REFERENCES
1. Gumbs AA, Alexander F, Karcz K, et al. White paper: definitions of artificial intelligence and autonomous actions in clinical surgery. Art Int Surg 2022;2:93-100.
2. Mise Y, Hasegawa K, Satou S, et al. How has virtual hepatectomy changed the practice of liver surgery? Ann Surg 2018;268:127-33.
3. Kazami Y, Kaneko J, Keshwani D, et al. Artificial intelligence enhances the accuracy of portal and hepatic vein extraction in computed tomography for virtual hepatectomy. J Hepatobiliary Pancreat Sci 2022;29:359-68.
4. Takamoto T, Ban D, Nara S, et al. Automated three-dimensional liver reconstruction with artificial intelligence for virtual hepatectomy. J Gastrointest Surg 2022;26:2119-27.
5. Chen WF, Ou HY, Lin HY, et al. Development of novel residual-dense-attention (RDA) U-net network architecture for hepatocellular carcinoma segmentation. Diagnostics 2022;12:1916.
6. Koitka S, Gudlin P, Theysohn JM, et al. Fully automated preoperative liver volumetry incorporating the anatomical location of the central hepatic vein. Sci Rep 2022;12:16479.
7. Mojtahed A, Núñez L, Connell J, et al. Repeatability and reproducibility of deep-learning-based liver volume and Couinaud segment volume measurement tool. Abdom Radiol 2022;47:143-51.
8. Lyu F, Ma AJ, Yip TC, Wong GL, Yuen PC. Weakly supervised liver tumor segmentation using Couinaud segment annotation. IEEE Trans Med Imaging 2022;41:1138-49.
9. Tanzi L, Piazzolla P, Porpiglia F, Vezzetti E. Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance. Int J Comput Assist Radiol Surg 2021;16:1435-45.
10. Lin J, Clancy NT, Qi J, et al. Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks. Med Image Anal 2018;48:162-76.
11. Luo H, Yin D, Zhang S, et al. Augmented reality navigation for liver resection with a stereoscopic laparoscope. Comput Methods Programs Biomed 2020;187:105099.
12. Bertrand LR, Abdallah M, Espinel Y, et al. A case series study of augmented reality in laparoscopic liver resection with a deformable preoperative model. Surg Endosc 2020;34:5642-8.
13. Phutane P, Buc E, Poirot K, et al. Preliminary trial of augmented reality performed on a laparoscopic left hepatectomy. Surg Endosc 2018;32:514-5.
14. Barash Y, Klang E, Lux A, et al. Artificial intelligence for identification of focal lesions in intraoperative liver ultrasonography. Langenbecks Arch Surg 2022;407:3553-60.
15. Mai RY, Zeng J, Meng WD, et al. Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion. BMC Cancer 2021;21:283.
16. Mai RY, Lu HZ, Bai T, et al. Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma. Surgery 2020;168:643-52.
17. Merath K, Hyer JM, Mehta R, et al. Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery. J Gastrointest Surg 2020;24:1843-51.
18. Chu T, Zhao C, Zhang J, et al. Application of a convolutional neural network for multitask learning to simultaneously predict microvascular invasion and vessels that encapsulate tumor clusters in hepatocellular carcinoma. Ann Surg Oncol 2022;29:6774-83.
19. Li X, Qi Z, Du H, et al. Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs. Eur Radiol 2022;32:771-82.
20. Ishizawa T, Saiura A. Fluorescence imaging for minimally invasive cancer surgery. Surg Oncol Clin N Am 2019;28:45-60.
21. Zhang C, Wang K, Tian J. Adaptive brightness fusion method for intraoperative near-infrared fluorescence and visible images. Biomed Opt Express 2022;13:1243-60.
22. Bari H, Wadhwani S, Dasari BVM. Role of artificial intelligence in hepatobiliary and pancreatic surgery. World J Gastrointest Surg 2021;13:7-18.
23. Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: challenges and opportunities. World J Gastrointest Oncol 2022;14:765-93.
24. Saito Y, Shimada M, Morine Y, Yamada S, Sugimoto M. Essential updates 2020/2021: current topics of simulation and navigation in hepatectomy. Ann Gastroenterol Surg 2022;6:190-6.
25. Meiabadi MS, Moradi M, Karamimoghadam M, et al. Modeling the producibility of 3D printing in polylactic acid using artificial neural networks and fused filament fabrication. Polymers 2021;13:3219.
26. Rojek I, Mikołajewski D, Kopowski J, Kotlarz P, Piechowiak M, Dostatni E. Reducing waste in 3D printing using a neural network based on an own elbow exoskeleton. Materials 2021;14:5074.
27. Pugliese R, Regondi S. Artificial intelligence-empowered 3D and 4D printing technologies toward smarter biomedical materials and approaches. Polymers 2022;14:2794.
28. Giannone F, Felli E, Cherkaoui Z, Mascagni P, Pessaux P. Augmented reality and image-guided robotic liver surgery. Cancers 2021;13:6268.
29. Adballah M, Espinel Y, Calvet L, et al. Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours. Surg Endosc 2022;36:833-43.
30. Landsman ML, Kwant G, Mook GA, Zijlstra WG. Light-absorbing properties, stability, and spectral stabilization of indocyanine green. J Appl Physiol 1976;40:575-83.
31. Ishizawa T, Tamura S, Masuda K, et al. Intraoperative fluorescent cholangiography using indocyanine green: a biliary road map for safe surgery. J Am Coll Surg 2009;208:e1-4.
32. Ishizawa T, Bandai Y, Kokudo N. Fluorescent cholangiography using indocyanine green for laparoscopic cholecystectomy: an initial experience. Arch Surg 2009;144:381-2.
33. Ishizawa T, Bandai Y, Ijichi M, Kaneko J, Hasegawa K, Kokudo N. Fluorescent cholangiography illuminating the biliary tree during laparoscopic cholecystectomy. Br J Surg 2010;97:1369-77.
34. Kono Y, Ishizawa T, Tani K, et al. Techniques of fluorescence cholangiography during laparoscopic cholecystectomy for better delineation of the bile duct anatomy. Medicine 2015;94:e1005.
35. Terasawa M, Ishizawa T, Mise Y, et al. Applications of fusion-fluorescence imaging using indocyanine green in laparoscopic hepatectomy. Surg Endosc 2017;31:5111-8.
36. Liu Y, Dong L, Ji Y, Xu W. Infrared and visible image fusion through details preservation. Sensors 2019;19:4556.
37. Shen B, Zhang Z, Shi X, et al. Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks. Eur J Nucl Med Mol Imaging 2021;48:3482-92.
38. Young K, Ma E, Kejriwal S, Nielsen T, Aulakh SS, Birkeland AC. Intraoperative
39. Ochoa M, Rudkouskaya A, Yao R, Yan P, Barroso M, Intes X. High compression deep learning based single-pixel hyperspectral macroscopic fluorescence lifetime imaging in vivo. Biomed Opt Express 2020;11:5401-24.
40. Marsden M, Fukazawa T, Deng YC, et al. FLImBrush: dynamic visualization of intraoperative free-hand fiber-based fluorescence lifetime imaging. Biomed Opt Express 2020;11:5166-80.
