For decades, three-dimensional C-arm Cone-Beam Computed Tomography (CBCT)
imaging system has been a critical component for complex vascular and
nonvascular interventional procedures. While it can significantly improve
multiplanar soft tissue imaging and provide pre-treatment target lesion
roadmapping and guidance, the traditional workflow can be cumbersome and
time-consuming, especially for less experienced users. To streamline this
process and enhance procedural efficiency overall, we proposed a visual
perception system, namely AutoCBCT, seamlessly integrated with an angiography
suite. This system dynamically models both the patient's body and the surgical
environment in real-time. AutoCBCT enables a novel workflow with automated
positioning, navigation and simulated test-runs, eliminating the need for
manual operations and interactions. The proposed system has been successfully
deployed and studied in both lab and clinical settings, demonstrating
significantly improved workflow efficiency.

AutoCBCT 是一个基于视觉感知系统的融入血管造影套房中的创新工作流，通过自动定位、导航和模拟测试运行，消除了手动操作和交互，从而提高了工作效率。

实时感知技术在导管室自动化中的应用

Automating Catheterization Labs with Real-Time Perception

Clinical calculators play a vital role in healthcare by offering accurate
evidence-based predictions for various purposes such as prognosis.
Nevertheless, their widespread utilization is frequently hindered by usability
challenges, poor dissemination, and restricted functionality. Augmenting large
language models with extensive collections of clinical calculators presents an
opportunity to overcome these obstacles and improve workflow efficiency, but
the scalability of the manual curation process poses a significant challenge.
In response, we introduce AgentMD, a novel language agent capable of curating
and applying clinical calculators across various clinical contexts. Using the
published literature, AgentMD has automatically curated a collection of 2,164
diverse clinical calculators with executable functions and structured
documentation, collectively named RiskCalcs. Manual evaluations show that
RiskCalcs tools achieve an accuracy of over 80% on three quality metrics. At
inference time, AgentMD can automatically select and apply the relevant
RiskCalcs tools given any patient description. On the newly established RiskQA
benchmark, AgentMD significantly outperforms chain-of-thought prompting with
GPT-4 (87.7% vs. 40.9% in accuracy). Additionally, we also applied AgentMD to
real-world clinical notes for analyzing both population-level and risk-level
patient characteristics. In summary, our study illustrates the utility of
language agents augmented with clinical calculators for healthcare analytics
and patient care.

我们的研究展示了具备临床计算器的语言代理在医疗分析和患者护理中的实用性。