Auris Health, Inc.

Other

Hedyeh Rafii-Tari

Prasanth Jeevan

Ritwik Ummalaneni

David Paul Noonan

Subashini Srinivasan

Nicolas E Robert

David S Mintz

David Michael Schummers

Chauncey F Graetzel

David Burdick Berman

Menglong Ye

Alexander James Sheehy

Melissa A Teran

Miroslav Drahos

Jeffery David Howard

Douglas T Boyd

Andrew Esbenshade Zeldis

René Ango Mambembe

Elif Ayvali

Christopher K Sramek

Yuriy Malinin

Simon Wei Quan Lim

Provided are systems and methods for path-based navigation of tubular networks. In one aspect, the method includes receiving location data from at least one of a set of location sensors and a set of robot command inputs, the location data being indicative of a location of an instrument configured to be driven through a luminal network. The method also includes determining a first estimate of the location of the instrument at a first time based on the location data, determining a second estimate of the location of the instrument at the first time based on the path, and determining the location of the instrument at the first time based on the first estimate and the second estimate.

Path-based navigation of tubular networks

Provided are systems and methods for location sensor-based branch prediction. In one aspect, the method includes determining a first orientation of an instrument based on first location data generated by a set of one or more location sensors for the instrument and determining a second orientation of the instrument at a second time based on second location data. A distal end of the instrument is located within a first segment of a model at the first time and the second time and the first segment branches into two or more child segments. The method also includes determining data indicative of a difference between the first orientation and the second orientation and determining a prediction that the instrument will advance into a first one of the child segments based on the data indicative of the difference.

Systems and methods for location sensor-based branch prediction

A robotic system is configured to evaluate an identified phase of a medical procedure. The robotic system includes a video capture device; a robotic manipulator; one or more sensors; an input device; a data store; and control circuitry. The control circuitry is configured to: determine a first status of the robotic manipulator based on sensor data from the one or more sensors; identify a first input from the input device for initiating a first action of the robotic manipulator; perform a first analysis of a video of a patient site captured by the video capture device; identify a first phase of the medical procedure based at least in part on the first status of the robotic manipulator, the first input, and the first analysis of the video; and generate an evaluation of the first phase of the medical procedure based on one or more metrics associated with the first phase.

Automated procedure evaluation

Certain aspects relate to systems and techniques for luminal network navigation. Some aspects relate to incorporating respiratory frequency and/or magnitude into a navigation system to implement patient safety measures. Some aspects relate to identifying, and compensating for, motion caused by patient respiration in order to provide a more accurate identification of the position of an instrument within a luminal network.

Robotic systems for navigation of luminal networks that compensate for physiological noise

Certain aspects relate to systems and techniques for navigation path tracing. In one aspect, a system displays a preoperative model of a luminal network is displayed. The system determines a position of an instrument within the luminal network relative to the preoperative model. Based on the position of the instrument relative to the preoperative model, the system determines whether to enter a path tracing mode. In path tracing mode the system displays visual indicia indicative of a path of the instrument with respect to the displayed preoperative model. The visual indicia may be used to visual the navigation path of the instrument and/or to extend the preoperative model.

Robotic system configured for navigation path tracing

Certain aspects relate to systems and techniques for medical instrument navigation and targeting. In one aspect, a system includes a medical instrument having an elongate body and at least one sensor, a display, a processor, and a memory storing a model of a mapped portion of a luminal network and a position of a target with respect to the model. The processor may be configured to: determine, based on data from the at least one sensor, a position and orientation of a distal end of the medical instrument with respect to the model, and cause, on at least a portion of the display, a rendering of the model, the position of the target, and the position and orientation of the distal end of the medical instrument. The rendering may be based on a viewpoint directed at the target and different from a viewpoint of the medical instrument.

System and method for medical instrument navigation and targeting

An object sizing system sizes an object positioned within a patient. The object sizing system identifies a presence of the object. The object sizing system navigates an elongate body of an instrument to a position proximal to the object within the patient. An imaging sensor coupled to the elongate body captures one or more sequential images of the object. The instrument may be further moved around within the patient to capture additional images at different positions/orientations relative to the object. The object sizing system also acquires robot data and/or EM data associated with the positions and orientations of the elongate body. The object sizing system analyzes the captured images based on the acquired robot data to estimate a size of the object.

Endolumenal object sizing

Systems and methods for electromagnetic field generator alignment are disclosed. In one aspect, the system includes an electromagnetic (EM) sensor configured to generate, when positioned in a working volume of the EM field, one or more EM sensor signals based on detection of the EM field, the EM sensor configured for placement on a patient. The system may also include a processor and a memory storing computer-executable instructions to cause the processor to: determine a position of the EM sensor with respect to the field generator based on the one or more EM sensor signals, encode a representation of the position of the EM sensor with respect to the working volume of the EM field, and provide the encoded representation of the position to a display configured to render encoded data.

Electromagnetic field generator alignment

Systems and methods for estimating instrument location are described. The methods and systems can obtain a first motion estimate based on robotic data and a second motion estimate based on position sensor data. The methods and systems can determine a motion estimate disparity based on a comparison of the first and second motion estimates. Based on the motion estimate disparity, the methods and systems can update a weighting factor for a location derivable from the robotic data or a weighting factor for a location derivable from the position sensor data. Based on the updated weighting factor, the methods and systems can determine a location/position estimate for the instrument. The methods and systems can provide increased accuracy for a position estimate in cases where the instrument experiences buckling or hysteresis.

System and method for estimating instrument location

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.

Robotic systems and methods for navigation of luminal network that detect physiological noise

Systems and methods for electromagnetic distortion detection are disclosed. In one aspect, the system includes an electromagnetic (EM) sensor configured to generate an EM sensor signal in response to detection of the EM field. The system may also include a processor configured to calculate a baseline value of a metric indicative of a position of the EM sensor at a first time and calculate an updated value of the metric during a time period after the first time. The processor may be further configured to determine that a difference between the updated value and the baseline value is greater than a threshold value and determine that the EM field has been distorted in response to the difference being greater than the threshold value.

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Mountain View, CA, United States of America

Hedyeh Rafii-Tari