I-Corps: Translation potential of minimally invasive tubular retractors to maximize visualization in spine operations

I-Corps：微创管状牵开器的翻译潜力，可最大限度地提高脊柱手术的可视化

• 批准号: 2422243
• 负责人: Kevin Costa
• 金额: $ 5万
• 依托单位: Icahn School of Medicine at Mount Sinai
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2422243&HistoricalAwards=false
• 关键词: Corps; Translation; potential; minimally; invasive

The broader impact of this I-Corps project is the development of a system to aid surgeons performing spinal surgery. Currently, minimally invasive surgical techniques are becoming the primary method by which spinal pathologies and deformities are treated. However, the small incision sizes inherent in these approaches prevents optimal visualization of the operating area, and these procedures require the use of cumbersome surgical microscopes at awkward angles, increasing the risk of surgical error and negatively impacting surgical ergonomics. This solution is a camera system with integrated artificial intelligence (AI) software that subtracts obstructive tool shafts from the field of view, and also highlights tooltips and vital anatomy for the surgeon in real time. By allowing for maximal visualization in minimally invasive settings, this technology may increase the accessibility, ease, and reliability of these procedures. In addition, the AI software and modular hardware create a foundation for a database of surgical procedures that may be used to train other algorithms to be applied in a range of surgical settings including laparoscopic, endoscopic, and robotic techniques.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the previous development of a camera system with integrated artificial intelligence (AI) software that maximizes the visualization of minimally invasive spine operations through minimally invasive tubular retractors (MITRs). Minimally invasive tubular retractors (MITRs) are metal tubes with diameters ranging from 14-22 mm that are used as operating corridors in minimally invasive spine surgeries. They suffer from limited visibility, awkward tool angling, and reliance on cumbersome surgical microscopes. This solution utilizes an array of cameras that focus down the MITR’s operating corridor and capture the operating field from multiple angles. These views are analyzed by the integrated AI software to produce a single live image that removes obstructive and unwanted parts of surgeon tools (e.g., tool shaft) while retaining other necessary components (e.g., tooltip). Specifically, the software aims to combine the array of camera inputs into a singular real-time video output that is segmented and inpainted to increase the surgeons view by over 30%. This is achieved with the help of a specialized deep learning computer vision model that enables the software to identify (via segmentation) and crop out (via inpainting) obstructive objects (tool shafts) within the video feed. Ultimately, this visually maximized image is displayed on a heads-up screen in front of the surgeon, improving outcomes by maximizing the visualization of the operating area and the ergonomics of the procedure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

I-Corps项目更广泛的影响是开发一个系统来帮助外科医生进行脊柱手术。目前，微创手术技术正成为治疗脊柱病变和畸形的主要方法。然而，这些方法固有的小切口尺寸阻碍了手术区域的最佳可视化，并且这些程序需要在尴尬的角度使用笨重的手术显微镜，增加了手术错误的风险，并对手术人体工程学产生负面影响。该解决方案是一个集成了人工智能（AI）软件的摄像系统，可以从视野中去除阻塞的工具轴，并实时为外科医生突出显示工具提示和重要解剖结构。通过在微创环境中实现最大程度的可视化，该技术可以增加这些手术的可及性、易用性和可靠性。此外，人工智能软件和模块化硬件为外科手术数据库奠定了基础，该数据库可用于训练其他算法，以应用于一系列手术环境，包括腹腔镜、内窥镜和机器人技术。这个I-Corps项目利用体验式学习和对行业生态系统的第一手调查来评估该技术的翻译潜力。该解决方案基于先前开发的集成人工智能（AI）软件的摄像系统，该系统通过微创管状牵开器（mitr）最大限度地实现微创脊柱手术的可视化。微创管状牵开器（mitr）是直径为14-22毫米的金属管，在微创脊柱手术中用作手术通道。他们的缺点是能见度有限，工具角度笨拙，并且依赖于笨重的手术显微镜。该解决方案利用一组聚焦MITR作业通道的摄像机，从多个角度捕捉作业区域。集成的人工智能软件对这些视图进行分析，生成单一的实时图像，去除外科医生工具（如工具轴）的阻碍和不需要的部分，同时保留其他必要的组件（如工具提示）。具体来说，该软件旨在将一系列摄像头输入组合成一个单一的实时视频输出，该视频输出经过分割和绘制，以将外科医生的视野提高30%以上。这是在专门的深度学习计算机视觉模型的帮助下实现的，该模型使软件能够识别（通过分割）并裁剪（通过涂漆）视频馈送中的阻碍物体（工具轴）。最终，这个视觉上最大化的图像显示在外科医生面前的平视屏幕上，通过最大化操作区域的可视化和手术的人体工程学来改善结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。