Research progress and prospect on diagnosis and treatment of robotic surgery in the era of artificial intelligence_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

HUANG Sha , HE Zhehao , WANG Zhitian , WANG Luming , ZHANG Chong , LV Wang ,  HU Jian

Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P.R.China;

Corresponding author：

HU Jian, Email: dr_hujian@zju.edu.cn

Keywords：

Artificial intelligence; robotic surgery; thoracic surgery; artificial intelligence-assisted surgical procedure

DOI：

10.7507/1007-4848.201812043

Video：

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The technical combination of artificial intelligence (AI) and thoracic surgery is increasingly close, especially in the field of image recognition and pathology diagnosis. Additionally, robotic surgery, as a representative of high-end technology in minimally invasive surgery is flourishing. What progress has been or will be made in robotic surgery in the era of AI? This article aims to summarize the application status of AI in thoracic surgery and progress in robotic surgery, and looks ahead the future.

Citation： HUANG Sha, HE Zhehao, WANG Zhitian, WANG Luming, ZHANG Chong, LV Wang, HU Jian. Research progress and prospect on diagnosis and treatment of robotic surgery in the era of artificial intelligence. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2019, 26(3): 197-202. doi: 10.7507/1007-4848.201812043 Copy

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6.	伍长荣, 接标, 叶明全. CT 图像肺结节计算机辅助检测与诊断技术研究综述. 数据采集与处理, 2016, 31(5): 868-881.
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8.	王媛媛, 周涛, 吴翠颖. 基于卷积神经网络的 PET/CT 多模态图像识别研究. 电视技术, 2017, 41(3): 88-94.
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14.	Zhou ZY, Wang ZT, Zheng ZL, et al. An " alternative finger” in robotic-assisted thoracic surgery: intraoperative ultrasound localization of pulmonary nodules. Med Ultrason, 2017, 19(4): 374-379.
15.	Miyazaki T, Honjyo H, Sohda M, et al. Successful tumor navigation technique during intrathoracoscopic esophagectomy: laparoscopic ultrasonography using endoscopically placed marking clips. J Am Coll Surg, 2015, 221(6): e125-128.
16.	Dou H, Jiang S, Yang Z, et al. Design and validation of a CT-guided robotic system for lung cancer brachytherapy. Med Phys, 2017, 44(9): 4828-4837.
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24.	Kikuchi H, Takeuchi H. Future perspectives of surgery for esophageal cancer. Ann Thorac Cardiovasc Surg, 2018, 24(5): 219-222.
25.	Yarmus LB, Arias S, Feller-Kopman D, et al. Electromagnetic navigation transthoracic needle aspiration for the diagnosis of pulmonary nodules: A safety and feasibility pilot study. J Thorac Dis, 2016, 8(1): 186-194.
26.	Marino KA, Sullivan JL, Weksler B. Electromagnetic navigation bronchoscopy for identifying lung nodules for thoracoscopic resection. Ann Thorac Surg, 2016, 102(2): 454-457.
27.	Sakamoto K, Kanzaki M, Mitsuboshi S, et al. A novel and simple method for identifying the lung intersegmental plane using thermography. Interact Cardiovasc Thorac Surg, 2016, 23(1): 171-173.
28.	Marescaux J, Diana M. Next step in minimally invasive surgery: Hybrid image-guided surgery. J Pediatr Surg, 2015, 50(1): 30-36.
29.	王成勇, 谢国能, 赵丹娜, 等. 医疗手术机器人发展概况. 工具技术, 2016, 50(7): 3-10.

