Challenges in developing a magnetic resonance–compatible haptic hand-controller for neurosurgical training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A haptic device is an actuated human-machine interface utilized by an operator to dynamically interact with a remote environment. This interaction could be virtual (virtual reality) or physical such as using a robotic arm. To date, different mechanisms have been considered to actuate the haptic device to reflect force feedback from the remote environment. In a low-force environment or limited working envelope, the control of some actuation mechanisms such as hydraulic and pneumatic may be problematic. In the development of a haptic device, challenges include limited space, high accuracy or resolution, limitations in kinematic and dynamic solutions, points of singularity, dexterity as well as control system development/design. Furthermore, the haptic interface designed to operate in a magnetic resonance imaging environment adds additional challenges related to electromagnetic interference, static/variable magnetic fields, and the use of magnetic resonance-compatible materials. Such a device would allow functional magnetic resonance imaging to obtain information on the subject's brain activity while performing a task. When used for surgical trainees, functional magnetic resonance imaging could provide an assessment of surgical skills. In this application, the trainee, located supine within the magnet bore while observing the task environment on a graphical user interface, uses a low-force magnetic resonance-compatible haptic device to perform virtual surgical tasks in a limited space. In the quest to develop such a device, this review reports the multiple challenges faced and their potential solutions. The review also investigates efforts toward prototyping such devices and classifies the main components of a magnetic resonance-compatible device including actuation and sensory systems and materials used.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it