Application of Computer Aided Surgery in Gastrointestinal Surgery
Author:
Affiliation:
1. Department of Colorectal Surgery, National Cancer Center Hospital East
2. Surgical Device Innovation Office, NEXT Medical Device Innovation Center, National Cancer Center Hospital East
Publisher
The Japan Society of Computer Aided Surgery
Link
https://www.jstage.jst.go.jp/article/jscas/23/3/23_104/_pdf
Reference5 articles.
1. 1) Wibe A, Eriksen MT, Syse A, et al. Effect of hospital caseload on long-term outcome after standardization of rectal cancer surgery at a national level. Br J Surg 2005; 92 (2) : 217-224.
2. 2) Renzulli P, Lowy A, Maibach R, et al. The influence of the surgeon's and the hospital's caseload on survival and local recurrence after colorectal cancer surgery. Surgery 2006; 139 (3) : 296-304.
3. 3) 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 (11) : 4924-4931.
4. 4) Kitaguchi D, Takeshita N, Matsuzaki H, et al. Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: Experimental research. Int J Surg. 2020; 79: 88-94.
5. 5) Jayne D, Pigazzi A, Marshall H, et al. Effect of Robotic-Assisted vs Conventional Laparoscopic Surgery on Risk of Conversion to Open Laparotomy Among Patients Undergoing Resection for Rectal Cancer: The ROLARR Randomized Clinical Trial. JAMA 2017; 318 (16) : 1569-1580.
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