应用人工智能(AI)预测全景x线图像中吞咽困难的基础研究第二部分:舌骨在全景x线片上的位置分析

Yukiko Matsuda, Emi Ito, Migiwa Kuroda, Kazuyuki Araki, Wataru Nakada, Yoshihiko Hayakawa
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引用次数: 0

摘要

背景:口腔虚弱与全身虚弱相关。当考虑到吞咽困难的风险时,舌骨的垂直位置很重要。然而,牙医通常不关注这个位置。目的:建立垂直舌骨位置检测的人工智能模型。方法:本研究使用915张全景x线片中的1830张舌骨图像进行人工智能学习。根据与我们先前研究相同的标准,舌骨的位置分为六种类型(0、1、2、3、4和5)。计划1学习了所有类型。在Plan 2中,学习除了Type 0之外的5种类型。为了减少分组的数量,在每个类中使用两个类型的组合形成三个类。计划3用于学习所有三个类别,计划4用于学习A类以外的两个类别(类型0和1)。计算精密度、召回率、f值、准确度和精确召回率曲线下面积(PR-AUCs)并进行比较评价。结果:方案4准确度最高,PR-AUC值分别为0.93和0.97。结论:通过减少课堂数量和不学习部分解剖结构不可见的病例,可以正确地发现垂直舌骨。
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A Basic Study for Predicting Dysphagia in Panoramic X-ray Images Using Artificial Intelligence (AI) Part 2: Analysis of the Position of the Hyoid Bone on Panoramic Radiographs
Background: Oral frailty is associated with systemic frailty. The vertical position of the hyoid bone is important when considering the risk of dysphagia. However, dentists usually do not focus on this position. Purpose: To create an AI model for detection of the position of the vertical hyoid bone. Methods: In this study, 1830 hyoid bone images from 915 panoramic radiographs were used for AI learning. The position of the hyoid bone was classified into six types (Types 0, 1, 2, 3, 4, and 5) based on the same criteria as in our previous study. Plan 1 learned all types. In Plan 2, the five types other than Type 0 were learned. To reduce the number of groupings, three classes were formed using combinations of two types in each class. Plan 3 was used for learning all three classes, and Plan 4 was used for learning the two classes other than Class A (Types 0 and 1). Precision, recall, f-values, accuracy, and areas under the precision–recall curves (PR-AUCs) were calculated and comparatively evaluated. Results: Plan 4 showed the highest accuracy and PR-AUC values, of 0.93 and 0.97, respectively. Conclusions: By reducing the number of classes and not learning cases in which the anatomical structure was partially invisible, the vertical hyoid bone was correctly detected.
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