Learning curve of robot-assisted surgery

INTRODUCTION. The learning curve is a period, during which surgical skills are improved through various training and educational techniques. The duration of the learning curve is characterized by the minimum number of completed operations necessary to reach a plateau of satisfactory results. The speed and widespread use of minimally invasive technologies in various sections of surgery necessitate a detailed study of learning curves. It is important to study the process of mastering new surgical techniques, since it is associated with possible complications during surgical interventions. As the number of surgical interventions using robot-assisted technologies has increased, the importance of evaluating surgical skills has also increased. It is important to repeatedly evaluate the surgical skills of each surgeon who performs robot-assisted surgery to determine that surgeon’s current position on the learning curve.

The OBJECTIVE was to conduct a systematic review of the literature devoted to the analysis of the learning curve in robot-assisted surgical interventions.

METHODS AND MATERIALS. A systematic review of available scientific articles on this topic has been carried out. When searching for the necessary articles to conduct a literary review on this topic, such platforms as PubMed, Elibrary, BSMU Scientific Library, Cyber Leninka, etc. were used.

RESULTS. During the period from 2014 to 2024, 56 articles were studied during the literary review, of which 50 articles by foreign authors and 6 articles by Russian authors. Parameters such as the time of surgery, the amount of blood loss, the duration of the inpatient period, the frequency of complications, as well as the rate of recovery of patients after surgery and the quality of life of patients were evaluated.

CONCLUSIONS. Despite significant progress, a number of unresolved issues remain, such as the standardization of learning curve parameters and the development of unified approaches to assessing surgical skills. The introduction of training programs, the use of simulators, and mentoring are key factors contributing to reducing the learning curve and improving patient outcomes. Future research should focus on the development of standardized training protocols and the introduction of new technologies such as artificial intelligence to objectively evaluate surgical skills.

1. Unguryan V. M., Kruglov E. A., Pobedintseva Yu. A. Learning curves in minimally invasive oncosurgery. Endoscopic surgery. 2020;26(4):54– 58. (In Russ.). https://doi.org/10.17116/endoskop20202604154.

2. Ryabov M. A., Byadretdinov I. Sh., Kotov S. V. Learning curve of laparoscopic and robot-assisted radical prostatectomy. Experimental and clinical urology. 2021;14(4):37–43. (In Russ.). https://doi.org/10.29188/22228543-2021-14-4-37-43.

3. Talyshinsky A. E., Guliev B. G., Kamyshanskaya I. G., et al. Analysis of deep learning approaches for automated extraction and segmentation of the prostate gland: a literature review. Oncourology. 2023;19(2):101–110. (In Russ.). https://doi.org/10.17650/1726-9776-2023-19-2-101-110.

4. Shvedov A. M., Kolontarev K. B., Bormotin A. V., et al. Risk factors for urinary incontinence in patients who underwent radical robot-assisted prostatectomy. Bulletin of Urology. 2023;11(1):150–158. (In Russ.).

5. Medvedev V. L. Robot-assisted laparoscopic radical prostatectomy. Bulletin of Urology. 2018;6(4):67–76. (In Russ.). https://doi.org/10.21886/2308-6424-2017-6-4-67-76.

6. Sung H., Ferlay J., Siegel R. L. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians. 2021;71(3):209–249. https://doi.org/10.3322/caac.21660.

7. Lee D., Yu H. W., Kwon H. et al. Evaluation of Surgical Skills during Robotic Surgery by Deep Learning-Based Multiple Surgical Instrument Tracking in Training and Actual Operations. Journal of Clinical Medicine. 2020;9(6):1964. https://doi.org/10.3390/jcm9061964.

8. Niu S., Ao L., Gao Y. et al. Suitability of the MP1000 Platform for Robotassisted Prostatectomy: A Prospective Randomised Controlled Trial. European Urology Open Science. 2024;64:2–8. https://doi.org/10.1016/j.euros.2024.02.017.

9. Thakker P. U., Sandberg M., Hemal A. K., Rodriguez A. R. A Comprehensive Review of the Current State of Robot-assisted Laparoscopic Salvage Prostatectomy. International Brazilian Journal of Urology. 2024;50(4):398–414. https://doi.org/10.1590/S1677-5538.IBJU.2024.0126.

10. Yeolekar A., Qadri H. The Learning Curve in Surgical Practice and Its Applicability to Rhinoplasty. Indian Journal of Otolaryngology and Head & Neck Surgery. 2018;70(1):38–42. https://doi.org/10.1007/s12070-0171199-x.

