A deep learning approach to diagnose atelectasis and attic retraction
pocket with otoscopic images
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. Key words: deep learning, otoscopic images,
atelectasis, attic retraction pocket