ArticleBMC oral health2026
Predicting fracture risk in Y-TZP zirconia implants using radiomics-based artificial intelligence.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
statement of problemZirconia implants offer acceptable osseointegration in pre-clinical studies and may present clinical advantages in aesthetics, plaque affinity and biocompatibility; however, their fracture resistance remains debated. PURPOSE: This study aims to evaluate the predictability of fractures in zirconia implants—specifically those resulting from structural changes due to prolonged functional loading—through the application of a machine learning (ML) algorithm developed using radiomic features. MATERIAL AND
methodsMicro-computed tomography (micro-CT) images with a resolution of 10.1 μm were acquired from 48 zirconia implants subjected to chewing simulation and 25 surviving implants. Following image post-processing, a comprehensive set of radiomic features was extracted using a radiomics platform designed to detect fracture-related changes. Nine machine learning algorithms were implemented and evaluated for their diagnostic performance.
resultsThe area under the curve (AUC) values for the training datasets ranged from 0.950 to 1.000, while AUC values for the test datasets ranged from 0.336 to 0.722. Among the algorithms tested, the Random Forest (RF) model demonstrated the highest diagnostic accuracy for detecting zirconia implant fractures from micro-CT imaging.
conclusionsIn an ex-vivo chewing-simulation model, a Random-Forest classifier (AUC = 0.72) modestly predicted fracture-prone zirconia implants from micro-CT radiomics. After prospective CBCT validation, such models might assist but not replace clinical decision-making. CLINICAL IMPLICATIONS: The radiomic features and machine learning algorithm developed in this study, particularly when adapted to cone beam computed tomography (CBCT) imaging, may serve as a valuable tool in clinical practice for predicting implant outcomes and guiding decision-making regarding long-term implant maintenance.
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.