Application and research progress of artificial intelligence technology in trauma treatment_Chinese Journal of Reparative and Reconstructive Surgery

Authors：

ZHANG Heng ^1,2 , MA Xiaodong ¹ , WANG Jiaqin ³ , GUAN Jianzhong ¹ , LI Kuanxin ¹ ,  ZHAO Jianning ² ,  ZHOU Jiansheng ¹

1. Department of Orthopedics, Laboratory of Tissue and Transplant in Anhui Province, the First Affiliated Hospital of Bengbu Medical College, Bengbu Anhui, 233004, P. R. China;
2. Department of Orthopedics, Jinling Hospital, School of Medicine, Nanjing University, Nanjing Jiangsu, 210002, P. R. China;
3. School of Life Sciences, Bengbu Medical College, Bengbu Anhui, 233030, P. R. China;

Corresponding author：

ZHAO Jianning, Email: zhaojianning.0207@163.com; ZHOU Jiansheng, Email: zhoujs12399@163.com

Keywords：

Artificial intelligence; machine learning; trauma treatment; prediction model; surgical robot

DOI：

10.7507/1002-1892.202308003

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To review the application and research progress of artificial intelligence (AI) technology in trauma treatment. Methods The recent research literature on the application of AI and related technologies in trauma treatment was reviewed and summarized in terms of prehospital assistance, in-hospital emergency care, and post-traumatic stress disorder risk regression prediction, meanwhile, the development trend of AI technology in trauma treatment were outlooked. Results The AI technology can rapidly analyze and manage large amount of clinical data to help doctors identify patients’ situation of trauma and predict the risk of possible complications more accurately. The application of AI technology in surgical assistance and robotic operations can achieve precise surgical plan and treatment, reduce surgical risks, and shorten the operation time, so as to improve the efficiency and long-term effectiveness of the trauma treatment. Conclusion There is a promising future for the application of AI technology in the trauma treatment. However, it is still in the stage of exploration and development, and there are many difficulties of historical data bias, application condition limitations, as well as ethical and moral issues need to be solved.

Citation： ZHANG Heng, MA Xiaodong, WANG Jiaqin, GUAN Jianzhong, LI Kuanxin, ZHAO Jianning, ZHOU Jiansheng. Application and research progress of artificial intelligence technology in trauma treatment. Chinese Journal of Reparative and Reconstructive Surgery, 2023, 37(11): 1431-1437. doi: 10.7507/1002-1892.202308003 Copy

