应用三维神经网络和可解释人工智能对下颌磨牙的 ICDAS 检测系统进行分类。

IF 4.3 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE
Taseef Hasan Farook, Saif Ahmed, Farah Rashid, Faisal Ahmed Sifat, Preena Sidhu, Pravinkumar Patil, Sumaya Yousuf Zai, Nafij Bin Jamayet, James Dudley, Umer Daood
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引用次数: 0

摘要

问题陈述:龋洞制备方法和方式存在很大差异。目的:本研究的目的是调查不同操作者在进行国际龋病检测与评估系统(ICDAS)分类后的三维(3D)模型龋洞预备的差异,以及随后对深度学习模型预测龋洞分类能力的影响:两名操作者根据4种ICDAS分类在模拟下颌第一磨牙上制作了56个修复体,随后进行了三维扫描和计算机辅助设计处理。计算表面积、虚拟体积、豪斯多夫距离(HD)和骰子相似系数。多变量方差分析用于评估龋洞大小与操作员熟练程度的交互作用,单因素方差分析用于评估 4 种龋洞分类中的 HD 差异(α=.05)。三维卷积神经网络(CNN)预测了 ICDAS 等级,而 Saliency Maps 则解释了模型的决定:操作员 1 的龋洞预备表面积为 360.55 ±15.39 平方毫米,操作员 2 的龋洞预备表面积为 355.24 ±10.79 平方毫米。体积差异显示,操作员 1 为 440.41 ±35.29 mm3,操作员 2 为 441.01 ±35.37 mm3。腔隙大小与操作者熟练程度之间存在显著的交互作用(F=2.31,P=.01)。两名操作者的重叠制备差异最小为 0.13 ±0.097 mm。三维 CNN 模型对 ICDAS 分级的准确率为 94.44%,在区分两名操作者制备的牙洞时,准确率为 66.67%:在咬合龋洞底,操作者的表现差异明显,这主要是由于龋洞深度不同造成的。深度学习能有效地对三维口内扫描的龋洞深度进行分类,受预备质量或操作者技能的影响较小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of 3D neural networks and explainable AI to classify ICDAS detection system on mandibular molars.

Statement of problem: Considerable variations exist in cavity preparation methods and approaches. Whether the extent and depth of cavity preparation because of the extent of caries affects the overall accuracy of training deep learning models remains unexplored.

Purpose: The purpose of this study was to investigate the difference in 3-dimensionsal (3D) model cavity preparations after International Caries Detection and Assessment System (ICDAS) classification performed by different practitioners and the subsequent influence on the ability of a deep learning model to predict cavity classification.

Material and methods: Two operators prepared 56 restorative cavities on simulated mandibular first molars according to 4 ICDAS classifications, followed by 3D scanning and computer-aided design processing. The surface area, virtual volume, Hausdorff distance (HD), and Dice Similarity Coefficients were computed. Multivariate analysis of variance was used to assess cavity size and operator proficiency interactions, and 1-way ANOVA was used to evaluate HD differences across 4 cavity classifications (α=.05). The 3D convolutional neural network (CNN) predicted the ICDAS class, and Saliency Maps explained the decisions of the models.

Results: Operator 1 exhibited a cavity preparation surface area of 360.55 ±15.39 mm2, and operator 2 recorded 355.24 ±10.79 mm2. Volumetric differences showed operator 1 with 440.41 ±35.29 mm3 and operator 2 with 441.01 ±35.37 mm3. Significant interactions (F=2.31, P=.01) between cavity size and operator proficiency were observed. A minimal 0.13 ±0.097 mm variation was noted in overlapping preparations by the 2 operators. The 3D CNN model achieved an accuracy of 94.44% in classifying the ICDAS classes with a 66.67% accuracy when differentiating cavities prepared by the 2 operators.

Conclusions: Operator performance discrepancies were evident in the occlusal cavity floor, primarily due to varying cavity depths. Deep learning effectively classified cavity depths from 3D intraoral scans and was less affected by preparation quality or operator skills.

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来源期刊
Journal of Prosthetic Dentistry
Journal of Prosthetic Dentistry 医学-牙科与口腔外科
CiteScore
7.00
自引率
13.00%
发文量
599
审稿时长
69 days
期刊介绍: The Journal of Prosthetic Dentistry is the leading professional journal devoted exclusively to prosthetic and restorative dentistry. The Journal is the official publication for 24 leading U.S. international prosthodontic organizations. The monthly publication features timely, original peer-reviewed articles on the newest techniques, dental materials, and research findings. The Journal serves prosthodontists and dentists in advanced practice, and features color photos that illustrate many step-by-step procedures. The Journal of Prosthetic Dentistry is included in Index Medicus and CINAHL.
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