Deep-learning based fusion of spatial relationship classification between mandibular third molar and inferior alveolar nerve using panoramic radiograph images

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biomedical Signal Processing and Control Pub Date : 2024-10-29 DOI:10.1016/j.bspc.2024.107059
Nida Kumbasar , Mustafa Taha Güller , Özkan Miloğlu , Emin Argun Oral , Ibrahim Yucel Ozbek
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Abstract

It is crucial for clinicians to have a prior knowledge of spatial relationship between impacted mandibular third molar tooth (MM3) and inferior alveolar nerve (IAN) before an extraction procedure. This relationship may exist in four spatial forms in terms of IAN position relative to MM3 although it has not been studied extensively. To identify such relationship type, on the other hand, this study proposes a novel four-class classification framework utilizing fusion of AlexNet, VGG16, VGG19 deep learning methods using panoramic radiograph (PR) images. For this purpose, 546 PR images of impacted MM3, collected from 290 patients, were labeled by specialists using corresponding cone beam computed tomography (CBCT) images. The proposed network is trained and tested using 10 folds cross validation. Experimental studies were performed in different categories. In the first (MM3 and IAN are related/unrelated) an accuracy rate of 94.1% was obtained. In the following IAN resides on the lingual or vestibule (buccal) side of MM3 classification problem, test result of 80.6% accuracy was obtained. Finally, in the challenging four-class classification problem that includes unrelated, lingual, vestibule and other classes, an accuracy rate of 79.7% was achieved. Obtained results show that the proposed method not only presents state-of-the-art results but also suggests a new classification basis for the existing MM3-IAN relationship problem.
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基于深度学习的下颌第三磨牙与下牙槽神经空间关系分类融合(使用全景放射摄影图像
对于临床医生来说,在拔牙手术前事先了解下颌第三磨牙(MM3)和下牙槽神经(IAN)之间的空间关系至关重要。这种关系可能存在于四种空间形式中,即 IAN 相对于 MM3 的位置,尽管尚未进行广泛研究。另一方面,为了识别这种关系类型,本研究提出了一种新颖的四级分类框架,利用全景X光片(PR)图像融合 AlexNet、VGG16、VGG19 深度学习方法。为此,专家使用相应的锥形束计算机断层扫描(CBCT)图像对从 290 名患者处收集到的 546 幅影响 MM3 的 PR 图像进行了标记。提出的网络通过 10 次交叉验证进行训练和测试。实验研究按不同类别进行。第一类(MM3 和 IAN 相关/不相关)的准确率为 94.1%。在接下来的 IAN 位于 MM3 分类问题的舌侧或前庭(颊侧)时,测试结果的准确率为 80.6%。最后,在具有挑战性的四类分类问题(包括无关类、舌侧类、前庭类和其他类)中,准确率达到了 79.7%。结果表明,所提出的方法不仅呈现了最先进的结果,还为现有的 MM3-IAN 关系问题提出了新的分类基础。
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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