1.	Zhang B, Zhu J, Su H. Toward the third generation artificial intelligence. Science China Information Sciences, 2023, 66(2): 1-19.
2.	谷向民, 李志辉, 何忠杰, 等. 白金十分钟自救互救实践创新展现新时代人文精神价值. 军民两用技术与产品, 2021(9): 68-72.
3.	Murphy M, McCloughen A, Curtis K. The impact of simulated multidisciplinary Trauma Team Training on team performance: A qualitative study. Australasian Emergency Care, 2019, 22(1): 1-7.
4.	Jin Q, Lin R, Yang F. BroadGAN: Generative adversarial networks of discriminating separate features based on broad learning. Engineering Applications of Artificial Intelligence, 2022, 109: 1-10.
5.	Fei N, Lu Z, Gao Y, et al. Towards artificial general intelligence via a multimodal foundation model. Nature Communications, 2022, 13(1): 1-13.
6.	Elamrani Abou Elassad Z, Mousannif H, Al Moatassime H. Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study. Traffic Injury Prevention, 2020, 21(3): 201-208.
7.	Stonko DP, Dennis BM, Betzold RD, et al. Artificial intelligence can predict daily trauma volume and average acuity. J Trauma Acute Care Surg, 2018, 85(2): 393-397.
8.	张晗, 张爱舷, 罗嘉琪, 等. 基于机器学习的创伤性休克患者院内死亡预测模型研究. 解放军医学院学报, 2023, 44(4): 339-344, 371.
9.	Divya S, Indumathi V, Ishwarya S, et al. A self-diagnosis medical chatbot using artificial intelligence. Journal of Web Development and Web Designing, 2018, 3(1): 1-7.
10.	Liang Y, Liu Y, Liu B, et al. Deep learning-based medical information system in first aid of surgical trauma. Computational and Mathematical Methods in Medicine, 2022, 2022: 8789920.
11.	Dairi A, Zerrouki N, Harrou F, et al. EEG-based mental tasks recognition via a deep learning-driven anomaly detector. Diagnostics, 2022, 12(12): 2984.
12.	Lee DH, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. Int J Environ Res Public Health, 2021, 18(1): 271.
13.	Wang L, Chen XH, Ling WH, et al. Application of trauma time axis management in the treatment of severe trauma patients. Chinese Journal of Traumatology, 2021, 24(1): 39-44.
14.	Wu J, Yang X, Gao J, et al. Application of MRI and CT energy spectrum imaging in hand and foot tendon lesions. J Med Syst, 2019, 43(5): 116.
15.	刘想, 谢辉辉, 许玉峰, 等. 人工智能在胸部创伤肋骨骨折CT诊断中应用的初步研究. 上海交通大学学报 (医学版), 2021, 41(7): 920-925.
16.	赵佳琦, 徐琪, 章建全, 等. 骨骼肌超声诊断迈向人工智能新领域: 计算机辅助骨骼肌损伤超声定量诊断. 第二军医大学学报, 2017, 38(10): 1217-1224.
17.	Molinari F, Caresio C, Acharya UR, et al. Advances in quantitative muscle ultrasonography using texture analysis of ultrasound images. Ultrasound Med Biol, 2015, 41(9): 2520-2532.
18.	Sun Y, Shi J, Sun L, et al. Image reconstruction through dynamic scattering media based on deep learning. Opt Express, 2019, 27(11): 16032-16046.
19.	Higaki T, Nakamura Y, Zhou J, et al. Deep learning reconstruction at CT: Phantom study of the image characteristics. Acad Radiol, 2020, 27(1): 82-87.
20.	Kassahun Y, Yu B, Tibebu AT, et al. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions. Int J Comput Assist Radiol Surg, 2016, 11(4): 553-568.
21.	Ryan H, Tsuda S. History of and current systems in robotic surgery//Essentials of Robotic Surgery. Cham: Springer International Publishing, 2014: 1-12.
22.	Li N, Zhu Z, Xiao C, et al. The efficacy of “TiRobot” orthopaedic robot-assisted vs conventional fluoroscopic percutaneous screw fixation of the sacroiliac joint. International Orthopaedics, 2023, 47(2): 351-358.
23.	Sutherland GR, Latour I, Greer AD. Integrating an image-guided robot with intraoperative MRI. IEEE Engineering in Medicine and Biology Magazine, 2008, 27(3): 59-65.
