Purpose: To assess the accuracy of virtual articulation in direct digital workflow (DDW) and indirect digital workflow (IDW) in arches prepared for fixed partial dentures (FPDs).
Materials And Methods: Five pairs of master models were used in this study representing different clinical scenarios of full dentate (FD), and prepared arches for fixed partial dentures as follows: FD group, short span posterior (SSP group), long span posterior (LSP group), short span anterior (SSA group), and long span anterior (LSA). Fourteen pairs of interarch reference points were added to each set of master models to measure linear interarch distance with a caliper (reference measurements). The direct digital workflow included digital scans and virtual articulation with buccal scan images using an intraoral scanner. The indirect digital workflow included conventional polyvinylsiloxane impressions and bites followed by pouring, mounting, and scanning the stone models in a laboratory scanner. The scanned stone models were virtually articulated with buccal scanning in the laboratory scanner. Digital linear interarch measurements on all virtually-articulated models were compared with reference measurements. The absolute mean differences in linear interarch distances were calculated. The Mann-Whitney test was used for statistical analysis (α = .05).
Results: The direct digital workflow produced significantly less linear interarch deviations in the virtually articulated models compared to the indirect digital workflow for all study groups (P < .05). However, the direct digital workflow had significantly less accuracy for virtual articulation in long span posterior, long span anterior, and short span anterior groups compared to the full dentate group. CONCLUSIONS: Both workflows produced virtually-articulated models with acceptable accuracy. However, the direct digital workflow had significantly better accuracy in all assessed clinical scenarios.
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http://dx.doi.org/10.1111/jopr.13877 | DOI Listing |
Hua Xi Kou Qiang Yi Xue Za Zhi
February 2025
Center of Stomatology, Peking University Shenzhen Hospital, Shenzhen 518036, China.
Objectives: This study aims to explore the effect of improving clinical efficiency by replacing traditional impression workflow with centralized digital impression workflow.
Methods: The department of prosthodontics in Center of Stomatology, Peking University Shenzhen Hospital has improved the clinical workflow by replacing the traditional impression made by doctors using impression materials for each patient with a centralized digital impression made by one technician for all patients in the department. This cross-sectional study recorded the chairside time required for impression taking in patients undergoing single posterior zirconia full crown restoration before clinical process improvement; the time required for centralized digital impression production; the comfort level of patients; and the adjacency relationship, occlusal contact relationship, and time required for prostheses adjusting (i.
J Prosthodont
January 2025
Department of Prosthodontics, Jordan University of Science & Technology, Irbid, Jordan.
Purpose: To investigate the feasibility and accuracy (trueness and precision) of facial scanning and virtual patient representation (VPR).
Materials And Methods: One participant was recruited and informed consent was obtained. VPR was performed 30 times with a custom fabricated intraoral scan body (ISB).
Oral Oncol
January 2025
Department of Pathology, School of Basic Medical Science, Hubei University of Medicine, Shiyan 442000, Hubei, China. Electronic address:
Clin Oncol (R Coll Radiol)
December 2024
Department of Pathology and Laboratory Medicine, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226, USA; Department of Pathology, Yale School of Medicine, 20 York Street, Ste East Pavilion 2-631, New Haven, CT 06510, USA. Electronic address:
Aims: The recent widespread use of electronic health records (EHRs) has opened the possibility for innumerable artificial intelligence (AI) tools to aid in genomics, phenomics, and other research, as well as disease prevention, diagnosis, and therapy. Unfortunately, much of the data contained in EHRs are not optimally structured for even the most sophisticated AI approaches. There are very few published efforts investigating methods for recording discrete data in EHRs that would not slow current clinical workflows or ways to prioritise patient characteristics worth recording.
View Article and Find Full Text PDFArch Pathol Lab Med
January 2025
the Department of Pathology, The Ohio State University, Columbus (Parwani).
Context.—: Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities.
Objective.
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