ArticleBMC oral health2026
AI-driven automation of occlusal surface design in digital prosthodontics: a clinically validated workflow.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Mapping the Influence of Artificial Intelligence in Prosthodontics: A Scoping Review of the Current Trends and Challenges.Dentistry journal · 2026Review
- Accuracy and Functional Performance of Artificial Intelligence-Based Automated Crown Design Systems: A Systematic Review and Meta-Analysis.Biomedical engineering and computational biology · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundAutomation and artificial intelligence (AI) are increasingly applied to digital dentistry, aiming to improve efficiency and accuracy in restorative and surgical workflows. The precise design of mandibular first molar occlusal surfaces remain a time-consuming manual task that limits scalability. This study aimed to develop and validate a fully automated AI framework for individualized occlusal surface reconstruction from intraoral scans, supporting the integration of automation into prosthodontic procedures.
methodsIntraoral scans from 110 patients were partitioned into three cohorts: a training set (n = 90), a validation set (n = 10), and a testing set (n = 10). Occlusal surfaces of maxillary premolars and molars were encoded into a 30-dimensional latent space via a Dimension Elevator Module. An attention-guided fusion network integrated multi-scale geometric features to reconstruct mandibular first molar occlusal surfaces. Performance was assessed using point-to-point error, Dice similarity coefficient (DSC), and surface normal cosine similarity. Clinical validation was conducted through structured evaluation by ten dental professionals using a five-point Likert scale.
resultsThe framework achieved a mean point-to-point error of 0.66 mm, a DSC of 86.9%, and a surface normal cosine similarity exceeding 87%. Expert evaluation yielded mean scores above 4.0 across all criteria, with favorable ratings in occlusal accuracy (4.08 ± 0.78), clinical feasibility (4.09 ± 0.78), and overall acceptability (4.10 ± 0.78).
conclusionsThe proposed AI-driven workflow enables automated and clinically validated occlusal surface design, demonstrating both quantitative accuracy and expert endorsement. By streamlining digital prosthodontic workflows, this approach aligns with the broader trend of robotics and automation in dental surgery, offering potential to enhance surgical precision, reduce manual workload, and improve patient outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.