Development and prospect of intelligent specialized disease-specific robots for thoracic surgery_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

HUANG Xuhua ¹ , XU Jinming ¹ , WANG Xin ² , LV Wang ¹ , XIA Pinghui ¹ , WANG Yiqing ¹ ,  HU Jian ¹

1. Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P. R. China;
2. Department of Cardiothoracic Surgery, Nanyang Central Hospital, Nanyang, 473009, Henan, P. R. China;

Corresponding author：

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

Keywords：

Artificial intelligence; thoracic surgery; specialized disease-specific robots; review

DOI：

10.7507/1007-4848.202204043

Video：

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The application of robots in thoracic surgery is mainly based on the da Vinci general surgery robot. With the popularization of artificial intelligence (AI) application scenarios, the combination of AI and robots is more closely, and there is a strong clinical demand and huge application space for the development of specialized disease-specific robotic systems for thoracic surgery. This article aims to systematically describe the history of the rise of specialized surgical robots and the status of the localization of surgical robots in China, propose the concept of applying AI to the research and development of integrated specialized disease-specific robots in thoracic surgery, and clarify the ethics and prospects that intelligent specialized disease-specific surgical robots will face.

Citation： HUANG Xuhua, XU Jinming, WANG Xin, LV Wang, XIA Pinghui, WANG Yiqing, HU Jian. Development and prospect of intelligent specialized disease-specific robots for thoracic surgery. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(9): 1210-1216. doi: 10.7507/1007-4848.202204043 Copy

1.	Haidegger T, Sándor J, Benyó Z. Surgery in space: The future of robotic telesurgery. Surg Endosc, 2011, 25(3): 681-690.
2.	Kwoh YS, Hou J, Jonckheere EA, et al. A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans Biomed Eng, 1988, 35(2): 153-160.
3.	Huang J, Tian Y, Zhou QJ, et al. Comparison of perioperative outcomes of robotic-assisted versus video-assisted thoracoscopic right upper lobectomy in non-small cell lung cancer. Transl Lung Cancer Res, 2021, 10(12): 4549-4557.
4.	Testori A, Giudici VM, Voulaz E, et al. Robotic and video-assisted thoracic surgery for early-stage lung cancer: Comparison of long-term pain at a single centre. J Clin Med, 2022, 11(4): 1108.
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6.	Erbe R, Gore J, Gemmill K, et al. The use of machine learning to discover regulatory networks controlling biological systems. Mol Cell, 2022, 82(2): 260-273.
7.	Dixon F, Khanna A, Vitish-Sharma P, et al. Initiation and feasibility of a multi-specialty minimally invasive surgical programme using a novel robotic system: A case series. Int J Surg, 2021, 96: 106182.
8.	Jung M, Morel P, Buehler L, et al. Robotic general surgery: Current practice, evidence, and perspective. Langenbecks Arch Surg, 2015, 400(3): 283-292.
9.	Roche M. The MAKO robotic-arm knee arthroplasty system. Arch Orthop Trauma Surg, 2021, 141(12): 2043-2047.
10.	D'Souza M, Gendreau J, Feng A, et al. Robotic-assisted spine surgery: History, efficacy, cost, and future trends. Robot Surg, 2019, 6: 9-23.
11.	Nath A, Prashanth A, Lal H, et al. Robotic-assisted computed tomography-guided 18F-FDG PET/computed tomography-directed biopsy for diagnosis of intra thoracic lesions: An experience from a tertiary care centre in North India. Nucl Med Commun, 2020, 41(3): 246-251.
12.	Fong AJ, Stewart CL, Lafaro K, et al. Robotic assistance for quick and accurate image-guided needle placement. Updates Surg, 2021, 73(3): 1197-1201.
