Journal article
Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance
Journal of prosthodontics, Vol.34(2), pp.204-215
02/2025
DOI: 10.1111/jopr.13900
PMID: 39010644
Abstract
PurposeThis 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.Materials and MethodsThis 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.ResultsThe 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.ConclusionsThis 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.
Details
- Title: Subtitle
- Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance
- Creators
- Hang-Nga Mai - Kyungpook Natl Univ, Inst Translat Res Dent, Daegu, South KoreaThaw Thaw Win - Kyungpook National UniversityHyeong-Seob Kim - Kyung Hee UniversityAhran Pae - Kyung Hee University Medical CenterWael Att - University of FreiburgDang Dinh Nguyen - Kyungpook National UniversityDu-Hyeong Lee - Kyungpook Natl Univ, Inst Translat Res Dent, Daegu, South Korea
- Resource Type
- Journal article
- Publication Details
- Journal of prosthodontics, Vol.34(2), pp.204-215
- DOI
- 10.1111/jopr.13900
- PMID
- 39010644
- NLM abbreviation
- J Prosthodont
- ISSN
- 1059-941X
- eISSN
- 1532-849X
- Publisher
- Wiley
- Number of pages
- 12
- Grant note
- Ministry of Science and ICT; Ministry of Science, ICT & Future Planning, Republic of Korea 2020R1I1A1A01062967 / Bio & Medical Technology Development Program of the National Research Foundation - Korean government; National Research Foundation of Korea Korea Medical Device Development Fund Ministry of Trade, Industry and Energy 202011A02 / Ministry of Food and Drug Safety; Ministry of Food & Drug Safety (MFDS), Republic of Korea Ministry of Health& Welfare, Republic of Korea; Ministry of Health & Welfare (MOHW), Republic of Korea Korean government; Korean Government
- Language
- English
- Date published
- 02/2025
- Academic Unit
- Prosthodontics
- Record Identifier
- 9984914018302771
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