Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Medical image analysis Pub Date : 2024-10-24 DOI:10.1016/j.media.2024.103376
Lanzhuju Mei , Ke Deng , Zhiming Cui , Yu Fang , Yuan Li , Hongchang Lai , Maurizio S. Tonetti , Dinggang Shen
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Abstract

Accurate classification of periodontal disease through panoramic X-ray images carries immense clinical importance for effective diagnosis and treatment. Recent methodologies attempt to classify periodontal diseases from X-ray images by estimating bone loss within these images, supervised by manual radiographic annotations for segmentation or keypoint detection. However, these annotations often lack consistency with the clinical gold standard of probing measurements, potentially causing measurement inaccuracy and leading to unstable classifications. Additionally, the diagnosis of periodontal disease necessitates exceptional sensitivity. To address these challenges, we introduce HC-Net, an innovative hybrid classification framework devised for accurately classifying periodontal disease from X-ray images. This framework comprises three main components: tooth-level classification, patient-level classification, and a learnable adaptive noisy-OR gate. In the tooth-level classification, we initially employ instance segmentation to individually identify each tooth, followed by tooth-level periodontal disease classification. For patient-level classification, we utilize a multi-task strategy to concurrently learn patient-level classification and a Classification Activation Map (CAM) that signifies the confidence of local lesion areas within the panoramic X-ray image. Eventually, our adaptive noisy-OR gate acquires a hybrid classification by amalgamating predictions from both levels. In particular, we incorporate clinical knowledge into the workflows used by professional dentists, targeting the enhanced handling of sensitivity of periodontal disease diagnosis. Extensive empirical testing on a dataset amassed from real-world clinics demonstrates that our proposed HC-Net achieves unparalleled performance in periodontal disease classification, exhibiting substantial potential for practical application.
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临床知识指导下的混合分类网络用于 X 光图像中牙周病的自动诊断
通过全景 X 光图像对牙周疾病进行准确分类,对有效诊断和治疗具有极大的临床意义。最近的方法试图通过估算 X 光图像中的骨质流失来对牙周疾病进行分类,并通过人工放射注释进行分割或关键点检测。然而,这些注释往往与探查测量的临床黄金标准缺乏一致性,有可能造成测量不准确,导致分类不稳定。此外,牙周病的诊断还需要极高的灵敏度。为了应对这些挑战,我们引入了 HC-Net,这是一个创新的混合分类框架,用于从 X 光图像中准确地对牙周病进行分类。该框架由三个主要部分组成:牙齿级分类、患者级分类和可学习的自适应噪声-OR 门。在牙齿级分类中,我们首先使用实例分割来单独识别每颗牙齿,然后进行牙齿级牙周病分类。在患者级分类中,我们采用多任务策略,同时学习患者级分类和分类激活图(CAM),该图表示全景 X 光图像中局部病变区域的可信度。最终,我们的自适应噪声-OR 门通过综合两个层面的预测结果,获得混合分类。特别是,我们将临床知识融入到专业牙医使用的工作流程中,以提高牙周疾病诊断的敏感度为目标。在真实诊所收集的数据集上进行的广泛实证测试表明,我们提出的 HC-Net 在牙周病分类方面取得了无与伦比的性能,在实际应用中展现出巨大的潜力。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
期刊最新文献
Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]. Editorial for Special Issue on Foundation Models for Medical Image Analysis. Few-shot medical image segmentation with high-fidelity prototypes. The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction. AutoFOX: An automated cross-modal 3D fusion framework of coronary X-ray angiography and OCT.
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