Three-dimensional simulation of the pancreatic parenchyma, pancreatic duct and vascular arrangement in pancreatic surgery using a deep learning algorithm.

Three-dimensional surgical simulation, already in use for hepatic surgery, can be used in pancreatic surgery. However, some problems still need to be overcome to achieve more precise pancreatic surgical simulation. The present study evaluates the performance of SYNAPSE VINCENT® (version 6.6, Fujifilm Medical Co., Ltd., Tokyo, Japan) in the semiautomated surgical simulation of the pancreatic parenchyma, pancreatic ducts, and peripancreatic vessels using an artificial intelligence (AI) engine designed with deep learning algorithms. One-hundred pancreatic cancer patients and a control group of 100 nonpancreatic cancer patients were enrolled. The evaluation methods for visualizing the extraction were compared using the Dice coefficient (DC). In the pancreatic cancer patients, tumor size, position, and stagewise correlations with the pancreatic parenchymal DC were analyzed. The relationship between the pancreatic duct diameter and the DC, and between the manually and AI-measured diameters of the pancreatic duct were analyzed. In the pancreatic cancer/control groups, the pancreatic parenchymal DC and pancreatic duct extraction were 0.83/0.86 and 0.84/0.77. The DC of the arteries (portal veins/veins) and associated sensitivity and specificity were 0.89/0.88 (0.89/0.88), 0.85/0.83 (0.85/0.82), and 0.82/0.81 (0.84/0.81), respectively. No correlations were observed between pancreatic parenchymal DC and tumor size, position, or stage. No correlation was observed between the pancreatic duct diameter and the DC. A positive correlation (r = 0.61, p<0.001) was observed between the manually and AI-measured diameters of the pancreatic duct. Extraction of the pancreatic parenchyma, pancreatic duct, and surrounding vessels with the SYNAPSE VINCENT® AI engine assumed to be useful as surgical simulation.

Reward sensitivity differs depending on global self-esteem in value-based decision-making.

Global self-esteem is a component of individual personality that impacts decision-making. Many studies have discussed the different preferences for decision-making in response to threats to a person's self-confidence, depending on global self-esteem. However, studies about global self-esteem and non-social decision-making have indicated that decisions differ due to reward sensitivity. Here, reward sensitivity refers to the extent to which rewards change decisions. We hypothesized that individuals with lower global self-esteem have lower reward sensitivity and investigated the relationship between self-esteem and reward sensitivity using a computational model. We first examined the effect of expected value and maximum value in learning under uncertainties because some studies have shown the possibility of saliency (e.g. maximum value) and relative value (e.g. expected value) affecting decisions, respectively. In our learning task, expected value affected decisions, but there was no significant effect of maximum value. Therefore, we modelled participants' choices under the condition of different expected value without considering maximum value. We used the Q-learning model, which is one of the traditional computational models in explaining experiential learning decisions. Global self-esteem correlated positively with reward sensitivity. Our results suggest that individual reward sensitivity affects decision-making depending on one's global self-esteem.