  Vascular anomalies, more colloquially known as birthmarks, affect up to 1 in
10 infants. Though many of these lesions self-resolve, some types can result in
medical complications or disfigurement without proper diagnosis or management.
Accurately diagnosing vascular anomalies is challenging for pediatricians and
primary care physicians due to subtle visual differences and similarity to
other pediatric dermatologic conditions. This can result in delayed or
incorrect referrals for treatment. To address this problem, we developed a
convolutional neural network (CNN) to automatically classify images of vascular
anomalies and other pediatric skin conditions to aid physicians with diagnosis.
We constructed a dataset of 21,681 clinical images, including data collected
between 2002-2018 at Seattle Children's hospital as well as five
dermatologist-curated online repositories, and built a taxonomy over vascular
anomalies and other common pediatric skin lesions. The CNN achieved an average
AUC of 0.9731 when ten-fold cross-validation was performed across a taxonomy of
12 classes. The classifier's average AUC and weighted F1 score was 0.9889 and
0.9732 respectively when evaluated on a previously unseen test set of six of
these classes. Further, when used as an aid by pediatricians (n = 7), the
classifier increased their average visual diagnostic accuracy from 73.10% to
91.67%. The classifier runs in real-time on a smartphone and has the potential
to improve diagnosis of these conditions, particularly in resource-limited
areas.
