REFERENCES

1. Kitaguchi D, Takeshita N, Matsuzaki H, et al. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc. 2020;34:4924-31.

2. Cai T, Zhao Z. Convolutional neural network-based surgical instrument detection. Technol Health Care. 2020;28:81-8.

3. Luongo F, Hakim R, Nguyen JH, Anandkumar A, Hung AJ. Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery. Surgery. 2021;169:1240-4.

4. Choksi S, Szot S, Zang C, et al. Bringing artificial intelligence to the operating room: edge computing for real-time surgical phase recognition. Surg Endosc. 2023;37:8778-84.

5. Birkmeyer JD, Finks JF, O’Reilly A, et al. Surgical skill and complication rates after bariatric surgery. N Engl J Med. 2013;369:1434-42.

6. Grewal B, Kianercy A, Gerrah R. Characterization of surgical movements as a training tool for improving efficiency. J Surg Res. 2024;296:411-7.

7. Azari DP, Frasier LL, Quamme SRP, et al. Modeling surgical technical skill using expert assessment for automated computer rating. Ann Surg. 2019;269:574-81.

8. Welcome to FLS. Fundamentals of laparoscopic surgery. Available from: https://www.flsprogram.org/about-fls/. [Last accessed on 27 Mar 2025].

9. Fuchs HF, Collins JW, Babic B, et al. Robotic-assisted minimally invasive esophagectomy (RAMIE) for esophageal cancer training curriculum-a worldwide Delphi consensus study. Dis Esophagus. 2022;35:doab055.

10. Stegemann AP, Ahmed K, Syed JR, et al. Fundamental skills of robotic surgery: a multi-institutional randomized controlled trial for validation of a simulation-based curriculum. Urology. 2013;81:767-74.

11. Satava RM, Stefanidis D, Levy JS, et al. Proving the effectiveness of the fundamentals of robotic surgery (FRS) skills curriculum: a single-blinded, multispecialty, multi-institutional randomized control trial. Ann Surg. 2020;272:384-92.

12. Ayoub-Charette S, McGlynn ND, Lee D, et al. Rationale, design and participants baseline characteristics of a crossover randomized controlled trial of the effect of replacing SSBs with NSBs versus water on glucose tolerance, gut microbiome and cardiometabolic risk in overweight or obese adult SSB consumer: strategies to oppose SUGARS with non-nutritive sweeteners or water (STOP sugars NOW) trial and ectopic fat sub-study. Nutrients. 2023;15:1238.

13. Lazar A, Sroka G, Laufer S. Automatic assessment of performance in the FLS trainer using computer vision. Surg Endosc. 2023;37:6476-82.

14. Islam G, Kahol K, Li B, Smith M, Patel VL. Affordable, web-based surgical skill training and evaluation tool. J Biomed Inform. 2016;59:102-14.

15. Hung AJ, Bao R, Sunmola IO, Huang DA, Nguyen JH, Anandkumar A. Capturing fine-grained details for video-based automation of suturing skills assessment. Int J Comput Assist Radiol Surg. 2023;18:545-52.

16. Ma R, Kiyasseh D, Laca JA, et al. Artificial intelligence-based video feedback to improve novice performance on robotic suturing skills: a pilot study. J Endourol. 2024;38:884-91.

17. Raza SJ, Field E, Jay C, et al. Surgical competency for urethrovesical anastomosis during robot-assisted radical prostatectomy: development and validation of the robotic anastomosis competency evaluation. Urology. 2015;85:27-32.

18. Otiato MX, Ma R, Chu TN, Wong EY, Wagner C, Hung AJ. Surgical gestures to evaluate apical dissection of robot-assisted radical prostatectomy. J Robot Surg. 2024;18:245.

Artificial Intelligence Surgery
ISSN 2771-0408 (Online)
Follow Us

Portico

All published articles will be preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles will be preserved here permanently:

https://www.portico.org/publishers/oae/