  Perioperative data are essential to investigating the causes of adverse
surgical outcomes. In some low to middle income countries, these data are
computationally inaccessible due to a lack of digitization of surgical
flowsheets. In this paper, we present a deep image segmentation approach using
a U-Net architecture that can detect hand-drawn symbols on a flowsheet graph.
The segmentation mask outputs are post-processed with techniques unique to each
symbol to convert into numeric values. The U-Net method can detect, at the
appropriate time intervals, the symbols for heart rate and blood pressure with
over 99 percent accuracy. Over 95 percent of the predictions fall within an
absolute error of five when compared to the actual value. The deep learning
model outperformed template matching even with a small size of annotated images
available for the training set.