24.	DiMaio S, Hanuschik M, Kreaden U. The da Vinci surgical system. Surgical Robotics: Systems Applications and Visions, 2011: 199-217.
25.	Blavier A, Gaudissart Q, Cadiere GB, et al. Impact of 2D and 3D vision on performance of novice subjects using da Vinci robotic system. Acta Chirurgica Belgica, 2006, 106(6): 662-664.
26.	Mega S, Bono MC, Castiglione I, et al. Robotic assisted mitral valve repair: early experience with the da Vinci S robotic system. European Heart Journal, 2013, 34(suppl_1): 979-979.
27.	Kira S, Mitsui T, Sawada N, et al. Feasibility and necessity of the fourth arm of the da Vinci Si surgical system for robot—assisted partial nephrectomy. Int J Med Robot, 2020, 16(3): e2092.
28.	Kim DH, Kim H, Kwak S, et al. The settings, pros and cons of the new surgical robot da Vinci Xi system for transoral robotic surgery (TORS): a comparison with the popular da Vinci Si system. Surg Laparosc Endosc Percutan Tech, 2016, 26(5): 391-396.
29.	Huang YM, Huang YJ, Wei PL. Colorectal cancer surgery using the Da Vinci Xi and Si systems: comparison of perioperative outcomes. Surgical Innovation, 2019, 26(2): 192-200.
30.	Wilson TG. Advancement of technology and its impact on urologists: release of the daVinci Xi, a new surgical robot. Eur Urol, 2014, 66(5): 793-794.
31.	Tsuda S, Oleynikov D, Gould J, et al. SAGES TAVAC safety and effectiveness analysis: da Vinci^® surgical system (Intuitive Surgical, Sunnyvale, CA). Surg Endosc, 2015, 29(10): 2873-2884.
32.	Funk E, Goldenberg D, Goyal N. Demonstration of transoral robotic supraglottic laryngectomy and total laryngectomy in cadaveric specimens using the Medrobotics Flex System. Head Neck, 2017, 39(6): 1218-1225.
33.	Samalavicius NE, Dulskas A, Janusonis V, et al. Robotic colorectal surgery using the Senhance^® robotic system: a single center experience. Tech Coloproctol, 2022, 26(6): 437-442.
34.	Li J, Tien CJ, Kassick M, et al. Investigating the efficacy of simulation-based education for interstitial gynecologic brachytherapy using a novel US/MR/CT-compatible gynecologic phantom. Brachytherapy, 2022, 21(6): S38.
35.	Fan M, Liu Y, Tian W. Internal fixation in upper cervical spinal surgery: a randomized controlled study. EPiC Series in Health Sciences, 2018, 2: 51-55.
36.	Prokhorenko L, Klimov D, Mishchenkov D, et al. Modular robot interface for a smart operating theater. J Robot Surg, 2023, 17(4): 1721-1733.
37.	Liu H, Petukhova M V, Sampson N A, et al. Association of DSM-Ⅳposttraumatic stress disorder with traumatic experience type and history in the World Health Organization World Mental Health Surveys. JAMA psychiatry, 2017, 74(3): 270-281.
38.	Malgaroli M, Schultebraucks K. Artificial intelligence and posttraumatic stress disorder (PTSD). European Psychologist, 2021. doi: 10.1027/1016-9040/a000423.
39.	邓傲骞, 杨燕贻, 李云静, 等. 用机器学习算法预测长沙消防员患创伤后应激障碍的风险. 中南大学学报 (医学版), 2023, 48(1): 84-91.
40.	Karas C, Manning K, Childress DT, et al. Evaluating the safety of trough versus area under the curve (AUC)-based dosing method of vancomycin with concomitant piperacillin-tazobactam. J Pharm Technol, 2022, 38(4): 218-224.
41.	Malgaroli M, Schultebraucks K. Artificial intelligence and posttraumatic stress disorder (PTSD). European Psychologist, 2021, 25(4): 272-282.
42.	Li X. Artificial intelligence neural network based on intelligent diagnosis. Journal of Ambient Intelligence and Humanized Computing, 2021, 12: 923-931.
43.	Johnson KB, Wei WQ, Weeraratne D, et al. Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 2021, 14(1): 86-93.

