This technical report presents MOSaiC 3.6.2, a web-based collaborative
platform designed for the annotation and evaluation of medical videos. MOSaiC
is engineered to facilitate video-based assessment and accelerate surgical data
science projects. We provide an overview of MOSaiC's key functionalities,
encompassing group and video management, annotation tools, ontologies,
assessment capabilities, and user administration. Finally, we briefly describe
several medical data science studies where MOSaiC has been instrumental in the
dataset development.

MOSaiC 3.6.2 是一个基于 Web 的协作平台，用于医学视频的标注和评估，旨在促进基于视频的评估和加速外科数据科学项目。

MOSaiC：协作医学视频评估和标注的基于 Web 的平台

MOSaiC: a Web-based Platform for Collaborative Medical Video Assessment  and Annotation

There is a growing interest in using pose estimation algorithms for
video-based assessment of Bradykinesia in Parkinson's Disease (PD) to
facilitate remote disease assessment and monitoring. However, the accuracy of
pose estimation algorithms in videos from video streaming services during
Telehealth appointments has not been studied. In this study, we used seven
off-the-shelf hand pose estimation models to estimate the movement of the thumb
and index fingers in videos of the finger-tapping (FT) test recorded from
Healthy Controls (HC) and participants with PD and under two different
conditions: streaming (videos recorded during a live Zoom meeting) and
on-device (videos recorded locally with high-quality cameras). The accuracy and
reliability of the models were estimated by comparing the models' output with
manual results. Three of the seven models demonstrated good accuracy for
on-device recordings, and the accuracy decreased significantly for streaming
recordings. We observed a negative correlation between movement speed and the
model's accuracy for the streaming recordings. Additionally, we evaluated the
reliability of ten movement features related to bradykinesia extracted from
video recordings of PD patients performing the FT test. While most of the
features demonstrated excellent reliability for on-device recordings, most of
the features demonstrated poor to moderate reliability for streaming
recordings. Our findings highlight the limitations of pose estimation
algorithms when applied to video recordings obtained during Telehealth visits,
and demonstrate that on-device recordings can be used for automatic
video-assessment of bradykinesia in PD.

本研究通过使用七种现有手部姿势估计模型，评估了巴金森病（PD）患者和健康对照组在不同条件下（即视频流和设备本地录制）进行手指敲击（FT）测试的视频中拇指和食指运动的准确性和可靠性，并且指出姿势估计算法在电视医疗就诊期间的视频记录中的应用存在的局限性，并表明设备本地录制可用于自动评估 PD 中的运动缓慢性。

基于视频的帕金森病肢体无力远程评估的手势姿势估计

Video-Based Hand Pose Estimation for Remote Assessment of Bradykinesia  in Parkinson's Disease

To ensure satisfactory clinical outcomes, surgical skill assessment must be
objective, time-efficient, and preferentially automated - none of which is
currently achievable. Video-based assessment (VBA) is being deployed in
intraoperative and simulation settings to evaluate technical skill execution.
However, VBA remains manually- and time-intensive and prone to subjective
interpretation and poor inter-rater reliability. Herein, we propose a deep
learning (DL) model that can automatically and objectively provide a
high-stakes summative assessment of surgical skill execution based on video
feeds and low-stakes formative assessment to guide surgical skill acquisition.
Formative assessment is generated using heatmaps of visual features that
correlate with surgical performance. Hence, the DL model paves the way to the
quantitative and reproducible evaluation of surgical tasks from videos with the
potential for broad dissemination in surgical training, certification, and
credentialing.

使用基于视频的评估和深度学习模型，生成热力图进行形式评估，并给出高风险总结性评估，使外科手术技能训练成果定量化和可复制化。