Artificial intelligence in thoracic surgery_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

 JIANG Jie ,  GENG Guojun , ZHU Xiaolei , ZHANG Xiaowen

Department of Thoracic Surgery, First Affilated Hospital of Xiamen University, Lung Cancer Institute of Medicine School of Xiamen University, Xiamen, 361003, Fujian, P.R.China;

Corresponding author：

JIANG Jie, Email: jiangjie06@126.com; GENG Guojun, Email: ggj622@126.com

Keywords：

Artificial intelligence (AI); lung cancer; diagnosis; integration

DOI：

10.7507/1007-4848.202102020

Video：

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As an emerging technology, artificial intelligence (AI) uses human theory and technology for robots to study, develop, learn and identify human technologies. Thoracic surgeons should be aware of new opportunities that may affect their daily practice by the direct use of AI technology, or indirect use in the relevant medical fields (radiology, pathology, and respiratory medicine). The purpose of this paper is to review the application status and future development of AI associated with thoracic surgery, diagnosis of AI-related lung cancer, prognosis-assisted decision-making programs and robotic surgery. While AI technology has made rapid progress in many areas, the medical industry only accounts for a small part of AI use, and AI technology is gradually becoming widespread in the diagnosis, treatment, rehabilitation, and care of diseases. The future of AI is bright and full of innovative perspectives. The field of thoracic surgery has conducted valuable exploration and practice on AI, and will receive more and more influence and promotion from AI.

Citation： JIANG Jie, GENG Guojun, ZHU Xiaolei, ZHANG Xiaowen. Artificial intelligence in thoracic surgery. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(10): 1156-1159. doi: 10.7507/1007-4848.202102020 Copy

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15.	Hosoda K, Niihara M, Harada H, et al. Robot-assisted minimally invasive esophagectomy for esophageal cancer: Meticulous surgery minimizing postoperative complications. Ann Gastroenterol Surg, 2020, 4(6): 608-617.
16.	Hu X, Wang M. Efficacy and safety of robot-assisted thoracic surgery (RATS) compare with video-assisted thoracoscopic surgery (VATS) for lung lobectomy in patients with non-small cell lung cancer. Comb Chem High Throughput Screen, 2019, 22(3): 169-178.
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1. 向润, 李强. 肺癌“一体化诊疗、全程管理”模式的发展现状与思考—基于四川省肿瘤医院肺癌MDT团队经验. 中国肺癌杂志, 2020, 23(4): 211-215.
2. Zheng S, Cornelissen LJ, Cui X, et al. Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification. Med Phys, 2021, 48(2): 733-744.
3. Tan JR, Cheong EHT, Chan LP, et al. Implementation of an artificial intelligence-based fouble read system in capturing pulmonary nodule discrepancy in CT studies. Curr Probl Diagn Radiol, 2021, 50(2): 119-122.
4. Oh JY, Kwon SY, Yoon HI, et al. Clinical significance of a solitary ground-glass opacity (GGO) lesion of the lung detected by chest CT. Lung Cancer, 2007, 55(1): 67-73.
5. Fan L, Liu SY, Li QC, et al. Multidetector CT features of pulmonary focal ground-glass opacity: Differences between benign and malignant. Br J Radiol, 2012, 85(1015): 897-904.
6. Blanc D, Racine V, Khalil A, et al. Artificial intelligence solution to classify pulmonary nodules on CT. Diagn Interv Imaging, 2020, 101(12): 803-810.
7. Ciompi F, Chung K, van Riel SJ, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep, 2017, 7: 46479.
8. Yang W, Xia W, Xie Y, et al. Optimisation analysis of pulmonary nodule diagnostic test based on deep belief nets. IET Image Processing, 2020, 14(7): 1227-1232.
9. Krarup MMK, Krokos G, Subesinghe M, et al. Artificial intelligence for the characterization of pulmonary nodules, lung tumors and mediastinal nodes on PET/CT. Semin Nucl Med, 2021, 51(2): 143-156.
10. 张逊. 人工智能辅助肺癌诊疗一体化解决方案的临床实践与展望. 中国胸心血管外科临床杂志, 2019, 26(12): 1167-1170.
11. Heuvelmans MA, van Ooijen PMA, Ather S, et al. Lung cancer prediction by deep learning to identify benign lung nodules. Lung Cancer, 2021, 154: 1-4.
12. Yang Y, Jin G, Pang Y, et al. The diagnostic accuracy of artificial intelligence in thoracic diseases: A protocol for systematic review and meta-analysis. Medicine (Baltimore), 2020, 99(7): e19114.
13. Yamashita S, Yoshida Y, Iwasaki A. Robotic surgery for thoracic disease. Ann Thorac Cardiovasc Surg, 2016, 22(1): 1-5.
14. 方燕红, 陈加优, 王枫. CT三维重建在肺小结节行胸腔镜肺段切除术中的应用. 现代医用影像学, 2021, 30(2): 215-218.
15. Hosoda K, Niihara M, Harada H, et al. Robot-assisted minimally invasive esophagectomy for esophageal cancer: Meticulous surgery minimizing postoperative complications. Ann Gastroenterol Surg, 2020, 4(6): 608-617.
16. Hu X, Wang M. Efficacy and safety of robot-assisted thoracic surgery (RATS) compare with video-assisted thoracoscopic surgery (VATS) for lung lobectomy in patients with non-small cell lung cancer. Comb Chem High Throughput Screen, 2019, 22(3): 169-178.
17. Shademan A, Decker RS, Opfermann J, et al. Plenoptic cameras in surgical robotics: Calibration, registration, and evaluation. IEEE Int Conf Robot Autom, 2016, 2016: 708-714.
18. Chen-Yoshikawa TF, Fukui T, Nakamura S, et al. Current trends in thoracic surgery. Nagoya J Med Sci, 2020, 82(2): 161-174.
19. Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
20. Etienne H, Hamdi S, Le Roux M, et al. Artificial intelligence in thoracic surgery: Past, present, perspective and limits. Eur Respir Rev, 2020, 29(157): 200010.

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