基于卷积神经网络的口腔角化牙龈检测和测量效率

IF 4.2 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE
Gokce Aykol-Sahin, Ozgun Yucel, Nihal Eraydin, Gonca Cayir Keles, Umran Unlu, Ulku Baser
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引用次数: 0

摘要

背景:随着人工智能领域的最新进展,该技术的使用已开始促进全面的组织评估和干预规划。本研究旨在评估深度学习算法中的不同卷积神经网络(CNN),以根据口内照片检测角化牙龈,并评估网络测量角化牙龈宽度的能力:方法: 使用涂抹揭露剂前后拍摄的 1200 张照片中的 600 张照片来比较神经网络分割角化牙龈的能力。使用准确率、交集大于联合和 F1 分数评估网络的分割性能。根据地面实况图像测量了参考点的角化牙龈宽度,并与临床医生的测量结果和由 ResNet50 模型生成的 DeepLab 图像进行了比较。通过三因素混合设计方差分析(ANOVA)评估了测量操作者、表型和颌骨对测量差异的影响:在比较的网络中,ResNet50区分角化牙龈的准确率最高,达到91.4%。根据颌骨和表型,深度学习和临床医生的测量结果非常一致。在分析测量操作员、表型和颌骨对根据基本真相进行的测量的影响时,测量操作员和颌骨存在显著的统计学差异(p 结论:颌骨和表型的测量结果与基本真相的测量结果存在显著差异,而颌骨和表型的测量结果与基本真相的测量结果存在显著差异:使用 ResNet50 模型进行角化牙龈自动分割可能是辅助专业人员的一种可行方法。白话摘要:随着人工智能(AI)的最新进展,现在有可能利用这项技术来评估组织并全面规划医疗程序。这项研究主要测试不同的人工智能模型,特别是 CNN,利用口腔内拍摄的照片来识别和测量一种特定类型的牙龈组织,即角化牙龈。研究使用了 1200 张照片中的 600 张,以比较不同 CNN 在识别牙龈组织方面的性能。对这些模型的准确性和有效性进行了测量,并与人类临床医生的评分进行了比较。研究发现,ResNet50 模型最准确,91.4% 的时间都能正确识别牙龈组织。在比较人工智能模型和临床医生对牙龈组织宽度的测量结果时,结果非常相似,尤其是在考虑到不同颌骨和牙龈结构的情况下。研究还分析了各种因素对测量结果的影响,并发现测量者和颌骨类型之间存在显著差异。总之,使用 ResNet50 模型自动识别和测量牙龈组织可以成为牙科专业人员的实用工具,既节省时间,又减少了对专业知识的要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficiency of oral keratinized gingiva detection and measurement based on convolutional neural network.

Background: With recent advances in artificial intelligence, the use of this technology has begun to facilitate comprehensive tissue evaluation and planning of interventions. This study aimed to assess different convolutional neural networks (CNN) in deep learning algorithms to detect keratinized gingiva based on intraoral photos and evaluate the ability of networks to measure keratinized gingiva width.

Methods: Six hundred of 1200 photographs taken before and after applying a disclosing agent were used to compare the neural networks in segmenting the keratinized gingiva. Segmentation performances of networks were evaluated using accuracy, intersection over union, and F1 score. Keratinized gingiva width from a reference point was measured from ground truth images and compared with the measurements of clinicians and the DeepLab image that was generated from the ResNet50 model. The effect of measurement operators, phenotype, and jaw on differences in measurements was evaluated by three-factor mixed-design analysis of variance (ANOVA).

Results: Among the compared networks, ResNet50 distinguished keratinized gingiva at the highest accuracy rate of 91.4%. The measurements between deep learning and clinicians were in excellent agreement according to jaw and phenotype. When analyzing the influence of the measurement operators, phenotype, and jaw on the measurements performed according to the ground truth, there were statistically significant differences in measurement operators and jaw (p < 0.05).

Conclusions: Automated keratinized gingiva segmentation with the ResNet50 model might be a feasible method for assisting professionals. The measurement results promise a potentially high performance of the model as it requires less time and experience.

Plain language summary: With recent advances in artificial intelligence (AI), it is now possible to use this technology to evaluate tissues and plan medical procedures thoroughly. This study focused on testing different AI models, specifically CNN, to identify and measure a specific type of gum tissue called keratinized gingiva using photos taken inside the mouth. Out of 1200 photos, 600 were used in the study to compare the performance of different CNN in identifying gingival tissue. The accuracy and effectiveness of these models were measured and compared to human clinician ratings. The study found that the ResNet50 model was the most accurate, correctly identifying gingival tissue 91.4% of the time. When the AI model and clinicians' measurements of gum tissue width were compared, the results were very similar, especially when accounting for different jaws and gum structures. The study also analyzed the effect of various factors on the measurements and found significant differences based on who took the measurements and jaw type. In conclusion, using the ResNet50 model to identify and measure gum tissue automatically could be a practical tool for dental professionals, saving time and requiring less expertise.

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来源期刊
Journal of periodontology
Journal of periodontology 医学-牙科与口腔外科
CiteScore
9.10
自引率
7.00%
发文量
290
审稿时长
3-8 weeks
期刊介绍: The Journal of Periodontology publishes articles relevant to the science and practice of periodontics and related areas.
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