Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance.

PURPOSE: 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. MATERIALS AND 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.

User experience of and satisfaction with computer-aided design software when designing dental prostheses: A multicenter survey study.

AIM: The current study aimed to compare the responses and satisfaction reported by users with varying levels of experience when using different types of computer-aided design (CAD) software programs to design crowns. MATERIALS AND METHODS: A questionnaire was used to evaluate user responses to five domains (software visibility, 3Dscanned data preparation, crown design and adjustment, finish line registration, and overall experience) of various CAD software programs. The study included 50 undergraduate dental students (inexperienced group) and 50 dentists or dental technicians from two hospitals (experienced group). The participants used four different CAD software programs (Meshmixer, Exocad, BlueSkyPlan, and Dentbird) to design crowns and recorded the features using the questionnaire. Statistical analyses included one-way and two-way analysis of variance (ANOVA) tests to compare scores and verify the interaction between user response and experience. RESULT: User evaluation scores in the domains of software visibility and 3D-scanned data preparation varied between software programs (P < 0.001), with Exocad being favored by the experienced group. When evaluating crown design and finish line registration, Dentbird and Exocad scored significantly higher than the other software in both groups as they offered automation of the process using deep learning (P < 0.001). Two-way ANOVA showed that prior experience of using CAD significantly affected the users' responses to all queries (P < 0.001). CONCLUSION: User response and satisfaction varied with the type of CAD software used to design dental prostheses, with prior experience of using CAD playing a significant role. Automation of design functions can enhance user satisfaction with the software.

Reliable Reference Areas for Three-dimensional Smiling Facial Model Alignments: Posed Versus Natural Smile Expressions.

PURPOSE: This study aims to evaluate the reliability of various reference areas for digital alignment between three-dimensional (3D) resting and smiling facial models. MATERIALS AND METHODS: 3D posed and natural smiling faces of 33 adults were registered to the respective neutral faces, using six matching strategies with different reference matching surfaces: nose (N), nose + central forehead (NFc), nose + whole forehead (NFw), nose + chin (NC), nose + central forehead + chin (NFcC), and nose + whole forehead + chin (NFwC). The positional discrepancies of the registered images were measured at the left and right pupil centers. RESULTS: Two-way ANOVA and post hoc multiple pairwise t-test with Bonferroni correction (α = .05) were used to evaluate the measurements. As a result, the use of larger reference areas increases the trueness of image matching; whereas, there was no statistically significant difference between the matching strategies within the same smiling type. Meanwhile, the image registration of posed smiles resulted in fewer positional disparities than the natural smiles with significant differences observed for the registration using the NC and NFcC surface-based matching areas at the right pupil (P = .030 and .026, respectively). CONCLUSION: The findings of this study suggested that the reference surface areas and smiling types have some impacts on the accuracy of 3D smiling facial image alignments. Large and evenly distributed matching surfaces are recommended for posed smiles; whereas caution should be taken when using the chin area as a reference surface for matching natural smile facial images. Int J Prosthodont 2023. doi: 10.11607/ijp.8364.

Intra- and Interrater Agreement of Face Esthetic Analysis in 3D Face Images.

The use of three-dimensional (3D) facial scans for facial analysis is increasing in maxillofacial treatment. The aim of this study was to investigate the consistency of two-dimensional (2D) and 3D facial analyses performed by multiple raters. Six men and four women (25-36-year-old) participated in this study. The 2D images of the smiling and resting faces in the frontal and sagittal planes were obtained. The 3D facial and intraoral scans were merged to generate virtual 3D faces. Ten clinicians performed facial analyses by investigating 14 indices of 2D and 3D faces. Intra- and interrater agreements of the results of 2D and 3D facial analyses within and among the participants were evaluated. The intrarater agreement between the 2D and 3D facial analyses varied according to the indices. The highest and lowest agreements were found for the dental crowding index (0.94) and smile line curvature index (0.56) in the frontal plane, and Angle's classification (canine) index (0.98) and occlusal plane angle index (0.55) in the profile plane. In the frontal plane, the interrater agreements were generally higher for the 3D images than for the 2D images, while in the profile plane, the interrater agreements were high in the Angle's classification (canine) index however low in the other indices. Several occlusion-related indices were missing in the 2D images because the posterior teeth were not observed. Esthetic analysis results between 2D and 3D face images can differ according to the evaluation indices. The use of 3D faces is recommended over 2D images to increase the reliability of facial analyses, as it can fully assess both esthetic and occlusion-related indices.

Three-dimensional morphometric analysis of facial units in virtual smiling facial images with different smile expressions.

PURPOSE: Accuracy of image matching between resting and smiling facial models is affected by the stability of the reference surfaces. This study aimed to investigate the morphometric variations in subdivided facial units during resting, posed and spontaneous smiling. MATERIALS AND METHODS: The posed and spontaneous smiling faces of 33 adults were digitized and registered to the resting faces. The morphological changes of subdivided facial units at the forehead (upper and lower central, upper and lower lateral, and temple), nasal (dorsum, tip, lateral wall, and alar lobules), and chin (central and lateral) regions were assessed by measuring the 3D mesh deviations between the smiling and resting facial models. The one-way analysis of variance, Duncan post hoc tests, and Student's t-test were used to determine the differences among the groups (α = .05). RESULTS: The smallest morphometric changes were observed at the upper and central forehead and nasal dorsum; meanwhile, the largest deviation was found at the nasal alar lobules in both the posed and spontaneous smiles (P < .001). The spontaneous smile generally resulted in larger facial unit changes than the posed smile, and significant difference was observed at the alar lobules, central chin, and lateral chin units (P < .001). CONCLUSION: The upper and central forehead and nasal dorsum are reliable areas for image matching between resting and smiling 3D facial images. The central chin area can be considered an additional reference area for posed smiles; however, special cautions should be taken when selecting this area as references for spontaneous smiles.