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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6766159PMC
http://dx.doi.org/10.1155/2019/1903297DOI Listing

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Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery.

World J Gastrointest Surg

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Department of Minimally Invasive Digestive Surgery, Antoine-Béclère Hospital, Assistance Publique-Hôpitaux de ParisClamart 92140, Haute-Seine, France.

Anastomotic leakage (AL) is a significant complication following rectal cancer surgery, adversely affecting both quality of life and oncological outcomes. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for predicting and preventing AL. These technologies can analyze extensive clinical datasets to identify preoperative and perioperative risk factors such as malnutrition, body composition, and radiological features.

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Background: Liver malignancies present substantial challenges to surgeons due to the extensive hepatic resections required, frequently resulting in posthepatectomy liver failure. Associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) was designed to increase the resectable liver volume, yet it is associated with significant mortality and morbidity rates. Recently, minimally invasive techniques have been incorporated into ALPPS, with the potential to improve the procedure's safety profile whilst maintaining efficacy.

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Significance: Laparoscopic surgery presents challenges in localizing oncological margins due to poor contrast between healthy and malignant tissues. Optical properties can uniquely identify various tissue types and disease states with high sensitivity and specificity, making it a promising tool for surgical guidance. Although spatial frequency domain imaging (SFDI) effectively measures quantitative optical properties, its deployment in laparoscopy is challenging due to the constrained imaging environment.

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Background: Transabdominal pre-peritoneal inguinal hernia repair using the da Vinci Single-Port robot (SP-TAPP) is currently performed in few centers. We aimed to define the learning curve for SP-TAPP by analyzing operative times.

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Laparoscopic distal pancreatectomy is a minimally invasive approach for the surgical treatment of neoplasms in the distal pancreas. This study aimed to compare this approach to the open procedure. A retrospective analysis of a prospectively maintained database of 400 pancreatectomies was performed.

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