Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons.

This study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively reflect laparoscopic surgical skill levels using an artificial intelligence system consisting of a three-layer chaos neural network. Sixty-seven surgeons (23 experts and 44 novices) performed a laparoscopic skill assessment task while their hand motions were recorded using a magnetic tracking sensor. Eight parameters evaluated as measures of skill in a previous study were used as inputs to the neural network.

Analysis of hand motion differentiates expert and novice surgeons.

Background: The number of operations performed by a surgeon may be an indicator of surgical skill. The hand motions made by a surgeon also reflect skill and level of expertise. We hypothesized that the hand motions of expert and novice surgeons differ significantly, regardless of whether they are familiar with specific tasks during an operation.

Nonlinear analyses of heart rate variability in monochorionic and dichorionic twin fetuses.

Aims: To examine whether the heart rate dynamics of a monochorionic diamniotic (MD) twin fetus is different from a dichorionic diamniotic (DD) twin fetus by nonlinear methods of time series analysis.

Methods: 32 fetal heart rate (FHR) recordings from healthy MD twin fetuses and 58 recordings from healthy DD twin fetuses were used. Nonlinear analyses included attractor reconstruction, calculation of the largest Lyapunov exponents, and estimation of correlation dimension.