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经皮图像引导介入的导航和机器人系统评估：一种用于高级成像与人工智能集成的新指标。

Evaluation of navigation and robotic systems for percutaneous image-guided interventions: A novel metric for advanced imaging and artificial intelligence integration.

作者信息

Cornelis Francois H, Filippiadis Dimitrios K, Wiggermann Philipp, Solomon Stephen B, Madoff David C, Milot Laurent, Bodard Sylvain

机构信息

Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Weill Cornell Medical College, Department of Radiology, New York, NY 10065, USA.

2nd Department of Radiology, General University Hospital "ATTIKON", Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece.

出版信息

Diagn Interv Imaging. 2025 May;106(5):157-168. doi: 10.1016/j.diii.2025.01.004. Epub 2025 Jan 29.

DOI:10.1016/j.diii.2025.01.004

PMID:39884887

Abstract

PURPOSE

Navigation and robotic systems aim to improve the accuracy and efficiency of percutaneous image-guided interventions, but the evaluation of their autonomy and integration of advanced imaging and artificial intelligence (AI) is lacking. The purpose of this study was to evaluate the level of automation and integration of advanced imaging and artificial intelligence in navigation and robotic systems for percutaneous image-guided interventions, using established and novel metrics to categorize and compare their capabilities.

MATERIALS AND METHODS

Following PRISMA guidelines, a systematic review was conducted to identify studies on clinically validated navigation and robotic systems published between 2000 and May 2024. The PubMed, Embase, Cochrane Library, and Web of Science databases were searched. Data on navigation devices were extracted and analyzed. The levels of autonomy in surgical robotics (LASR) classification system (from 1 to 5) was used to analyze automation. A novel taxonomy, the Levels of Integration of Advanced Imaging and AI (LIAI2) classification system, was created to categorize the integration of imaging technologies and AI (from 1 to 5). These two scores were combined into an aggregate score (from 1 to 10) to reflect the autonomy in percutaneous image-guided intervention.

RESULTS

The review included 20 studies assessing two navigation systems and eight robotic devices. The median LASR score was 1 (Q1, Q3: 1, 1), the median LIAI2 score was 2 (Q1, Q3: 2, 3), and the median aggregate score was 3 (Q1, Q3: 3, 4). Only one robotic system (10 % of those reviewed) achieved the highest LASR qualification in the literature, a level 2/5. Four systems (40 %) shared the highest rating for LIAI2, which was a score of 3/5. Four systems (40 %) achieved the highest aggregate scores of 4/10.

CONCLUSION

None of the navigation and robotic systems achieved full autonomy for percutaneous image-guided intervention. The LASR and LIAI2 scales can guide innovation by identifying areas for further development and integration.

摘要

目的

导航和机器人系统旨在提高经皮图像引导介入手术的准确性和效率，但对其自主性以及先进成像技术与人工智能（AI）整合的评估尚显不足。本研究的目的是使用既定和新颖的指标对经皮图像引导介入手术的导航和机器人系统中先进成像技术与人工智能的自动化水平和整合情况进行评估，以分类并比较它们的能力。

材料与方法

遵循PRISMA指南，进行了一项系统综述，以识别2000年至2024年5月间发表的关于临床验证的导航和机器人系统的研究。检索了PubMed、Embase、Cochrane图书馆和科学引文索引数据库。提取并分析了有关导航设备的数据。使用手术机器人自主性水平（LASR）分类系统（从1到5）来分析自动化程度。创建了一种新颖的分类法，即先进成像与人工智能整合水平（LIAI2）分类系统，用于对成像技术和人工智能的整合情况进行分类（从1到5）。将这两个分数合并为一个总分（从1到10），以反映经皮图像引导介入手术中的自主性。

结果

该综述纳入了20项评估两个导航系统和八个机器人设备的研究。LASR分数中位数为1（第一四分位数，第三四分位数：1, 1），LIAI2分数中位数为2（第一四分位数，第三四分位数：2, 3），总分中位数为3（第一四分位数，第三四分位数：3, 4）。在文献中，只有一个机器人系统（占所审查系统的10%）达到了最高的LASR级别，即2/5级。四个系统（40%）在LIAI2方面获得了最高评级，分数为3/5。四个系统（40%）获得了最高总分4/10。