1. Zhang B, Zhu J, Su H. Toward the third generation artificial intelligence. Science China Information Sciences, 2023, 66(2): 1-19.
2. 谷向民, 李志辉, 何忠杰, 等. 白金十分钟自救互救实践创新展现新时代人文精神价值. 军民两用技术与产品, 2021(9): 68-72.
3. Murphy M, McCloughen A, Curtis K. The impact of simulated multidisciplinary Trauma Team Training on team performance: A qualitative study. Australasian Emergency Care, 2019, 22(1): 1-7.
4. Jin Q, Lin R, Yang F. BroadGAN: Generative adversarial networks of discriminating separate features based on broad learning. Engineering Applications of Artificial Intelligence, 2022, 109: 1-10.
5. Fei N, Lu Z, Gao Y, et al. Towards artificial general intelligence via a multimodal foundation model. Nature Communications, 2022, 13(1): 1-13.
6. Elamrani Abou Elassad Z, Mousannif H, Al Moatassime H. Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study. Traffic Injury Prevention, 2020, 21(3): 201-208.
7. Stonko DP, Dennis BM, Betzold RD, et al. Artificial intelligence can predict daily trauma volume and average acuity. J Trauma Acute Care Surg, 2018, 85(2): 393-397.
8. 张晗, 张爱舷, 罗嘉琪, 等. 基于机器学习的创伤性休克患者院内死亡预测模型研究. 解放军医学院学报, 2023, 44(4): 339-344, 371.
9. Divya S, Indumathi V, Ishwarya S, et al. A self-diagnosis medical chatbot using artificial intelligence. Journal of Web Development and Web Designing, 2018, 3(1): 1-7.
10. Liang Y, Liu Y, Liu B, et al. Deep learning-based medical information system in first aid of surgical trauma. Computational and Mathematical Methods in Medicine, 2022, 2022: 8789920.
11. Dairi A, Zerrouki N, Harrou F, et al. EEG-based mental tasks recognition via a deep learning-driven anomaly detector. Diagnostics, 2022, 12(12): 2984.
12. Lee DH, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. Int J Environ Res Public Health, 2021, 18(1): 271.
13. Wang L, Chen XH, Ling WH, et al. Application of trauma time axis management in the treatment of severe trauma patients. Chinese Journal of Traumatology, 2021, 24(1): 39-44.
14. Wu J, Yang X, Gao J, et al. Application of MRI and CT energy spectrum imaging in hand and foot tendon lesions. J Med Syst, 2019, 43(5): 116.
15. 刘想, 谢辉辉, 许玉峰, 等. 人工智能在胸部创伤肋骨骨折CT诊断中应用的初步研究. 上海交通大学学报 (医学版), 2021, 41(7): 920-925.
16. 赵佳琦, 徐琪, 章建全, 等. 骨骼肌超声诊断迈向人工智能新领域: 计算机辅助骨骼肌损伤超声定量诊断. 第二军医大学学报, 2017, 38(10): 1217-1224.
17. Molinari F, Caresio C, Acharya UR, et al. Advances in quantitative muscle ultrasonography using texture analysis of ultrasound images. Ultrasound Med Biol, 2015, 41(9): 2520-2532.
18. Sun Y, Shi J, Sun L, et al. Image reconstruction through dynamic scattering media based on deep learning. Opt Express, 2019, 27(11): 16032-16046.
19. Higaki T, Nakamura Y, Zhou J, et al. Deep learning reconstruction at CT: Phantom study of the image characteristics. Acad Radiol, 2020, 27(1): 82-87.
20. Kassahun Y, Yu B, Tibebu AT, et al. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions. Int J Comput Assist Radiol Surg, 2016, 11(4): 553-568.
21. Ryan H, Tsuda S. History of and current systems in robotic surgery//Essentials of Robotic Surgery. Cham: Springer International Publishing, 2014: 1-12.
22. Li N, Zhu Z, Xiao C, et al. The efficacy of “TiRobot” orthopaedic robot-assisted vs conventional fluoroscopic percutaneous screw fixation of the sacroiliac joint. International Orthopaedics, 2023, 47(2): 351-358.
23. Sutherland GR, Latour I, Greer AD. Integrating an image-guided robot with intraoperative MRI. IEEE Engineering in Medicine and Biology Magazine, 2008, 27(3): 59-65.
24. DiMaio S, Hanuschik M, Kreaden U. The da Vinci surgical system. Surgical Robotics: Systems Applications and Visions, 2011: 199-217.
