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.

翻译：准确检测骨开窗与骨开裂（FD）对于牙科治疗规划至关重要。虽然锥形束计算机断层扫描（CBCT）是评估FD的金标准，但与口腔内图像相比，其存在辐射暴露、可及性有限及成本较高等局限性。在口腔内图像中，牙医面临FD鉴别诊断的挑战。本文提出了一种仅从口腔内图像进行FD检测的新颖且具有临床意义的应用。为此，我们提出了FD-SOS，一种用于口腔内图像FD检测的新型开放集目标检测器。FD-SOS包含两个新颖组件：条件对比去噪（CCDN）和牙齿特异性匹配分配（TMA）。这些模块使FD-SOS能够有效利用外部牙科语义。实验结果表明，我们的方法优于现有检测方法，并在相同精度水平下将召回率比牙科专业人员提高了35%。代码发布于：https://github.com/xmed-lab/FD-SOS。