Abstract：
OBJECTIVE: This study aimed to examine the satisfaction of dental professionals, including dental students, dentists, and dental technicians, with computer-aided design (CAD) software performance using deep learning (DL) and explainable artificial intelligence (XAI)-based behavioral analysis concepts.
METHODS: This study involved 436 dental professionals with diverse CAD experiences to assess their satisfaction with various dental CAD software programs. Through exploratory factor analysis, latent factors affecting user satisfaction were extracted from the observed variables. A multilayer perceptron artificial neural network (MLP-ANN) model was developed along with permutation feature importance analysis (PFIA) and the Shapley additive explanation (Shapley) method to gain XAI-based insights into individual factors\' significance and contributions.
RESULTS: The MLP-ANN model outperformed a standard logistic linear regression model, demonstrating high accuracy (95%), precision (84%), and recall rates (84%) in capturing complex psychological problems related to human attitudes. PFIA revealed that design adjustability was the most important factor impacting dental CAD software users\' satisfaction. XAI analysis highlighted the positive impacts of features supporting the finish line and crown design, while the number of design steps and installation time had negative impacts. Notably, finish-line design-related features and the number of design steps emerged as the most significant factors.
CONCLUSIONS: This study sheds light on the factors influencing dental professionals\' decisions in using and selecting CAD software. This approach can serve as a proof-of-concept for applying DL-XAI-based behavioral analysis in dentistry and medicine, facilitating informed software selection and development.
METHODS: This study involved 436 dental professionals with diverse CAD experiences to assess their satisfaction with various dental CAD software programs. Through exploratory factor analysis, latent factors affecting user satisfaction were extracted from the observed variables. A multilayer perceptron artificial neural network (MLP-ANN) model was developed along with permutation feature importance analysis (PFIA) and the Shapley additive explanation (Shapley) method to gain XAI-based insights into individual factors\' significance and contributions.
RESULTS: The MLP-ANN model outperformed a standard logistic linear regression model, demonstrating high accuracy (95%), precision (84%), and recall rates (84%) in capturing complex psychological problems related to human attitudes. PFIA revealed that design adjustability was the most important factor impacting dental CAD software users\' satisfaction. XAI analysis highlighted the positive impacts of features supporting the finish line and crown design, while the number of design steps and installation time had negative impacts. Notably, finish-line design-related features and the number of design steps emerged as the most significant factors.
CONCLUSIONS: This study sheds light on the factors influencing dental professionals\' decisions in using and selecting CAD software. This approach can serve as a proof-of-concept for applying DL-XAI-based behavioral analysis in dentistry and medicine, facilitating informed software selection and development.
摘要：
目的：本研究旨在检查牙科专业人员的满意度，包括牙科学生，牙医,和牙科技术员,使用基于深度学习（DL）和可解释人工智能（XAI）的行为分析概念的计算机辅助设计（CAD）软件性能。
方法：这项研究涉及436名具有不同CAD经验的牙科专业人员，以评估他们对各种牙科CAD软件程序的满意度。通过探索性因素分析,从观察到的变量中提取了影响用户满意度的潜在因素。开发了多层感知器人工神经网络（MLP-ANN）模型以及置换特征重要性分析（PFIA）和Shapley加性解释（Shapley）方法，以获得基于XAI的对单个因素的重要性和贡献的见解。
结果：MLP-ANN模型优于标准逻辑线性回归模型，显示高精度（95％），精度（84%），和召回率（84％）在捕捉与人类态度有关的复杂心理问题。PFIA透露，设计可调性是影响牙科CAD软件用户满意度的最重要因素。XAI分析强调了支持终点线和皇冠设计的功能的积极影响，而设计步骤和安装时间的数量有负面影响。值得注意的是,与终点线设计相关的功能和设计步骤的数量成为最重要的因素。
结论：本研究揭示了影响牙科专业人员使用和选择CAD软件决策的因素。这种方法可以作为在牙科和医学中应用基于DL-XAI的行为分析的概念证明。促进明智的软件选择和开发。
方法：这项研究涉及436名具有不同CAD经验的牙科专业人员，以评估他们对各种牙科CAD软件程序的满意度。通过探索性因素分析,从观察到的变量中提取了影响用户满意度的潜在因素。开发了多层感知器人工神经网络（MLP-ANN）模型以及置换特征重要性分析（PFIA）和Shapley加性解释（Shapley）方法，以获得基于XAI的对单个因素的重要性和贡献的见解。
结果：MLP-ANN模型优于标准逻辑线性回归模型，显示高精度（95％），精度（84%），和召回率（84％）在捕捉与人类态度有关的复杂心理问题。PFIA透露，设计可调性是影响牙科CAD软件用户满意度的最重要因素。XAI分析强调了支持终点线和皇冠设计的功能的积极影响，而设计步骤和安装时间的数量有负面影响。值得注意的是,与终点线设计相关的功能和设计步骤的数量成为最重要的因素。
结论：本研究揭示了影响牙科专业人员使用和选择CAD软件决策的因素。这种方法可以作为在牙科和医学中应用基于DL-XAI的行为分析的概念证明。促进明智的软件选择和开发。