25. Blavier A, Gaudissart Q, Cadiere GB, et al. Impact of 2D and 3D vision on performance of novice subjects using da Vinci robotic system. Acta Chirurgica Belgica, 2006, 106(6): 662-664.
26. Mega S, Bono MC, Castiglione I, et al. Robotic assisted mitral valve repair: early experience with the da Vinci S robotic system. European Heart Journal, 2013, 34(suppl_1): 979-979.
27. Kira S, Mitsui T, Sawada N, et al. Feasibility and necessity of the fourth arm of the da Vinci Si surgical system for robot—assisted partial nephrectomy. Int J Med Robot, 2020, 16(3): e2092.
28. Kim DH, Kim H, Kwak S, et al. The settings, pros and cons of the new surgical robot da Vinci Xi system for transoral robotic surgery (TORS): a comparison with the popular da Vinci Si system. Surg Laparosc Endosc Percutan Tech, 2016, 26(5): 391-396.
29. Huang YM, Huang YJ, Wei PL. Colorectal cancer surgery using the Da Vinci Xi and Si systems: comparison of perioperative outcomes. Surgical Innovation, 2019, 26(2): 192-200.
30. Wilson TG. Advancement of technology and its impact on urologists: release of the daVinci Xi, a new surgical robot. Eur Urol, 2014, 66(5): 793-794.
31. Tsuda S, Oleynikov D, Gould J, et al. SAGES TAVAC safety and effectiveness analysis: da Vinci^® surgical system (Intuitive Surgical, Sunnyvale, CA). Surg Endosc, 2015, 29(10): 2873-2884.
32. Funk E, Goldenberg D, Goyal N. Demonstration of transoral robotic supraglottic laryngectomy and total laryngectomy in cadaveric specimens using the Medrobotics Flex System. Head Neck, 2017, 39(6): 1218-1225.
33. Samalavicius NE, Dulskas A, Janusonis V, et al. Robotic colorectal surgery using the Senhance^® robotic system: a single center experience. Tech Coloproctol, 2022, 26(6): 437-442.
34. Li J, Tien CJ, Kassick M, et al. Investigating the efficacy of simulation-based education for interstitial gynecologic brachytherapy using a novel US/MR/CT-compatible gynecologic phantom. Brachytherapy, 2022, 21(6): S38.
35. Fan M, Liu Y, Tian W. Internal fixation in upper cervical spinal surgery: a randomized controlled study. EPiC Series in Health Sciences, 2018, 2: 51-55.
36. Prokhorenko L, Klimov D, Mishchenkov D, et al. Modular robot interface for a smart operating theater. J Robot Surg, 2023, 17(4): 1721-1733.
37. Liu H, Petukhova M V, Sampson N A, et al. Association of DSM-Ⅳposttraumatic stress disorder with traumatic experience type and history in the World Health Organization World Mental Health Surveys. JAMA psychiatry, 2017, 74(3): 270-281.
38. Malgaroli M, Schultebraucks K. Artificial intelligence and posttraumatic stress disorder (PTSD). European Psychologist, 2021. doi: 10.1027/1016-9040/a000423.
39. 邓傲骞, 杨燕贻, 李云静, 等. 用机器学习算法预测长沙消防员患创伤后应激障碍的风险. 中南大学学报 (医学版), 2023, 48(1): 84-91.
40. Karas C, Manning K, Childress DT, et al. Evaluating the safety of trough versus area under the curve (AUC)-based dosing method of vancomycin with concomitant piperacillin-tazobactam. J Pharm Technol, 2022, 38(4): 218-224.
41. Malgaroli M, Schultebraucks K. Artificial intelligence and posttraumatic stress disorder (PTSD). European Psychologist, 2021, 25(4): 272-282.
42. Li X. Artificial intelligence neural network based on intelligent diagnosis. Journal of Ambient Intelligence and Humanized Computing, 2021, 12: 923-931.
43. Johnson KB, Wei WQ, Weeraratne D, et al. Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 2021, 14(1): 86-93.

Chinese Journal of Reparative and Reconstructive Surgery

Application and research progress of artificial intelligence technology in trauma treatment

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