Does neural network pattern recognition analysis of graft flow characteristics improve intra-operative anastomotic error detection in minimally invasive CABG?
Pattern recognition of transit-time flow probe tracings using neural network systems can detect anastomotic errors significantly better than the surgeon's visual assessment, thereby improving the clinical outcome of minimally invasive CABG.
This study suggests that liver resections in two stages are not associated with more post-operative complications than one-stage resections. These results should support the adoption of two-stage liver resections in selected patients.