1. Introduction
Chronic rhinosinusitis (CRS), which is characterized by inflammatory
dysregulation of the nasal and parasinus mucosa for ≥12 consecutive
weeks, is among the most common otolaryngological diseases, with a
prevalence rate of 8% in the general population [1]. Currently, CRS
is classified into two types based on the presence or absence of nasal
polyps; further, it can be classified as eosinophilic sinusitis (ECRS)
and non-eosinophilic sinusitis (NECRS) based on the eosinophilic
infiltration level in the nasal mucosa or polyp. There has been recent
progress in the elucidation of the pathogenesis of CRS. There are
several limitations in the traditional phenotypic classification of CRS
into CRSwNP and CRSsNP. First, this classification does not account for
pathophysiological differences; moreover, immune system cells are
crucially involved in inflammatory mechanisms [2-4]. Many patients
with extensive tissue eosinophil infiltration do not present polyp-like
degeneration, with the incidence rate being approximately 27.5%
[5]. Further, even patients with polypoid changes may predominantly
present non-eosinophilic inflammation [6]. Classification of CRS
into ECRS and NECRS is more likely to reflect the underlying
inflammatory process. Additionally, the level of eosinophil infiltration
in CRS lesions is strongly associated with postoperative outcomes [7,
8].
ECRS has a recurrence rate as high as 98.5%; additionally, it is the
main cause of refractory sinusitis recurrence [9]. The gold standard
for ECSR diagnosis is histopathological examination; however, it is
invasive. Recently, the main treatments for ECSR and NECRS are contoured
endoscopic nasal surgery and functional endoscopic nasal surgery,
respectively [10, 11]. Given the high postoperative recurrence rate
of ECRS and differences in the surgical methods for both CRS types,
accurate preoperative prediction of the CRS type is necessary for
predicting postoperative outcomes and administering personalized
treatment. Eosinophil levels in the peripheral blood are associated with
the eosinophil infiltration degree in the nasal sinus mucosa
[12]. However, allergies, autoimmune diseases, drug reactions,
parasitic infections, and corticosteroid treatment can alter circulating
eosinophil levels. Additionally, increased eosinophil levels in
peripheral blood do not necessarily reflect an increase in tissue
eosinophils; moreover, the predictive utility of blood eosinophil levels
for ECRS remains limited [13, 14]. There has been extensive research
on the preoperative predictive utility of exhaled nitric oxide levels,
serum total immunoglobulin E (IgE), specific IgE, and skin prick tests
for ECRS and NECRS; however, they have low sensitivity and specificity
[15, 16]. There are several differences in sinus computed tomography
(CT) findings between patients with ECRS and NECRS. Early-stage ECRS
often presents as ethmoid sinus lesions and mild maxillary sinus
lesions, while NECRS often presents with maxillary sinus lesions.
However, these characteristics do not effectively distinguish ECRS from
NECRS [8, 17].
Considering the strong feature extraction and screening ability of
artificial intelligence, applying artificial intelligence technology to
sinus CT-assisted ECRS diagnosis may allow accurate preoperative
prediction of ECRS. CRS lesions are limited to the nasal cavity and
paranasal sinus areas in each disease type; moreover, the sinus area
comprises a small part of the sinus CT images, with the surrounding
tissue structure being complex. Therefore, noise information in the
whole image increases the required training data. Manually delimiting
the regions of interest (ROIs) is inconsistent with the original
intention of artificial intelligence; further, its huge sketching
workload greatly reduces its clinical application value. Therefore, we
aimed to develop a segmentation model that allowed automatic
segmentation of the sinus region. Moreover, since different networks
have certain data ”preferences,” we used four common classification
networks to train the segmented images.