Proficiency assessment system and method for deep brain stimulation (DBS)

Patent metadata

Field	Value
Publication number	US-11756689-B2
Application number	US-201816754089-A
Country	US
Kind code	B2
Filing date	Oct 9, 2018
Priority date	Oct 11, 2017
Publication date	Sep 12, 2023
Grant date	Sep 12, 2023

Abstract

A method for simulating a deep-brain stimulation in a computer-assisted platform that includes providing to a neurosurgeon, through a man-machine interface, visual information of a pre-operative situation, including a representation of a brain. The method also includes monitoring inputs of said neurosurgeon on the man-machine interface, until a trajectory is determined between an entry point and a target for the placement of an electrode. The method further includes comparing said trajectory to a set of previously-established trajectories for the pre-operative situation, so as to determine an overall measurement representative of a quality of the trajectory compared to the previously-established trajectories.

First claim

The invention claimed is:

1. Method for simulating a deep-brain stimulation in a computer-assisted platform, comprising steps of:
as part of a training process of a training neurosurgeon:
providing to the training neurosurgeon, through a man-machine interface, visual information of a pre-operative situation, including a representation of a brain, wherein the representation of the brain comprises a three-dimensional (3D) representation;
receiving inputs of said training neurosurgeon on said man-machine interface indicating movement around the 3D representation and a change in one or more display parameters of the man-machine interface;
providing via the man-machine interface the movement around the 3D representation and changing the one or more display parameters of the man-machine interface in response to receiving the inputs of said training neurosurgeon indicating the movement around the 3D representation and the change in the one or more display parameters;
monitoring the inputs of said training neurosurgeon on said man-machine interface, until a trajectory is determined between an entry point and a target for placement of an electrode;
comparing said trajectory of said training neurosurgeon to a set of previously-recorded trajectories of other neurosurgeons for said pre-operative situation stored in a database; and
determining an overall measurement representative of a quality of said trajectory of said training neurosurgeon compared to the trajectories of said set of previously-recorded trajectories based on the comparing; and
providing feedback to the training neurosurgeon via the man-machine interface, the feedback comprising textual information including a distance to a reference point, an angle to a reference target, and a risk of a planned trajectory.

2. Method according to claim 1 , wherein said overall measurement is based on a set of measurements corresponding to primitive proficiency metrics according to which said trajectory is compared to said set of previously-recorded trajectories.

3. Method according to claim 2 , wherein said primitive proficiency metrics comprises
an angle between said trajectory and a trajectory of said set of previously-recorded trajectories;
a distance between said trajectory and a trajectory of said set of previously-recorded trajectories;
a trajectory risk, representative of a risk involved by said trajectory.

4. Method according to claim 1 , further comprising comparing other inputs than said trajectory to previously recorded inputs, so as to provide measurements according to additional primitive proficiency metrics.

5. Method according to claim 2 , wherein said overall measurement (c i ) for said training neurosurgeon (i) is determined by:

c
i

=

∑

j
=
1

N
P

⁢

r

i
,
j

N
E

×

N
P

wherein N E is the number of trajectories of said set of previously-recorded trajectories, N P is the number of said primitive proficiency metrics and r i,j is the rank of said training neurosurgeon among measurements related to the trajectories of said set of previously-recorded trajectories for a j th primitive proficiency metric of said primitive proficiency metrics.

6. Method according to claim 1 , wherein the target is provided to said training neurosurgeon, and wherein determining said trajectory consists in determining the entry point.

7. Method according to claim 1 , wherein a set of pre-operative situations is provided to said training neurosurgeon, and wherein said overall measurement is determined for a first pre-operative situation and for a last pre-operative situation, among said set, so as to compare them for assessing a progress of said training neurosurgeon.

8. Method according to claim 1 , wherein some feedbacks are provided to said training neurosurgeon through said man-machine interface.

9. Computer program product comprising:
a non-transitory computer storage medium having instructions stored thereon that, when deployed on a data computing unit of a network node, as part of a training process of a training neurosurgeon:
provides to the training neurosurgeon, through a man-machine interface, visual information of a pre-operative situation, including a representation of a brain that comprises a three-dimensional (3D) representation;
receives inputs of said training neurosurgeon on said man-machine interface indicating movement around the 3D representation and a change in one or more display parameters of the man-machine interface;
provides via the man-machine interface the movement around the 3D representation and changes the one or more display parameters of the man-machine interface in response to receiving the inputs of said training neurosurgeon indicating the movement around the 3D representation and the change in the one or more display parameters;
monitors the inputs of said training neurosurgeon on said man-machine interface, until a trajectory is determined between an entry point and a target for placement of an electrode;
compares said trajectory of said training neurosurgeon to a set of previously-recorded trajectories of other neurosurgeons for said pre-operative situation stored in a database;
determines an overall measurement representative of a quality of said trajectory of said training neurosurgeon compared to the trajectories of said set of previously-recorded trajectories based on the comparing; and
provides feedback to the training neurosurgeon via the man-machine interface, the feedback comprising textual information including a distance to a reference point, an angle to a reference target, and a risk of a planned trajectory.

10. System for simulating a deep-brain stimulation in a computer-assisted platform, comprising:
a man-machine interface for providing to a training neurosurgeon, visual information of a pre-operative situation, including a representation of a brain that comprises a three-dimensional (3D) representation, and for monitoring inputs of said training neurosurgeon on said man-machine interface, until a trajectory is determined between an entry point and a target for placement of an electrode;
a database storing a set of previously-recorded trajectories of other neurosurgeons for said pre-operative situation; and
an application stored in a non-transitory memory that, when executed by a processor, during a training process of the training neurosurgeon:
receives the inputs of said training neurosurgeon on said man-machine interface, wherein the inputs indicate movement around the 3D representation and a change in one or more display parameters of the man-machine interface,
provides via the man-machine interface the movement around the 3D representation and changes the one or more display parameters of the man-machine interface in response to receiving the inputs of said training neurosurgeon indicating the

Assignees

• Univ Rennes
• Inserm—Institut National De La Sante Et De La Rech Medicale
• Centre Hospitalier Univ Pontchaillou
• Queens Univ

Inventors

• Fichtinger Gabor
• Holden Matthew
• Jannin Pierre
• Haegelen Claire
• Zhao Yulong

Classifications

• A61N1/36142 Primary

  for improving safety · CPC title

• G16H50/50 Primary

  for simulation or modelling of medical disorders · CPC title

• A61N1/0534

  Electrodes for deep brain stimulation · CPC title

• G16H40/60

  for the operation of medical equipment or devices · CPC title