13.	Yang X, Lee AY, Law YM, et al. Stereotactic robot-assisted transperineal prostate biopsy under local anaesthesia and sedation: Moving robotic biopsy from operating theatre to clinic. J Robot Surg, 2020, 14(5): 767-772.
14.	Radhakrishnan RK, Mittal BR, Gorla AKR, et al. Real-time intraprocedural 18 F-FDG PET/CT-guided biopsy using automated robopsy arm (ARA) in the diagnostic evaluation of thoracic lesions with prior inconclusive biopsy results: Initial experience from a tertiary health care centre. Br J Radiol, 2017, 90(1080): 20170258.
15.	Lu M, Nath S, Semaan RW. A review of robotic-assisted bronchoscopy platforms in the sampling of peripheral pulmonary lesions. J Clin Med, 2021, 10(23): 5678.
16.	Reisenauer J, Simoff MJ, Pritchett MA, et al. Ion: Technology and techniques for shape-sensing robotic-assisted bronchoscopy. Ann Thorac Surg, 2022, 113(1): 308-315.
17.	Berkovic P, Gulyban A, Defraene G, et al. Stereotactic robotic body radiotherapy for patients with oligorecurrent pulmonary metastases. BMC Cancer, 2020, 20(1): 402.
18.	Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
19.	Seijo LM, Peled N, Ajona D, et al. Biomarkers in lung cancer screening: Achievements, promises, and challenges. J Thorac Oncol, 2019, 14(3): 343-357.
20.	Wang S, Yu H, Gan Y, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: A multicohort study. Lancet Digit Health, 2022, 4(5): e309-e319.
21.	Li J, Wu J, Zhao Z, et al. Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: A narrative review. J Thorac Dis, 2021, 13(12): 7021-7033.
22.	Siddique S, Chow JCL. Artificial intelligence in radiotherapy. Rep Pract Oncol Radiother, 2020, 25(4): 656-666.
23.	高奇琦, 吕俊延. 智能医疗: 人工智能时代对公共卫生的机遇与挑战. 电子政务, 2017, 11: 11-19.
24.	Yang GZ, Cambias J, Cleary K, et al. Medical robotics-regulatory, ethical, and legal considerations for increasing levels of autonomy. Sci Robot, 2017, 2(4): eaam8638.
25.	Skjold-Ødegaard B, Søreide K. Standardization in surgery: Friend or foe? Br J Surg, 2020, 107(9): 1094-1096.
26.	Ansmann L, Pfaff H. Providers and patients caught between standardization and individualization: Individualized standardization as a solution comment on "(re) making the procrustean bed? Standardization and customization as competing logics in healthcare". Int J Health Policy Manag, 2018, 7(4): 349-352.
27.	陈凯, 张亚杰, 项捷, 等. CT三维重建辅助达芬奇机器人解剖性肺段切除术的临床应用. 中华解剖与临床杂志, 2020, 25(3): 260-264.
28.	Zhang Q, Li S, Zhou W, et al. Application of metagenomic next-generation sequencing (mNGS) combined with rapid on-site cytological evaluation (ROSCE) for the diagnosis of Chlamydia psittaci pneumonia. Int J Clin Exp Pathol, 2021, 14(4): 389-398.
29.	Machi J, Takeda J, Sigel B, et al. Normal stomach wall and gastric cancer: Evaluation with high-resolution operative US. Radiology, 1986, 159(1): 85-87.
30.	Meijer S, Paul MA, Cuesta MA, et al. Intra-operative ultrasound in detection of liver metastases. Eur J Cancer, 1995, 31A(7-8): 1210-1211.
31.	Qu Q, Zhang Z, Guo X, et al. Novel multifunctional NIR-Ⅱ aggregation-induced emission nanoparticles-assisted intraoperative identification and elimination of residual tumor. J Nanobiotechnology, 2022, 20(1): 143.
32.	Wei J, Zhang C, Ma L, et al. Artificial intelligence algorithm-based intraoperative magnetic resonance navigation for glioma resection. Contrast Media Mol Imaging, 2022, 2022: 4147970.
33.	高奇琦. 人机合智: 机器智能和人类智能的未来相处之道. 广东社会科学, 2019, 3: 5-13, 254.
34.	Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40.
35.	Aristidou A, Jena R, Topol EJ. Bridging the chasm between AI and clinical implementation. Lancet, 2022, 399(10325): 620.

