  Deep learning has remarkably advanced in the last few years, supported by
large labeled data sets. These data sets are precious yet scarce because of the
time-consuming labeling procedures, discouraging researchers from producing
them. This scarcity is especially true in dentistry, where deep learning
applications are still in an embryonic stage. Motivated by this background, we
address in this study the construction of a public data set of dental panoramic
radiographs. Our objects of interest are the teeth, which are segmented and
numbered, as they are the primary targets for dentists when screening a
panoramic radiograph. We benefited from the human-in-the-loop (HITL) concept to
expedite the labeling procedure, using predictions from deep neural networks as
provisional labels, later verified by human annotators. All the gathering and
labeling procedures of this novel data set is thoroughly analyzed. The results
were consistent and behaved as expected: At each HITL iteration, the model
predictions improved. Our results demonstrated a 51% labeling time reduction
using HITL, saving us more than 390 continuous working hours. In a novel online
platform, called OdontoAI, created to work as task central for this novel data
set, we released 4,000 images, from which 2,000 have their labels publicly
available for model fitting. The labels of the other 2,000 images are private
and used for model evaluation considering instance and semantic segmentation
and numbering. To the best of our knowledge, this is the largest-scale publicly
available data set for panoramic radiographs, and the OdontoAI is the first
platform of its kind in dentistry.
