ArticleInternational journal of implant dentistry2025
AI-assisted radiographic identification of original vs. replica dental implants: comparing accuracy of human experts vs. probabilistic and deterministic AI.
Article in International journal of implant dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIn dental implantology, the application of artificial intelligence (AI) for the differentiation of various implant systems is gaining increasing importance. This study investigates the feasibility of distinguishing between two highly similar implant (original implant and its replica) systems using an automated, AI-based recognition software.
methodsA dataset of 906 radiographic images was initially compiled, consisting of standardized ex situ recordings of both the original and the replica implants (with and without a cover screw in situ). Four deterministic AI-models and one probabilistic model were trained using different subsets of varying sizes of the dataset, including the full dataset and then evaluated against a designated test dataset. For comparison, 28 dental professionals also assessed the same test dataset.
resultsThe accuracy of the deterministic model trained solely with 488 radiographs of implants with inserted cover screws was 0.579 (57.9%). The second and third models, trained with a greater number of radiographs without inserted cover screws, achieved accuracies exceeding 0.90 and, in some instances, even reached 1.00. The fourth deterministic model, as well as the probabilistic model, comprising 28 classifiers and trained on the complete dataset, classified the test dataset without error. The dental professionals achieved an overall accuracy of 0.8616 (86.16%) in their assessment of the test dataset.
conclusionThis study suggests that AI-supported implant recognition software has the potential to offer valuable assistance in clinical practice for distinguishing between original and replica implants. Such differentiation can play a crucial role for prosthetic suprastructures and associated manufacturer warranties.
Indexed as
Identifiers
What OpenQuestion holds
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.