Research Spotlight

Revilla-León, M., Zandinejad, A., Nair, M.K., Barmak, B.A., Feilzer, A.J. and Özcan, M. (2021). “Accuracy of a patient 3-dimensional virtual representation obtained from the superimposition of facial and intraoral scans guided by extraoral and intraoral scan body systems.” J Prosthet Dent Apr 7;S0022-3913(21)00106-2. [Epub ahead of print].

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STATEMENT OF PROBLEM: A patient 3-dimensional virtual representation aims to facilitate the integration of facial references into treatment planning or prosthesis design procedures, but the accuracy of the virtual patient representation remains unclear. PURPOSE: The purpose of the present observational clinical study was to determine and compare the accuracy (trueness and precision) of a virtual patient obtained from the superimposition procedures of facial and intraoral digital scans guided by 2 scan body systems. MATERIAL AND METHODS: Ten participants were recruited. An intraoral digital scan was completed (TRIOS 4). Four fiduciary markers were placed in the glabella (Gb), left (IOL) and right infraorbital canal (IOR), and tip of the nose (TN). Two digitizing procedures were completed: cone beam computed tomography (CBCT) (i-CAT FLX V-Series) and facial scans (Face Camera Pro Bellus) with 2 different scan body systems: AFT (ScanBodyFace) and Sat 3D (Sat 3D). For the AFT system, a reference facial scan was obtained, followed by a facial scan with the participant in the same position as when capturing the CBCT scan. For the Sat 3D system, a reference facial scan was recorded, followed by a facial scan with the patient in the same position as when capturing the CBCT scan. The patient 3-dimensional representation for each scan body system was obtained by using a computer program (Matera 2.4). A total of 14 interlandmark distances were measured in the CBCT scan and both 3-dimensional patient representations. The discrepancies between the CBCT scan (considered the standard) and each 3-dimensional representation of each patient were used to analyze the data. The Kolmogorov-Smirnov test revealed that trueness and precision values were not normally distributed (P<.05). A log(10) transformation was performed with 1-way repeated-measures MANOVA (α=.05). RESULTS: The accuracy of the virtual 3-dimensional patient representations obtained by using AFT and Sat 3D systems showed a trueness ranging from 0.50 to 1.64 mm and a precision ranging from 0.04 to 0.14 mm. The Wilks lambda detected an overall significant difference in the accuracy values between the AFT and Sat 3D systems (F=3628.041, df=14, P<.001). A significant difference was found in 12 of the 14 interlandmark measurements (P<.05). The AFT system presented significantly higher discrepancy values in Gb-IOL, TN-IOR, IOL-IOR, and TN-6 (P<.05) than in the Sat 3D system. The Sat 3D system had a significantly higher discrepancy in Gb-TN, TN-IOL, IOL-3, IOL-6, TN-8, TN-9, TN-11, IOR-11, and IOR-14 (P<.05) than in the AFT system. The Wilcoxon signed-rank test did not detect any significant difference in the precision values between the AFT and Sat 3D systems (Z=-0.838, P=.402). CONCLUSIONS: The accuracy of the patient 3-dimensional virtual representations obtained using AFT and Sat 3D systems showed trueness values ranging from 0.50 to 1.64 mm and precision values ranging from 0.04 to 0.14 mm. The AFT system obtained higher trueness than the Sat 3D system

Revilla-León, M., Pérez-Barquero, J.A., Barmak, B.A., Agustín-Panadero, R., Fernández-Estevan, L. and Att, W. (2021). “Facial scanning accuracy depending on the alignment algorithm and digitized surface area location: An in vitro study.” J Dent Apr 24;103680. [Epub ahead of print]. 103680.

