REFERENCES
1. Buia A, Stockhausen F, Hanisch E. Laparoscopic surgery: a qualified systematic review. World J Methodol. 2015;5:238-54.
2. Qian T, Huang K, Chen W, et al. Comparison of outcomes with stapler versus hand-sewn closure of the pancreatic stump following minimally invasive distal pancreatectomy: a retrospective cohort study. J Pancreatol. 2024;7:106-10.
3. Zhou JW, Huang XT, Cai JP, et al. Two-drain versus three-train strategy in robotic-assisted pancreaticoduodenectomy: impact on complications. J Pancreatol. 2025;8:272-8.
4. Serra J, Matias-Gulu X, Calabuig R, Garcia P, Sancho FJ, La Calle JP. Surgical gauze pseudotumor. Am J Surg. 1988;155:235-7.
6. Liu F, Chen D, Wang F, Li Z, Xu F. Deep learning based single sample face recognition: a survey. Artif Intell Rev. 2023;56:2723-48.
7. Grigorescu S, Trasnea B, Cocias T, Macesanu G. A survey of deep learning techniques for autonomous driving. J Field Robot. 2020;37:362-86.
8. Ma J, Wu F, Jiang T, Zhu J, Kong D. Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images. Med Phys. 2017;44:1678-91.
9. Suk HI, Wee CY, Lee SW, Shen D. State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage. 2016;129:292-307.
10. Liu F, Jang H, Kijowski R, Bradshaw T, Mcmillan AB. Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology. 2018;286:676-84.
11. Wang Y, Sun Q, Sun G, Gu L, Liu Z. Object detection of surgical instruments based on Yolov4. In: 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE; 2021. pp. 578-81.
12. Wang B, Zheng J, Yu JF, et al. Development of artificial intelligence for parathyroid recognition during endoscopic thyroid surgery. Laryngoscope. 2022;132:2516-23.
13. Takeuchi M, Collins T, Ndagijimana A, et al. Automatic surgical phase recognition in laparoscopic inguinal hernia repair with artificial intelligence. Hernia. 2022;26:1669-78.
14. Shinozuka K, Turuda S, Fujinaga A, et al. Artificial intelligence software available for medical devices: surgical phase recognition in laparoscopic cholecystectomy. Surg Endosc. 2022;36:7444-52.
15. Cheng K, You J, Wu S, et al. Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis. Surg Endosc. 2022;36:3160-8.
16. Hua S, Wang Z, Wang J, et al. Application value of machine learning algorithms for gauze detection in laparoscopic pancreatic surgery. Chin J Dig Surg. 2021;20:1324-30.
17. de La Fuente López E, Muñoz García Á, Santos Del Blanco L, Fraile Marinero JC, Pérez Turiel J. Automatic gauze tracking in laparoscopic surgery using image texture analysis. Comput Methods Programs Biomed. 2020;190:105378.
18. Lai SL, Chen CS, Lin BR, Chang RF. Intraoperative detection of surgical gauze using deep convolutional neural network. Ann Biomed Eng. 2023;51:352-62.
19. Sánchez-brizuela G, Santos-criado F, Sanz-gobernado D, et al. Gauze detection and segmentation in minimally invasive surgery video using convolutional neural networks. Sensors. 2022;22:5180.
20. Ao X, Leng Y, Gan Y, et al. Gauze detection and segmentation in laparoscopic liver surgery: a multi-center study. Eur J Med Res. 2025;30:905.
21. Mascagni P, Vardazaryan A, Alapatt D, et al. Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg. 2022;275:955-61.
22. Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg. 2022;276:363-9.
