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

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 movement around the 3D representation and the change in the one or more display parameters,

compares said trajectory of said training neurosurgeon to the set of previously-recorded trajectories of other neurosurgeons for said pre-operative situation from the database,

determines an overall measurement representative of a quality of said trajectory of said training neurosurgeon compared to said previously-recorded trajectories based on the comparison, 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.