11. Salim A., Moazzam Z., Dogar S. A., Qazi S. H. Simulation-based Training in the Paediatric Surgery Population: A Review of Current Trends and Future Direction. Journal of the Pakistan Medical Association. 2021;71(Suppl. 1):S38–S41.

12. Wilcox Vanden Berg R. N., Vertosick E. A., Sjoberg D. D. et al. Implementation and Validation of an Automated, Longitudinal Robotic Surgical Evaluation and Feedback Program at a High-volume Center and Impact on Training. European Urology Open Science. 2024;(62):81–90. https://doi.org/10.1016/j.euros.2024.02.014.

13. Ambinder D., Wang S., Siddiqui M. M. Determining the Componentbased Operative Time Learning Curve for Robotic-assisted Radical Prostatectomy. Current Urology. 2022;16(4):240–245. https://doi.org/10.1097/CU9.0000000000000119.

14. Pires R. D. S., Pereira C. W. A., Favorito L. A. Is the Learning Curve of the Urology Resident for Conventional Radical Prostatectomy Similar to That of Staff Initiating Robot-assisted Radical Prostatectomy? International Brazilian Journal of Urology. 2024;50(3):335–345. https://doi.org/10.1590/S1677-5538.IBJU.2024.9909.

15. Lee R. S., Ma R., Pham S. et al. Machine Learning to Delineate Surgeon and Clinical Factors That Anticipate Positive Surgical Margins After Robot-Assisted Radical Prostatectomy. Journal of Endourology. 2022;36(9):1192–1198. https://doi.org/10.1089/end.2021.0890.

16. Song C., Cheng L., Li Y. et al. Systematic Literature Review of Costeffectiveness Analyses of Robotic-assisted Radical Prostatectomy for Localised Prostate Cancer. BMJ Open. 2022;12(9):e058394. https://doi.org/10.1136/bmjopen-2021-058394.

17. Bock D., Nyberg M., Lantz A. et al. Learning Curve for Robot-assisted Laparoscopic Radical Prostatectomy in a Large Prospective Multicentre Study. Scandinavian Journal of Urology. 2022;56(3):182–190. https://doi.org/10.1080/21681805.2022.2070274.

18. Anceschi U., Galfano A., Luciani L. et al. Analysis of Predictors of Early Trifecta Achievement After Robot-assisted Radical Prostatectomy for Trainers and Expert Surgeons: The Learning Curve Never Ends. Minerva Urology and Nephrology. 2022;74(2):133–136. https://doi.org/10.23736/S2724-6051.22.04805-4.

19. Cassim R., Millan B., Guo Y. et al. Minimizing the Learning Curve for Robotic-assisted Radical Cystectomy: A Single-surgeon, Retrospective, Cohort Study. Canadian Urological Association Journal. 2023; 17(9):E252–E256. https://doi.org/10.5489/cuaj.8279.

20. Chahal B., Aydin A., Amin M. S. A. et al. The Learning Curves of Major Laparoscopic and Robotic Procedures in Urology: A Systematic Review. International Journal of Surgery. 2023;109(7):2037–2057. https://doi.org/10.1097/JS9.0000000000000345.

21. Fainberg J., Vanden Berg R. N. W., Chesnut G. et al. A Novel Expert Coaching Model in Urology, Aimed at Accelerating the Learning Curve in Robotic Prostatectomy. Journal of Surgical Education. 2022;79(6):1480–1488. https://doi.org/10.1016/j.jsurg.2022.06.006.

22. Esperto F., Prata F., Antonelli A. et al. Bioethical Implications of Robotic Surgery in Urology: A Systematic Review. Minerva Urology and Nephrology. 2021;73(6):700–710. https://doi.org/10.23736/S27246051.21.04240-3.

23. Thomas B. C., Slack M., Hussain M. et al. Preclinical Evaluation of the Versius Surgical System, a New Robot-assisted Surgical Device for Use in Minimal Access Renal and Prostate Surgery. European Urology Focus. 2021;7(2):444–452. https://doi.org/10.1016/j.euf.2020.01.011.

24. Raison N., Harrison P., Abe T. et al. Procedural Virtual Reality Simulation Training for Robotic Surgery: A Randomised Controlled Trial. Surgical Endoscopy. 2021;35(12):6897–6902. https://doi.org/10.1007/s00464020-08197-w.

25. Luongo F., Hakim R., Nguyen J. H. et al. Deep Learning-based Computer Vision to Recognize and Classify Suturing Gestures in Robot-assisted Surgery. Surgery. 2021;169(5):1240–1244. https://doi.org/10.1016/j.surg.2020.08.016.

