Abstract
Background: Atelectasis and attic retraction pocket are two
common tympanic membranes changes. However, general practitioners,
pediatricians and otolaryngologists showed low diagnostic accuracy for
these ear diseases. Therefore, there is a need to develop a deep
learning model to detect atelectasis and attic retraction pocket
automatically.
Method: 6393 OME otoscopic images from 3 centers were used to
develop and validate a deep learning model to detect atelectasis and
attic retraction pocket. 3-fold random cross validation was adopted to
divided dataset into training set and validation set. A team of
otologists were assigned to diagnose and label. Receiver operating
characteristic (ROC) curve, 3-fold average classification accuracy,
sensitivity and specificity were used to assess the performance of deep
learning model. Class Activation Mapping (CAM) was applied to show the
discriminative region in the otoscopic images.
Result: Among all the otoscopic images, 3564 (55.74%) images
were identified with attic retraction pocket, and 2460 (38.48%) images
were identified with atelectasis. The automatically diagnostic model of
attic retraction pocket and atelectasis achieved 3-fold cross validation
accuracy of 89% and 79%, AUC of 0.89 and 0.87, sensitivity of 0.93 and
0.71, and specificity of 0.62 and 0.84 respectively. Bigger and deeper
atelectasis and attic retraction pocket showed more weight with red
color in the heat map of CAM.
Conclusion: Deep learning algorithm could be used to identify
atelectasis and attic retraction pocket, which could be used as a tool
to assist general practitioners, pediatricians and otolaryngologists.