1. 董可男, 王楠. 智能医疗时代的曙光——人工智能+健康医疗应用概览. 大数据时代, 2017, 4: 26-37.
2. Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: General overview. Korean J Radiol, 2017, 18(4): 570-584.
3. Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 2017, 39(6): 1137-1149.
4. Cheng JZ, Ni D, Chou YH, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 15(6): 24454.
5. Chen J, Chen J, Ding HY, et al. Use of an artificial neural network to construct a model of predicting deep fungal infection in lung cancer patients. Asian Pac J Cancer Prev, 2015, 16(12): 5095-5099.
6. 伍长荣, 接标, 叶明全. CT 图像肺结节计算机辅助检测与诊断技术研究综述. 数据采集与处理, 2016, 31(5): 868-881.
7. Ciompi F, De HB, van Riel SJ, et al. Automatic clasification of pulmonary peri-fisural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Medical Image Analysis, 2015, 26(1): 195-202.
8. 王媛媛, 周涛, 吴翠颖. 基于卷积神经网络的 PET/CT 多模态图像识别研究. 电视技术, 2017, 41(3): 88-94.
9. Beck AH, Sangoi AR, Leung S, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med, 2011, 3(108): 108-113.
10. Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
11. 于观贞, 魏培莲, 陈颖, 等. 人工智能在肿瘤病理诊断和评估中的应用与思考. 第二军医大学学报, 2017, 38(11): 1349-1354.
12. Bao F, Zhang C, Yang Y, et al. Comparison of robotic and video-assisted thoracic surgery for lung cancer: a propensity-matched analysis. J Thorac Dis, 2016, 8(7): 1798-1803.
13. Marano A, Priora F, Lenti LM, et al. Application of fluorescence in robotic general surgery: Review of the literature and state of the art. World J Surg, 2013, 37(12): 2800-2811.
14. Zhou ZY, Wang ZT, Zheng ZL, et al. An " alternative finger” in robotic-assisted thoracic surgery: intraoperative ultrasound localization of pulmonary nodules. Med Ultrason, 2017, 19(4): 374-379.
15. Miyazaki T, Honjyo H, Sohda M, et al. Successful tumor navigation technique during intrathoracoscopic esophagectomy: laparoscopic ultrasonography using endoscopically placed marking clips. J Am Coll Surg, 2015, 221(6): e125-128.
16. Dou H, Jiang S, Yang Z, et al. Design and validation of a CT-guided robotic system for lung cancer brachytherapy. Med Phys, 2017, 44(9): 4828-4837.
17. Popescu T, Kacso AC, Pisla D, et al. Brachytherapy next generation: Robotic systems. J Contemp Brachytherapy, 2015, 7(6): 510-514.
18. Meershoek P, KleinJan GH, van Oosterom MN, et al. Multispectral fluorescence imaging as a tool to separate healthy and disease related lymphatic anatomies during robot-assisted laparoscopic procedures. J Nucl Med, 2018, 59(11): 1757-1760.
19. Zuo S, Yang GZ. Endomicroscopy for computer and robot assisted intervention. IEEE Rev Biomed Eng, 2017, 10: 12-25.
20. Miyashita K, Oude Vrielink T, Mylonas G. A cable-driven parallel manipulator with force sensing capabilities for high-accuracy tissue endomicroscopy. Int J Comput Assist Radiol Surg, 2018, 13(5): 659-669.
21. Giataganas P, Hughes M, Yang GZ. Force adaptive robotically assisted endomicroscopy for intraoperative tumour identification. Int J Comput Assist Radiol Surg, 2015, 10(6): 825-832.
22. Giataganas P, Hughes M, Payne C, et al. Intraoperative robotic-assisted large-area high-speed microscopic imaging and intervention. IEEE Trans Biomed Eng, 2019, 66(1): 208-216.
23. Lopez A, Zlatev DV, Mach KE, et al. Intraoperative optical biopsy during robotic assisted radical prostatectomy using confocal endomicroscopy. J Urol, 2016, 195(4): 1110-1117.
24. Kikuchi H, Takeuchi H. Future perspectives of surgery for esophageal cancer. Ann Thorac Cardiovasc Surg, 2018, 24(5): 219-222.
25. Yarmus LB, Arias S, Feller-Kopman D, et al. Electromagnetic navigation transthoracic needle aspiration for the diagnosis of pulmonary nodules: A safety and feasibility pilot study. J Thorac Dis, 2016, 8(1): 186-194.
26. Marino KA, Sullivan JL, Weksler B. Electromagnetic navigation bronchoscopy for identifying lung nodules for thoracoscopic resection. Ann Thorac Surg, 2016, 102(2): 454-457.
27. Sakamoto K, Kanzaki M, Mitsuboshi S, et al. A novel and simple method for identifying the lung intersegmental plane using thermography. Interact Cardiovasc Thorac Surg, 2016, 23(1): 171-173.
28. Marescaux J, Diana M. Next step in minimally invasive surgery: Hybrid image-guided surgery. J Pediatr Surg, 2015, 50(1): 30-36.
29. 王成勇, 谢国能, 赵丹娜, 等. 医疗手术机器人发展概况. 工具技术, 2016, 50(7): 3-10.

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