26. Gandi C., Totaro A., Bientinesi R. et al. A Multi-surgeon Learning Curve Analysis of Overall and Site-specific Positive Surgical Margins After RARP and Implications for Training. Journal of Robotic Surgery. 2022;16(6):1451–1461. https://doi.org/10.1007/s11701-022-01378-w.

27. Wang F., Zhang C., Guo F. et al. The Application of Virtual Reality Training for Anastomosis During Robot-assisted Radical Prostatectomy. Asian Journal of Urology. 2021;8(2):204–208. https://doi.org/10.1016/j.ajur.2019.11.005.

28. Yu C., Xu L., Ye L. et al. Single-port Robot-assisted Perineal Radical Prostatectomy with the da Vinci XI System: Initial Experience and Learning Curve Using the Cumulative Sum Method. World Journal of Surgical Oncology. 2023;21(1):46. https://doi.org/10.1186/s12957-023-02927-9.

29. Ju G. Q., Wang Z. J., Shi J. Z. et al. A Comparison of Perioperative Outcomes Between Extraperitoneal Robotic Single-port and Multiport Radical Prostatectomy with the da Vinci Si Surgical System. Asian Journal of Andrology. 2021;23(6):640–647. https://doi.org/10.4103/aja. aja_50_21.

30. Dell’Oglio P., Tappero S., Andras I. et al. Comment on: “The Surgical Learning Curve for Salvage Robot-assisted Radical Prostatectomy: A Prospective Single-surgeon Study”. Minerva Urology and Nephrology. 2021;73(5):680–682. https://doi.org/10.23736/S2724-6051.21.04721-2.

31. Hughes T., Rai B., Madaan S. et al. The Availability, Cost, Limitations, Learning Curve and Future of Robotic Systems in Urology and Prostate Cancer Surgery. Journal of Clinical Medicine. 2023;12(6):2268. https://doi.org/10.3390/jcm12062268.

32. Shelton T. M., Drake C., Vasquez R., Rivera M. Comparison of Contemporary Surgical Outcomes Between Holmium Laser Enucleation of the Prostate and Robotic-Assisted Simple Prostatectomy. Current Urology Reports. 2023;24(5):221–229. https://doi.org/10.1007/s11934023-01146-9.

33. Hashine K., Tada K., Minato R. et al. Patient-reported Outcomes After Robot-assisted Radical Prostatectomy and Institutional Learning Curve for Functional Outcomes. Urology Annals. 2023;15(1):60–67. https://doi.org/10.4103/ua.ua_75_22.

34. Cysouw M. C. F., Jansen B. H. E., van de Brug T. et al. Machine Learningbased Analysis of [18F]DCFPyL PET Radiomics for Risk Stratification in Primary Prostate Cancer. European Journal of Nuclear Medicine and Molecular Imaging. 2021;48(2):340–349. https://doi.org/10.1007/s00259-020-04971-z.

35. Flynn J., Larach J. T., Kong J. C. H. et al. The learning curve in robotic colorectal surgery compared with laparoscopic colorectal surgery: a systematic review. Colorectal Dis. 2021;23(11):2806–2820. https://doi.org/10.1111/codi.15843.

36. Lee M. R., Li W. M., Li C. C. et al. Cumulative Sum Analysis of the Learning Curve of Laparoendoscopic Single-site Robot-assisted Radical Prostatectomy. Asian Journal of Surgery. 2023;46(9):3614–3619. https://doi.org/10.1016/j.asjsur.2023.02.035.

37. Bravi C. A., Dell’Oglio P., Mazzone E. et al. The Surgical Learning Curve for Biochemical Recurrence After Robot-assisted Radical Prostatectomy. European Urology Oncology. 2023;6(4):414–421. https://doi.org/10.1016/j.euo.2022.06.010.

38. Ryan J. P. C., Lynch O., Broe M. P. et al. Robotic-assisted Radical Prostatectomy-impact of a Mentorship Program on Oncological Outcomes During the Learning Curve. Irish Journal of Medical Science. 2022;191(1):479–484. https://doi.org/10.1007/s11845-021-02556-9.

39. Bravi C. A., Dell’Oglio P., Piazza P. et al. Positive Surgical Margins After Anterior Robot-assisted Radical Prostatectomy: Assessing the Learning Curve in a Multi-institutional Collaboration. European Urology Oncology. 2024;7(4):821–828. https://doi.org/10.1016/j.euo.2023.11.006.

