  Accurate detection of bone fenestration and dehiscence (FD) is crucial for
effective treatment planning in dentistry. While cone-beam computed tomography
(CBCT) is the gold standard for evaluating FD, it comes with limitations such
as radiation exposure, limited accessibility, and higher cost compared to
intraoral images. In intraoral images, dentists face challenges in the
differential diagnosis of FD. This paper presents a novel and clinically
significant application of FD detection solely from intraoral images. To
achieve this, we propose FD-SOS, a novel open-set object detector for FD
detection from intraoral images. FD-SOS has two novel components: conditional
contrastive denoising (CCDN) and teeth-specific matching assignment (TMA).
These modules enable FD-SOS to effectively leverage external dental semantics.
Experimental results showed that our method outperformed existing detection
methods and surpassed dental professionals by 35% recall under the same level
of precision. Code is available at: https://github.com/xmed-lab/FD-SOS.
