Augmented Reality (AR) has emerged as a significant advancement in surgical
procedures, offering a solution to the challenges posed by traditional
neuronavigation methods. These conventional techniques often necessitate
surgeons to split their focus between the surgical site and a separate monitor
that displays guiding images. Over the years, many systems have been developed
to register and track the hologram at the targeted locations, each employed its
own evaluation technique. On the other hand, hologram displacement measurement
is not a straightforward task because of various factors such as occlusion,
Vengence-Accomodation Conflict, and unstable holograms in space. In this study,
we explore and classify different techniques for assessing an AR-assisted
neurosurgery system and propose a new technique to systematize the assessment
procedure. Moreover, we conduct a deeper investigation to assess surgeon error
in the pre- and intra-operative phases of the surgery based on the respective
feedback given. We found that although the system can undergo registration and
tracking errors, physical feedback can significantly reduce the error caused by
hologram displacement. However, the lack of visual feedback on the hologram
does not have a significant effect on the user 3D perception.

评估 AR 辅助神经外科系统的不同技术并提出了一种新的技术来系统化评估过程。此外，在手术的准备和术中阶段基于相应的反馈进行更深入的调查，发现物理反馈可以显著减少由全息图位移引起的错误，而全息图上的缺乏视觉反馈对用户的三维感知不会产生显著影响。

AR 辅助神经外科系统的端到端评估

End-to-End assessment of AR-assisted neurosurgery systems

Large language models demonstrate an emergent ability to learn a new task
from a small number of input-output demonstrations, referred to as in-context
few-shot learning. However, recent work shows that in such settings, models
mainly learn to mimic the new task distribution, instead of the mechanics of
the new task. We argue that the commonly-used evaluation settings of few-shot
models utilizing a random selection of in-context demonstrations is not able to
disentangle models' ability to learn new skills from demonstrations, as most of
the such-selected demonstrations are not informative for prediction beyond
exposing the new task's input and output distribution.
Therefore, we introduce an evaluation technique that disentangles few-shot
learners' gain from in-context learning by picking the demonstrations sharing a
specific, informative concept with the predicted sample, in addition to the
performance reached by mainly non-informative samples. We find that regardless
of the model size, existing few-shot learners are not able to benefit from
observing such informative concepts in demonstrations. We also find that such
ability may not be obtained trivially by exposing the informative
demonstrations in the training process, leaving the challenge of training true
in-context learners open.

通过挑选与预测样本分享特定信息的示范来区分少样本学习者的能力，结果表明现有的少样本学习者不利用信息，并且暴露了训练真正的上下文学习者的挑战。