A Novel Predictive Model for Anastomotic Leakage in Colorectal Cancer Using Auto-artificial Intelligence.

Journal: Anticancer research

Published Date: Nov 1, 2021

Abstract

AIM: Anastomotic leakage (AL) in left-sided colorectal cancer is a serious complication, with an incidence rate of 6-18%. We developed a novel predictive model for AL in colorectal surgery with double-stapling technique (DST) anastomosis using auto-artificial intelligence (AI).

Authors

Junichi Mazaki
Kenji Katsumata
Yuki Ohno

Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan.
Ryutaro Udo

Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan.
Tomoya Tago

Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan.
Kenta Kasahara

Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan.
Hiroshi Kuwabara

Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku, Tokyo 160-0023, Japan. hiroshi.kuwabara.bob@gmail.com.
Masanobu Enomoto
Tetsuo Ishizaki
Yuichi Nagakawa
Akihiko Tsuchida

Keywords

Aged Anastomotic Leak Colectomy Colorectal Neoplasms Databases, Factual Decision Support Techniques Female Humans Machine Learning Male Predictive Value of Tests Retrospective Studies Risk Assessment Risk Factors Surgical Staplers Surgical Stapling Time Factors Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (34732457)

A Novel Predictive Model for Anastomotic Leakage in Colorectal Cancer Using Auto-artificial Intelligence.

Abstract

Authors

Keywords

External Resources

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