40. Lin Y. C., Yuan L. H., Tseng C. S. et al. Comparison of Senhance and da Vinci Robotic Radical Prostatectomy: Short-term Outcomes, Learning Curve, and Cost Analysis. Prostate Cancer and Prostatic Diseases. 2024;27(1):116–121. https://doi.org/10.1038/s41391-023-00717-8.

41. Carlos A. F., Dario V. M., Popescu R. I. et al. Robot-Assisted Radical Prostatectomy (RARP) Trifecta Learning Curve for Surgeons with Previous Experience in Laparoscopy. Medicina. 2024;60(7):1032. https://doi.org/10.3390/medicina60071032.

42. Walker R. J. B., Stukel T. A., de Mestral C. et al. Hospital Learning Curves for Robot-assisted Surgeries: A Population-based Analysis. Surgical Endoscopy. 2024;38(3):1367–1378. https://doi.org/10.1007/s00464-023-10625-6.

43. López-Abad A., Server Gómez G., Loyola Maturana J. P. et al. Comparative Evaluation of Continence and Potency After Radical Prostatectomy: Robotic vs. Laparoscopic Approaches, Validating LAP01 Trial. Surgical Oncology. 2024;55:102098. https://doi.org/10.1016/j.suronc.2024.102098.

44. Wong S. W., Crowe P. Factors Affecting the Learning Curve in Robotic Colorectal Surgery. Journal of Robotic Surgery. 2022;16(6):1249–1256. https://doi.org/10.1007/s11701-022-01373-1.

45. Pernar L. I. M., Robertson F. C., Tavakkoli A. et al. An Appraisal of the Learning Curve in Robotic General Surgery. Surgical Endoscopy. 2017;31(11):4583–4596. https://doi.org/10.1007/s00464-017-5520-2.

46. Wong S. W., Ang Z. H., Crowe P. The Learning Curve to Attain Surgical Competency in Robotic Colorectal Surgery. ANZ Journal of Surgery. 2022;92(5):1117–1124. https://doi.org/10.1111/ans.17449.

47. Ma J., Yang J., Cheng S. et al. The Learning Curve of Laparoendoscopic Single-Site Surgery in Benign Gynecological Diseases. Journal of Investigative Surgery. 2022;35(2):363–370. https://doi.org/10.1080/08941939.2020.1867673.

48. de Rezende B. B., Assumpção L. R., Haddad R. et al. Characteristics of the Learning Curve in Robotic Thoracic Surgery in an Emerging Country. Journal of Robotic Surgery. 2023;17(4):1809–1816. https://doi.org/10.1007/s11701-023-01590-2.

49. Grange P., Mulla M. Learning the “Learning Curve”. Surgery. 2015;157(1):8–9. https://doi.org/10.1016/j.surg.2014.07.017.

50. Arnold B. N., Thomas D. C., Bhatnagar V. et al. Defining the Learning Curve in Robot-assisted Thoracoscopic Lobectomy. Surgery. 2019;165(2):450–454. https://doi.org/10.1016/j.surg.2018.06.011.

51. Lee D., Yu H. W., Kwon H. et al. Evaluation of Surgical Skills during Robotic Surgery by Deep Learning-Based Multiple Surgical Instrument Tracking in Training and Actual Operations. Journal of Clinical Medicine. 2020;9(6):1964. https://doi.org/10.3390/jcm9061964.

52. Bock D., Nyberg M., Lantz A. et al. Learning curve for robot-assisted laparoscopic radical prostatectomy in a large prospective multicentre study. Scandinavian Journal of Urology. 2022;56(3):182–190. https://doi.org/10.1080/21681805.2022.2070274.

53. Ma J., Yang J., Cheng S. et al. The Learning Curve of Laparoendoscopic Single-Site Surgery in Benign Gynecological Diseases. Journal of Investigative Surgery. 2022;35(2):363–370. https://doi.org/10.1080/08941939.2020.1867673.

54. Arnold B. N., Thomas D. C., Bhatnagar V. et al. Defining the learning curve in robot-assisted thoracoscopic lobectomy. Surgery. 2019;165(2):450– 454. https://doi.org/10.1016/j.surg.2018.06.011.

55. Wong S. W., Ang Z. H., Crowe P. Factors affecting the learning curve in robotic colorectal surgery. Journal of Robotic Surgery. 2022;16(6):1249– 1256. https://doi.org/10.1007/s11701-022-01373-1.

56. Luongo F., Hakim R., Nguyen J. H. et al. Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery. Surgery. 2021;169(5):1240–1244. https://doi.org/10.1016/j.surg.2020.08.016.