  Confocal laser endomicroscopy (CLE), although capable of obtaining images at
cellular resolution during surgery of brain tumors in real time, creates as
many non-diagnostic as diagnostic images. Non-useful images are often distorted
due to relative motion between probe and brain or blood artifacts. Many images,
however, simply lack diagnostic features immediately informative to the
physician. Examining all the hundreds or thousands of images from a single case
to discriminate diagnostic images from nondiagnostic ones can be tedious.
Providing a real-time diagnostic value assessment of images (fast enough to be
used during the surgical acquisition process and accurate enough for the
pathologist to rely on) to automatically detect diagnostic frames would
streamline the analysis of images and filter useful images for the
pathologist/surgeon. We sought to automatically classify images as diagnostic
or non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-fold
cross validation manner. Our dataset includes 16,795 images (8572 nondiagnostic
and 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The ground
truth for all the images is provided by the pathologist. Average model accuracy
on test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 %
specificity). To evaluate the model reliability we also performed receiver
operating characteristic (ROC) analysis yielding 0.958 average for the area
under ROC curve (AUC). These results demonstrate that a deeply trained AlexNet
network can achieve a model that reliably and quickly recognizes diagnostic CLE
images.
