Class Activation Map
The heat map of CAM image was generated using the otoscopic images from
the validation set. The CAM showed the deep learning model can identify
attic retraction pocket with red color accurately, and deeper or bigger
attic retraction pocket hold more values (Fig 3).
Partial atelectasis and general
atelectasis were identified by the deep learning model, and deeper or
bigger atelectasis showed more values with red color (Fig 4).
DISCUSSION
The diagnosis of attic retraction pocket and atelectasis is based on an
otoscopic examination. However, diagnosis of ear diseases with otoscopic
images is a hard task for general practitioners, pediatricians and
otolaryngologists, with averaged accuracy 39%-53%, 36%-51% and
61%-74% respectively 20-22. In this study, we
developed and validated a deep learning model to identify attic
retraction pocket and atelectasis with multi-centers otoscopic images.
Our CNN algorithm acquired an AUC of 0.89 for the identification of
attic retraction pocket and 0.87 for atelectasis.
Previous studies established deep learning models for the diagnosis of
tympanic retraction and achieved an averaged accuracy ranging from
85.78% to 88.06% 15,16. Shie et al15 only obtained 856 otoscopic images from one center
encompassing almost all otitis media categories. Cha et al16 included 1222 otoscopic images with tympanic
retraction and they merged atelectasis and attic retraction pocket into
a class. Our study included 6393 OME otoscopic images, of which 55.74%
were identified with attic retraction pocket and 38.48% were identified
with atelectasis. Considering that the
attic retraction pocket was limited
to the pars flaccida of tympanic membrane, the atelectasis is in the
pars tensa of tympanic membrane. During the progress of disease, attic
retraction pocket is more likely to progress to cholesteatoma, and
atelectasis is likely to evolve to ossicular erosions10. Therefore, we labeled and divided the retraction
pockets into atelectasis and attic retraction pocket. Compared with
previous models, we targeted the attic retraction pocket and atelectasis
separately, to our current knowledge, this image classification system
was the first to diagnose two types of tympanic membrane lesions.
Our results showed different region (pars tensa and pars flaccida)
retraction on the tympanic membranes with different
accuracy. It is reasonable for
clinical experience that attic retraction pocket is easier to identify
than atelectasis, because normal tympanic membrane shows a mild
retraction in pars tensa without retraction in pars flaccida. The reason
may be that in non-severe cases, the attic retraction pocket may be
subtle, and clinicians may find it difficult to determine whether this
is a normal or a grade I attic retraction pocket based on Tos and Sade
classification systems 17,18. On the other hand, cases
with severe attic retraction pockets and atelectasis often exposed the
ossicles inside the tympanic membrane, and sometimes it is difficult to
distinguish between perforation and severe atelectasis.
In order to show the discriminative region of deep learning, CAM
highlighted the important area with red color 17,
especially large and deep retraction pockets and atelectasis, which was
consistent with otologists. Moreover, our image datasets were
representative which were collected from three hospitals with different
type of otoscopes and image record systems. Many parameters of otoscopes
and systems differs in different hospitals, such as the white balance
was not equal in different hospital, even in the same hospital because
of different preference of practitioners.
During the procedure of follow up, if attic retraction pocket and
atelectasis is suggested, observation should be stop and it’s better to
triage the patients to otologists. On the other hand, in the clinical
practice, this model could be useful for generating diagnosis of attic
retraction pocket and atelectasis, which could be assistant for
otologists. For young otologists and non-otologists, this model could be
used as a study platform to learn
attic
retraction pocket and atelectasis.
Limitation : Some limitations did exist in our study. Although
this CNN algorithm could identify mild and severe attic retraction
pocket and atelectasis. However, without enough images of severe attic
retraction pocket and atelectasis, it is not easy to develop and
validate a deep learning model to identify different level of attic
retraction pocket and atelectasis. Moreover, accurate segment labeling
techniques may be helpful for improving the accuracy of model. We
developed the deep learning model with weak supervision, and further
detailed annotation before model development are suggested. Thirdly,
non-medical history and hearing information were provided to the deep
learning model and otolaryngologists, which may affect the accuracy of
diagnosis. The doctor can be greatly improved the accuracy of diagnosis
by adding disease history.