1. Haidegger T, Sándor J, Benyó Z. Surgery in space: The future of robotic telesurgery. Surg Endosc, 2011, 25(3): 681-690.
2. Kwoh YS, Hou J, Jonckheere EA, et al. A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans Biomed Eng, 1988, 35(2): 153-160.
3. Huang J, Tian Y, Zhou QJ, et al. Comparison of perioperative outcomes of robotic-assisted versus video-assisted thoracoscopic right upper lobectomy in non-small cell lung cancer. Transl Lung Cancer Res, 2021, 10(12): 4549-4557.
4. Testori A, Giudici VM, Voulaz E, et al. Robotic and video-assisted thoracic surgery for early-stage lung cancer: Comparison of long-term pain at a single centre. J Clin Med, 2022, 11(4): 1108.
5. McEvoy MA, Correll N. Materials science. Materials that couple sensing, actuation, computation, and communication. Science, 2015, 347(6228): 1261689.
6. Erbe R, Gore J, Gemmill K, et al. The use of machine learning to discover regulatory networks controlling biological systems. Mol Cell, 2022, 82(2): 260-273.
7. Dixon F, Khanna A, Vitish-Sharma P, et al. Initiation and feasibility of a multi-specialty minimally invasive surgical programme using a novel robotic system: A case series. Int J Surg, 2021, 96: 106182.
8. Jung M, Morel P, Buehler L, et al. Robotic general surgery: Current practice, evidence, and perspective. Langenbecks Arch Surg, 2015, 400(3): 283-292.
9. Roche M. The MAKO robotic-arm knee arthroplasty system. Arch Orthop Trauma Surg, 2021, 141(12): 2043-2047.
10. D'Souza M, Gendreau J, Feng A, et al. Robotic-assisted spine surgery: History, efficacy, cost, and future trends. Robot Surg, 2019, 6: 9-23.
11. Nath A, Prashanth A, Lal H, et al. Robotic-assisted computed tomography-guided 18F-FDG PET/computed tomography-directed biopsy for diagnosis of intra thoracic lesions: An experience from a tertiary care centre in North India. Nucl Med Commun, 2020, 41(3): 246-251.
12. Fong AJ, Stewart CL, Lafaro K, et al. Robotic assistance for quick and accurate image-guided needle placement. Updates Surg, 2021, 73(3): 1197-1201.
13. Yang X, Lee AY, Law YM, et al. Stereotactic robot-assisted transperineal prostate biopsy under local anaesthesia and sedation: Moving robotic biopsy from operating theatre to clinic. J Robot Surg, 2020, 14(5): 767-772.
14. Radhakrishnan RK, Mittal BR, Gorla AKR, et al. Real-time intraprocedural 18 F-FDG PET/CT-guided biopsy using automated robopsy arm (ARA) in the diagnostic evaluation of thoracic lesions with prior inconclusive biopsy results: Initial experience from a tertiary health care centre. Br J Radiol, 2017, 90(1080): 20170258.
15. Lu M, Nath S, Semaan RW. A review of robotic-assisted bronchoscopy platforms in the sampling of peripheral pulmonary lesions. J Clin Med, 2021, 10(23): 5678.
16. Reisenauer J, Simoff MJ, Pritchett MA, et al. Ion: Technology and techniques for shape-sensing robotic-assisted bronchoscopy. Ann Thorac Surg, 2022, 113(1): 308-315.
17. Berkovic P, Gulyban A, Defraene G, et al. Stereotactic robotic body radiotherapy for patients with oligorecurrent pulmonary metastases. BMC Cancer, 2020, 20(1): 402.
18. Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
19. Seijo LM, Peled N, Ajona D, et al. Biomarkers in lung cancer screening: Achievements, promises, and challenges. J Thorac Oncol, 2019, 14(3): 343-357.
20. Wang S, Yu H, Gan Y, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: A multicohort study. Lancet Digit Health, 2022, 4(5): e309-e319.
21. Li J, Wu J, Zhao Z, et al. Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: A narrative review. J Thorac Dis, 2021, 13(12): 7021-7033.
22. Siddique S, Chow JCL. Artificial intelligence in radiotherapy. Rep Pract Oncol Radiother, 2020, 25(4): 656-666.
23. 高奇琦, 吕俊延. 智能医疗: 人工智能时代对公共卫生的机遇与挑战. 电子政务, 2017, 11: 11-19.
24. Yang GZ, Cambias J, Cleary K, et al. Medical robotics-regulatory, ethical, and legal considerations for increasing levels of autonomy. Sci Robot, 2017, 2(4): eaam8638.
25. Skjold-Ødegaard B, Søreide K. Standardization in surgery: Friend or foe? Br J Surg, 2020, 107(9): 1094-1096.
26. Ansmann L, Pfaff H. Providers and patients caught between standardization and individualization: Individualized standardization as a solution comment on "(re) making the procrustean bed? Standardization and customization as competing logics in healthcare". Int J Health Policy Manag, 2018, 7(4): 349-352.
27. 陈凯, 张亚杰, 项捷, 等. CT三维重建辅助达芬奇机器人解剖性肺段切除术的临床应用. 中华解剖与临床杂志, 2020, 25(3): 260-264.
28. Zhang Q, Li S, Zhou W, et al. Application of metagenomic next-generation sequencing (mNGS) combined with rapid on-site cytological evaluation (ROSCE) for the diagnosis of Chlamydia psittaci pneumonia. Int J Clin Exp Pathol, 2021, 14(4): 389-398.
29. Machi J, Takeda J, Sigel B, et al. Normal stomach wall and gastric cancer: Evaluation with high-resolution operative US. Radiology, 1986, 159(1): 85-87.
30. Meijer S, Paul MA, Cuesta MA, et al. Intra-operative ultrasound in detection of liver metastases. Eur J Cancer, 1995, 31A(7-8): 1210-1211.
31. Qu Q, Zhang Z, Guo X, et al. Novel multifunctional NIR-Ⅱ aggregation-induced emission nanoparticles-assisted intraoperative identification and elimination of residual tumor. J Nanobiotechnology, 2022, 20(1): 143.
32. Wei J, Zhang C, Ma L, et al. Artificial intelligence algorithm-based intraoperative magnetic resonance navigation for glioma resection. Contrast Media Mol Imaging, 2022, 2022: 4147970.
33. 高奇琦. 人机合智: 机器智能和人类智能的未来相处之道. 广东社会科学, 2019, 3: 5-13, 254.
34. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40.
35. Aristidou A, Jena R, Topol EJ. Bridging the chasm between AI and clinical implementation. Lancet, 2022, 399(10325): 620.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Development and prospect of intelligent specialized disease-specific robots for thoracic surgery

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