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OBJECTIVES: To measure the accuracy (trueness and precision) of a facial scanner depending on the alignment method and the digitized surface area location. METHODS: Fourteen markers were adhered on a head mannequin and digitized using an industrial scanner (GOM Atos Q 3D 12 M; Carl Zeiss Industrielle Messtechnik GmbH). A control mesh was acquired. Subsequently, the mannequin was digitized using a facial scanner (Arc4; Bellus3D) (n = 30). The control mesh was delineated into 10 areas. Based on the alignment procedures, two groups were created: reference best fit (RBF group) and landmark-based best fit (LA group). The root mean square was used to calculate the discrepancy between the control mesh and each facial scan. A 2-way ANOVA and Tukey pairwise comparison tests were used to compare trueness and precision between the 2 groups across 10 areas (α = .05). RESULTS: Both alignment algorithms (P = .007) and digitized area (P < .001) were significant predictors of trueness with a significant interaction between the two predictors (F (9, 580) =25.13, P < .001). Tukey pairwise comparison showed that there was a significant difference between mean trueness values of RBF (mean=0.53 mm) and LA (mean=0.55 mm) groups. Moreover, a significant difference was detected among the trueness values across surface areas. The A9-area (left tragus area) had the highest and A5-area (right cheek area) had the lowest mean trueness. Both alignment algorithm (P < .001) and digitized surface area (P < .001) were significant predictors of precision with a significant interaction between the two predictors (F (9, 580) =14.34, P < .001). Tukey pairwise comparison showed that there was a significant difference between mean precision values of RBF (mean=0.38 mm) and LA (mean=0.35 mm) groups. Moreover, a significant difference was detected among the precision values across surface areas. Comparing the surface areas, A9-area had the highest and A10-area (forehead area) had the lowest mean precision. CONCLUSIONS: Alignment procedures influenced on the scanning trueness and precision mean values, but the facial scanner accuracy values obtained were within the clinically acceptable accuracy threshold of less or equal than 2 mm. Furthermore, the scanning accuracy (for both trueness and precision) depended on the location of the scanned surface area, being more accurate on the middle of the face than on the sides of the face.

Revilla-León, M., Gómez-Polo, M., Vyas, S., Barmak, B.A., Özcan, M., Att, W. and Krishnamurthy, V.R. (2021). “Artificial intelligence applications in restorative dentistry: A systematic review.” J Prosthet Dent Apr 9;S0022-3913(21)00087-1. [Epub ahead of print].

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STATEMENT OF PROBLEM: Artificial intelligence (AI) applications are increasing in restorative procedures. However, the current development and performance of AI in restorative dentistry applications has not yet been systematically documented and analyzed. PURPOSE: The purpose of this systematic review was to identify and evaluate the ability of AI models in restorative dentistry to diagnose dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure. MATERIAL AND METHODS: An electronic systematic review was performed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with AI models were selected based on 4 criteria: diagnosis of dental caries, diagnosis of vertical tooth fracture, detection of the tooth preparation finishing line, and prediction of restoration failure. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS: A total of 34 articles were included in the review: 29 studies included AI techniques for the diagnosis of dental caries or the elaboration of caries and postsensitivity prediction models, 2 for the diagnosis of vertical tooth fracture, 1 for the tooth preparation finishing line location, and 2 for the prediction of the restoration failure. Among the studies reviewed, the AI models tested obtained a caries diagnosis accuracy ranging from 76% to 88.3%, sensitivity ranging from 73% to 90%, and specificity ranging from 61.5% to 93%. The caries prediction accuracy among the studies ranged from 83.6% to 97.1%. The studies reported an accuracy for the vertical tooth fracture diagnosis ranging from 88.3% to 95.7%. The article using AI models to locate the finishing line reported an accuracy ranging from 90.6% to 97.4%. CONCLUSIONS: AI models have the potential to provide a powerful tool for assisting in the diagnosis of caries and vertical tooth fracture, detecting the tooth preparation margin, and predicting restoration failure. However, the dental applications of AI models are still in development. Further studies are required to assess the clinical performance of AI models in restorative dentistry.

Pérez-Giugovaz, M.G., Park, S.H. and Revilla-León, M. (2021). “3D Virtual Patient Representation for Guiding a Maxillary Overdenture Fabrication: A Dental Technique.” J Prosthodont.

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This report describes a technique to obtain a 3D virtual representation of a maxillary edentulous patient guided by an additively manufactured intraoral scan body. The intraoral scan body incorporated a custom tray and occlusion rim which facilitated the acquiring of a digital definitive cast, maxillary occlusion rim position, interocclusal registration, and guided the integration of the facial scans. The technique simplified the design and manufacturing of the maxillary overdenture.

Pérez-Giugovaz, M.G., Mostafavi, D. and Revilla-León, M. (2021). “Additively manufactured scan body for transferring a virtual 3-dimensional representation to a digital articulator for completely edentulous patients.” J Prosthet Dent Apr 29;S0022-3913(21)00159-1. [Epub ahead of print].

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A technique is described for obtaining a virtual 3-dimensional representation of completely edentulous patients with the virtual definitive casts mounted on the virtual articulator. An additively manufactured intraoral scan body was developed to record the definitive maxillary and mandibular casts and gothic arch interocclusal registration. The intraoral scan body guided the integration of the digital definitive casts and facial scans to obtain the virtual 3-dimensional patient’s representation and facilitated the transfer of the definitive casts to the virtual articulator