  The cone-beam computed tomography (CBCT) provides 3D volumetric imaging of a
target with low radiation dose and cost compared with conventional computed
tomography, and it is widely used in the detection of paranasal sinus disease.
However, it lacks the sensitivity to detect soft tissue lesions owing to
reconstruction constraints. Consequently, only physicians with expertise in
CBCT reading can distinguish between inherent artifacts or noise and diseases,
restricting the use of this imaging modality. The development of artificial
intelligence (AI)-based computer-aided diagnosis methods for CBCT to overcome
the shortage of experienced physicians has attracted substantial attention.
However, advanced AI-based diagnosis addressing intrinsic noise in CBCT has not
been devised, discouraging the practical use of AI solutions for CBCT. To
address this issue, we propose an AI-based computer-aided diagnosis method
using CBCT with a denoising module. This module is implemented before diagnosis
to reconstruct the internal ground-truth full-dose scan corresponding to an
input CBCT image and thereby improve the diagnostic performance. The external
validation results for the unified diagnosis of sinus fungal ball, chronic
rhinosinusitis, and normal cases show that the proposed method improves the
micro-, macro-average AUC, and accuracy by 7.4, 5.6, and 9.6% (from 86.2, 87.0,
and 73.4 to 93.6, 92.6, and 83.0%), respectively, compared with a baseline
while improving human diagnosis accuracy by 11% (from 71.7 to 83.0%),
demonstrating technical differentiation and clinical effectiveness. This
pioneering study on AI-based diagnosis using CBCT indicates denoising can
improve diagnostic performance and reader interpretability in images from the
sinonasal area, thereby providing a new approach and direction to radiographic
image reconstruction regarding the development of AI-based diagnostic
solutions.
