Cervical vertebral maturation assessment using an innovative artificial intelligence-based imaging analysis system

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biomedical Signal Processing and Control Pub Date : 2024-10-30 DOI:10.1016/j.bspc.2024.107088
Hossam Magdy Balaha , Ahmed Alksas , Amine Fattal , Amir A. Sewelam , Wael Aboelmaaty , Khaled Abdel-Ghaffar , Toru Deguchi , Ayman El-Baz
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Abstract

The Cervical Vertebral Maturation (CVM) assessment plays a pivotal role in orthodontic diagnosis and treatment planning by providing insights into skeletal growth and enabling timely interventions. This study introduces an innovative approach to predict CVM stages based on novel imaging markers extracted from X-ray images, which are then correlated with CVM stages. The proposed system comprises the following main steps: (i) initiating with manually delineated cervical vertebrae (i.e., C2, C3, and C4) from the X-ray images; (ii) parcellating the cervical vertebrae based on the Marching level-sets approach to generate five iso-contours for each segmented cervical vertebra; the primary objective of vertebrae segmentation is to extract both local and global imaging markers to accurately grade and classify CVM stages; (iii) extracting first and second-order appearance and morphology imaging markers that describe the shape and appearance of each extracted cervical vertebra; and (iv) employing two-stage classifiers to grade and classify CVM for each patient. The system without data augmentation demonstrated promising results, achieving an accuracy of 95.85%, sensitivity of 88.03%, specificity of 97.20%, and precision of 88.70%. After applying data augmentation techniques, the accuracy improved to 98.89%, with a mean score of 97.20%. To the best of our knowledge, this is the first system to assess the six stages of CVM with such high accuracy. The proposed AI-based system will enhance orthodontic patient care in the USA and worldwide by providing a new non-invasive tool for early CVM assessment.
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利用创新型人工智能成像分析系统评估颈椎成熟度
颈椎发育成熟度(CVM)评估通过深入了解骨骼生长情况和及时干预,在正畸诊断和治疗计划中发挥着关键作用。本研究介绍了一种基于从 X 光图像中提取的新型成像标记预测 CVM 阶段的创新方法,然后将这些标记与 CVM 阶段相关联。拟议的系统包括以下主要步骤:(i) 以人工划定的颈椎(即 C2、C3 和 C4)为起始点、椎体分割的主要目的是提取局部和全局成像标记,以准确分级和划分 CVM 阶段;(iii) 提取描述每个提取的颈椎的形状和外观的一阶和二阶外观和形态成像标记;以及 (iv) 采用两阶段分类器对每个患者的 CVM 进行分级和分类。未使用数据增强技术的系统取得了可喜的成果,准确率达到 95.85%,灵敏度达到 88.03%,特异性达到 97.20%,精确度达到 88.70%。应用数据增强技术后,准确率提高到 98.89%,平均得分 97.20%。据我们所知,这是首个能以如此高的准确率评估 CVM 六个阶段的系统。所提出的基于人工智能的系统将为早期 CVM 评估提供一种新的无创工具,从而提高美国乃至全球的正畸患者护理水平。
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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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