As the significance of simulation in medical care and intervention continues
to grow, it is anticipated that a simplified and low-cost platform can be set
up to execute personalized diagnoses and treatments. 3D Slicer can not only
perform medical image analysis and visualization but can also provide surgical
navigation and surgical planning functions. In this paper, we have chosen 3D
Slicer as our base platform and monocular cameras are used as sensors. Then, We
used the neural radiance fields (NeRF) algorithm to complete the 3D model
reconstruction of the human head. We compared the accuracy of the NeRF
algorithm in generating 3D human head scenes and utilized the MarchingCube
algorithm to generate corresponding 3D mesh models. The individual's head pose,
obtained through single-camera vision, is transmitted in real-time to the scene
created within 3D Slicer. The demonstrations presented in this paper include
real-time synchronization of transformations between the human head model in
the 3D Slicer scene and the detected head posture. Additionally, we tested a
scene where a tool, marked with an ArUco Maker tracked by a single camera,
synchronously points to the real-time transformation of the head posture. These
demos indicate that our methodology can provide a feasible real-time simulation
platform for nasopharyngeal swab collection or intubation.

我们使用 3D Slicer 作为基础平台，利用单目相机将人头进行 3D 模型重建，实时通过单目视觉传输头部姿态，并演示了利用 ArUco Marker 追踪工具实时指向头部姿态变化的可行性，为鼻咽拭子采集或插管等提供了一个可行的实时仿真平台。

通过单目摄像机实时进行航行和安全进入自然开口的头部姿态估计和三维神经表面重建

Head Pose Estimation and 3D Neural Surface Reconstruction via Monocular  Camera in situ for Navigation and Safe Insertion into Natural Openings

In the dynamic hospital setting, decision support can be a valuable tool for
improving patient outcomes. Data-driven inference of future outcomes is
challenging in this dynamic setting, where long sequences such as laboratory
tests and medications are updated frequently. This is due in part to
heterogeneity of data types and mixed-sequence types contained in variable
length sequences. In this work we design a probabilistic unsupervised model for
multiple arbitrary-length sequences contained in hospitalization Electronic
Health Record (EHR) data. The model uses a latent variable structure and
captures complex relationships between medications, diagnoses, laboratory
tests, neurological assessments, and medications. It can be trained on original
data, without requiring any lossy transformations or time binning. Inference
algorithms are derived that use partial data to infer properties of the
complete sequences, including their length and presence of specific values. We
train this model on data from subjects receiving medical care in the Kaiser
Permanente Northern California integrated healthcare delivery system. The
results are evaluated against held-out data for predicting the length of
sequences and presence of Intensive Care Unit (ICU) in hospitalization bed
sequences. Our method outperforms a baseline approach, showing that in these
experiments the trained model captures information in the sequences that is
informative of their future values.

在动态医院环境中，决策支持是提高患者结果的有价值工具，本研究设计了一个概率无监督模型用于医院电子病历数据中包含的多个任意长度的序列，包括诊断、实验室检查、神经评估和药物，通过推断算法可以预测序列的长度和特定值的出现。