Page 88 - Read Online
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Page 46                         McGivern et al. Art Int Surg 2023;3:27-47  https://dx.doi.org/10.20517/ais.2022.39

                    hepatocellular carcinoma. J Am Coll Surg 2015;220:28-37.  DOI  PubMed
               78.       Zhang J, Qiao QL, Guo XC, Zhao JX. Application of three-dimensional visualization technique in preoperative planning of
                    progressive hilar cholangiocarcinoma. Am J Transl Res 2018;10:1730-5.  PubMed  PMC
               79.       Okuda Y, Taura K, Seo S, et al. Usefulness of operative planning based on 3-dimensional CT cholangiography for biliary
                    malignancies. Surgery 2015;158:1261-71.  DOI  PubMed
               80.       Okamoto T, Onda S, Yasuda J, Yanaga K, Suzuki N, Hattori A. Navigation surgery using an augmented reality for pancreatectomy.
                    Dig Surg 2015;32:117-23.  DOI  PubMed
               81.       Fortmeier D, Mastmeyer A, Schröder J, Handels H. A virtual reality system for PTCD simulation using direct visuo-haptic rendering
                    of partially segmented image data. IEEE J Biomed Health Inform 2016;20:355-66.  DOI  PubMed
               82.       Fusaglia M, Hess H, Schwalbe M, et al. A clinically applicable laser-based image-guided system for laparoscopic liver procedures.
                    Int J Comput Assist Radiol Surg 2016;11:1499-513.  DOI  PubMed
               83.       Ntourakis D, Memeo R, Soler L, Marescaux J, Mutter D, Pessaux P. Augmented reality guidance for the resection of missing
                    colorectal liver metastases: an initial experience. World J Surg 2016;40:419-26.  DOI  PubMed
               84.       Mastmeyer A, Fortmeier D, Handels H. Evaluation of direct haptic 4D volume rendering of partially segmented data for liver
                    puncture simulation. Sci Rep 2017;7:671.  DOI  PubMed  PMC
               85.       Sauer IM, Queisner M, Tang P, et al. Mixed reality in visceral surgery: development of a suitable workflow and evaluation of
                    intraoperative use-cases. Ann Surg 2017;266:706-12.  DOI  PubMed
               86.       Cai W, Fan Y, Hu H, Xiang N, Fang C, Jia F. Postoperative liver volume was accurately predicted by a medical image three
                    dimensional visualization system in hepatectomy for liver cancer. Surg Oncol 2017;26:188-94.  DOI  PubMed
               87.       Miyamoto R, Oshiro Y, Nakayama K, et al. Three-dimensional simulation of pancreatic surgery showing the size and location of the
                    main pancreatic duct. Surg Today 2017;47:357-64.  DOI  PubMed
               88.       Hu M, Hu H, Cai W, et al. The safety and feasibility of three-dimensional visualization technology assisted right posterior lobe allied
                    with part of V and VIII sectionectomy for right hepatic malignancy therapy. J Laparoendosc Adv Surg Tech A 2018;28:586-94.  DOI
                    PubMed
               89.       Mise Y, Hasegawa K, Satou S, et al. How has virtual hepatectomy changed the practice of liver surgery? Ann Surg 2018;268:127-33.
                    DOI
               90.       Mascagni  P,  Fiorillo  C,  Urade  T,  et  al.  Formalizing  video  documentation  of  the  critical  view  of  safety  in  laparoscopic
                    cholecystectomy: a step towards artificial intelligence assistance to improve surgical safety. Surg Endosc 2020;34:2709-14.  DOI
                    PubMed
               91.       Teatini A, Pelanis E, Aghayan DL, et al. The effect of intraoperative imaging on surgical navigation for laparoscopic liver resection
                    surgery. Sci Rep 2019;9:18687.  DOI  PubMed  PMC
               92.       Ho H, Yu HB, Bartlett A, Hunter P. An in silico pipeline for subject-specific hemodynamics analysis in liver surgery planning.
                    Comput Methods Biomech Biomed Engin 2020;23:138-42.  DOI  PubMed
               93.       Prevost GA, Eigl B, Paolucci I, et al. Efficiency, accuracy and clinical applicability of a new image-guided surgery system in 3D
                    laparoscopic liver surgery. J Gastrointest Surg 2020;24:2251-8.  DOI  PubMed
               94.       Sandal B, Hacioglu Y, Salihoglu Z, Yagiz N. Fuzzy logic preanesthetic risk evaluation of laparoscopic cholecystectomy operations.
                    Am Surg 2023;89:414-23.  DOI  PubMed
               95.       Cervantes-sanchez F, Maktabi M, Köhler H, et al. Automatic tissue segmentation of hyperspectral images in liver and head neck
                    surgeries using machine learning. Art Int Surg 2021;1:22-37.  DOI
               96.       Tokuyasu T, Iwashita Y, Matsunobu Y, et al. Development of an artificial intelligence system using deep learning to indicate
                    anatomical landmarks during laparoscopic cholecystectomy. Surg Endosc 2021;35:1651-8.  DOI  PubMed  PMC
               97.       Guzmán-García C, Gómez-Tome M, Sánchez-González P, Oropesa I, Gómez EJ. Speech-based surgical phase recognition for non-
                    intrusive surgical skills' assessment in educational contexts. Sensors 2021;21:1330.  DOI  PubMed  PMC
               98.       Imler TD, Sherman S, Imperiale TF, et al. Provider-specific quality measurement for ERCP using natural language processing.
                    Gastrointest Endosc 2018;87:164-173.e2.  DOI  PubMed  PMC
               99.       Ruzzenente A, Bagante F, Poletto E, et al. A machine learning analysis of difficulty scoring systems for laparoscopic liver surgery.
                    Surg Endosc 2022;36:8869-80.  DOI  PubMed  PMC
               100.      Mascagni P, Alapatt D, Laracca GG, et al. Multicentric validation of EndoDigest: a computer vision platform for video
                    documentation of the critical view of safety in laparoscopic cholecystectomy. Surg Endosc 2022;36:8379-86.  DOI  PubMed
               101.      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.  DOI  PubMed
               102.      Tranter-entwistle I, Eglinton T, Connor S, Hugh TJ. Operative difficulty in laparoscopic cholecystectomy: considering the role of
                    machine learning platforms in clinical practice. Art Int Surg 2022;2:46-56.  DOI
               103.      Liu R, An J, Wang Z, et al. Artificial intelligence in laparoscopic cholecystectomy: does computer vision outperform human vision?
                    Art Int Surg 2022;2:80-92.  DOI
               104.      Ugail H, Abubakar A, Elmahmudi A, Wilson C, Thomson B. The use of pre-trained deep learning models for the photographic
                    assessment of donor livers for transplantation. Art Int Surg 2022;2:101-19.  DOI
               105.      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.  DOI  PubMed  PMC
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