深度卷积神经网络对颈椎成熟度的评估。

IF 1.6 3区 医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Oral Radiology Pub Date : 2023-10-01 Epub Date: 2023-03-09 DOI:10.1007/s11282-023-00678-7
Gülsün Akay, M Ali Akcayol, Kevser Özdem, Kahraman Güngör
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引用次数: 0

摘要

目的:本研究旨在使用所提出的基于深度学习的卷积神经网络(CNN)模型自动确定侧位头影测量X线片图像上的颈椎成熟(CVM)过程,并使用精度、召回率和F1评分测试该CNN模型在检测CVM阶段方面的成功率。方法:本研究共纳入588张按时间顺序年龄在8至22岁之间的患者的数字侧位头影测量照片。CVM评估由两名牙颌面部放射科医生进行。根据生长过程将图像中的CVM分期分为6个亚组。本研究开发了一个卷积神经网络(CNN)模型。开发的模型在Jupyter Notebook环境中使用Python编程语言、Keras和TensorFlow库进行了实验研究。结果:经过40个时期的训练,获得了58%的训练准确率和57%的测试准确率。该模型获得的结果与测试数据上的训练非常接近。另一方面,确定该模型在CVM阶段1的精度和F1得分方面表现出最高的成功率,在CVM第二阶段的召回值方面表现出最大的成功率。结论:实验结果表明,所开发的模型取得了中等的成功,并且在CVM阶段分类中达到了58.66%的分类准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep convolutional neural network-the evaluation of cervical vertebrae maturation.

Deep convolutional neural network-the evaluation of cervical vertebrae maturation.

Objectives: This study aimed to automatically determine the cervical vertebral maturation (CVM) processes on lateral cephalometric radiograph images using a proposed deep learning-based convolutional neural network (CNN) model and to test the success rate of this CNN model in detecting CVM stages using precision, recall, and F1-score.

Methods: A total of 588 digital lateral cephalometric radiographs of patients with a chronological age between 8 and 22 years were included in this study. CVM evaluation was carried out by two dentomaxillofacial radiologists. CVM stages in the images were divided into 6 subgroups according to the growth process. A convolutional neural network (CNN) model was developed in this study. Experimental studies for the developed model were carried out in the Jupyter Notebook environment using the Python programming language, the Keras, and TensorFlow libraries.

Results: As a result of the training that lasted 40 epochs, 58% training and 57% test accuracy were obtained. The model obtained results that were very close to the training on the test data. On the other hand, it was determined that the model showed the highest success in terms of precision and F1-score in the CVM Stage 1 and the highest success in the recall value in the CVM Stage 2.

Conclusion: The experimental results have shown that the developed model achieved moderate success and it reached a classification accuracy of 58.66% in CVM stage classification.

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来源期刊
Oral Radiology
Oral Radiology DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.20
自引率
13.60%
发文量
87
审稿时长
>12 weeks
期刊介绍: As the official English-language journal of the Japanese Society for Oral and Maxillofacial Radiology and the Asian Academy of Oral and Maxillofacial Radiology, Oral Radiology is intended to be a forum for international collaboration in head and neck diagnostic imaging and all related fields. Oral Radiology features cutting-edge research papers, review articles, case reports, and technical notes from both the clinical and experimental fields. As membership in the Society is not a prerequisite, contributions are welcome from researchers and clinicians worldwide.
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