Deep learning based quantitative cervical vertebral maturation analysis.

IF 2.4 2区 医学 Q2 DENTISTRY, ORAL SURGERY & MEDICINE
Fulin Jiang, Abbas Ahmed Abdulqader, Yan Yan, Fangyuan Cheng, Tao Xiang, Jinghong Yu, Juan Li, Yong Qiu, Xin Chen
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引用次数: 0

Abstract

Objectives: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives withstand the complex anatomical variations. Therefore, we designed an advanced two-stage convolutional neural network customized for improved accuracy in cervical vertebrae analysis.

Methods: This study analyzed 2100 cephalometric images. The data distribution to an 8:1:1 for training, validation, and testing. The CVnet system was designed as a two-step method with a comprehensive evaluation of various regions of interest (ROI) sizes to locate 19 cervical vertebral landmarks and classify precision maturation stages. The accuracy of landmark localization was assessed by success detection rate and student t-test. The QCVM diagnostic accuracy test was conducted to evaluate the assistant performances of our system for six junior orthodontists.

Results: Upon precise calibration with optimal ROI size, the landmark localization registered an average error of 0.66 ± 0.46 mm and a success detection rate of 98.10% within 2 mm. Additionally, the identification accuracy of QCVM stages was 69.52%, resulting in an enhancement of 10.95% in the staging accuracy of junior orthodontists in the diagnostic test.

Conclusions: This study presented a two-stage neural network that successfully automated the identification of cervical vertebral landmarks and the staging of QCVM. By streamlining the workflow and enhancing the accuracy of skeletal maturation estimation, this method offered valuable clinical support, particularly for practitioners with limited experience or access to advanced diagnostic resources, facilitating more consistent and reliable treatment planning.

基于深度学习的颈椎成熟度定量分析。
目的:本研究旨在通过精确的地标定位来提高颈椎成熟(QCVM)定量分期的临床诊断。现有的方法往往是主观的,耗时的,而深度学习的替代方案可以承受复杂的解剖变化。因此,我们设计了一种先进的两阶段卷积神经网络,以提高颈椎分析的准确性。方法:对2100张头颅图像进行分析。数据以8:1:1的比例分布,用于培训、验证和测试。CVnet系统设计为两步法,对各种感兴趣区域(ROI)大小进行综合评估,定位19个颈椎地标并对精确成熟阶段进行分类。采用成功检出率和学生t检验评价地标定位的准确性。对6名初级正畸医师进行QCVM诊断准确性测试,评价系统的辅助性能。结果:采用最佳ROI尺寸进行精确标定后,标记定位的平均误差为0.66±0.46 mm, 2 mm范围内的成功率为98.10%。QCVM分期的识别准确率为69.52%,使初级正畸医师在诊断测试中的分期准确率提高了10.95%。结论:本研究提出了一个两阶段神经网络,成功地自动识别颈椎标志和QCVM的分期。通过简化工作流程和提高骨骼成熟度估计的准确性,该方法提供了宝贵的临床支持,特别是对于经验有限或无法获得先进诊断资源的从业者,促进了更一致和可靠的治疗计划。
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来源期刊
Head & Face Medicine
Head & Face Medicine DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.70
自引率
3.30%
发文量
32
审稿时长
>12 weeks
期刊介绍: Head & Face Medicine is a multidisciplinary open access journal that publishes basic and clinical research concerning all aspects of cranial, facial and oral conditions. The journal covers all aspects of cranial, facial and oral diseases and their management. It has been designed as a multidisciplinary journal for clinicians and researchers involved in the diagnostic and therapeutic aspects of diseases which affect the human head and face. The journal is wide-ranging, covering the development, aetiology, epidemiology and therapy of head and face diseases to the basic science that underlies these diseases. Management of head and face diseases includes all aspects of surgical and non-surgical treatments including psychopharmacological therapies.
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