysis.

A medical system may comprise a catheter (101) for ablating tissue including a flexible longitudinal body including a distal end; and a distal portion extending distally from the distal end of longitudinal body. The distal portion may include a plurality of electrodes (103). The medical system may also comprise one or more control units (112) coupled to the catheter and configured to (1) control a supply of electrical energy to each of the plurality of electrodes and (2) automatically control a position of the distal portion of the catheter.

An imaging system aka 3d camera operative in conjunction with a tube having two open ends, the system comprising active portions small enough to fit into the tube and an electronic subsystem including a hardware processor operative to receive image/s from the active portions and to generate therefrom at least one 3D image of a scene visible via one of the tube's open ends. The system may comprise a tracker configured to be secured to the tube, and a method for monitoring location, e.g. absolute location, of the tube, accordingly.

A medical device includes an elongate device and one or more processors coupled to the elongate device. The elongate device includes a steerable distal end and a shape sensor located along a length of the elongate device. While the elongate device is being traversed through one or more passageways of a patient, the one or more processors are configured to, based on information from a sensor, monitor an insertion motion of the elongate device, detect a data collection event, and capture, in response to detecting the data collection event, a plurality of points along the length of the elongate device using the shape sensor. The data collection event is at least partially based on a change in direction of the insertion motion of the elongate device.

A method and apparatus is provided that uses a deep learning (DL) network to reduce noise and artifacts in reconstructed medical images, such as images generated using computed tomography, positron emission tomography, and magnetic resonance imaging. The DL network can operate either on pre-reconstruction data or on a reconstructed image. The DL network can be an artificial neural network or a convolutional neural network (e.g., using a three-channel volumetric kernel architecture). Different neural networks can be trained depending on the noise level, scanning protocol, or the anatomic, diagnostic or clinical objective of the reconstructed image (e.g., by partitioning the training data into noise-level range and training respective DL networks for each range). Further, the DL networks can be trained to mitigate artifacts, such as the cone-beam artifact.

A system according to at least one embodiment of the present disclosure includes an imaging source; an imaging detector; a depth sensor; and a controller, where the controller receives image information from the depth sensor, determines a gesture in relation to a working volume, and moves the imaging source and the imaging detector relative to the working volume based on the gesture.

A computing system may identify a surgical instrument for a surgical procedure in an operating room (OR). The computing system may detect a control input by a health care professional (HCP) to control the surgical instrument. The computing system may determine the HCP's access control level associated with the surgical instrument. The computing system may determine whether the HCP has an authorization to control the surgical instrument. If the computing system determines that HCP is unauthorized to control the surgical instrument based on the access control level associated with the HCP, the computing system may block the control input by the HCP. If the computing system determines that the HCP is authorized to control the surgical instrument based on the access control level associated with the HCP, the computing system may effectuate the control input by the HCP to control the surgical instrument.

A surgical robotic platform operates within a constrained space of an imaging scanner in which a patient resides. The platform includes a gross positioning stage configured to be located outside of the constrained space An end-effector having a rotatable shaft is extendable from the gross positioning stage and into the constrained space of the imaging scanner. The shaft has a proximal end operatively coupled to the positioning stage outside of the constrained space and a distal end configured to be located in the constrained space. The distal end has a medical instrument gripper for holding a medical instrument used in a percutaneous procedure. The end-effector further includes a joint arrangement operatively coupling the shaft to the medical gripper for providing motion to the medical instrument gripper for enabling position and/or orientation control of the medical instrument. A drive module controls the joint arrangement.