ArticleJournal of dental sciences2026
Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs.
Article in Journal of dental sciences, 2026. 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.
- Detection and Classification of Peri-Implant Marginal Bone Loss in Cone-Beam Computed Tomography Using a Deep Learning Approach.Clinical and experimental dental research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/purpose: Dental implant surgery had become a standard treatment option for oral rehabilitation. The severity of peri-implant bone loss was a critical clinical indicator for evaluating the success of dental implants. This study assessed an automated framework combining deep learning and image processing techniques for classifying the severity of peri-implant bone loss using periapical radiographs and providing diagnostic assistance. Materials and methods: A total of 780 periapical radiographs containing 1210 implants were analyzed. A YOLO-based object detection model was employed to localize peri-implant regions accurately. Subsequently, we applied our custom-developed peri-implant image processing pipeline and alveolar crest localization algorithm to categorize each implant into one of three severity levels. The clinical feasibility of the proposed framework was also evaluated. Results: On a test dataset of 120 periapical radiographs, the YOLOv8-S model achieved a detection precision of 98.1 %, with a sensitivity of 96.0 % and a specificity of 99.1 %. For the three-grade classification of peri-implant bone loss severity, the highest accuracy reached 96.61 %, with an overall classification accuracy of 95.8 %. In clinical feasibility testing, our framework demonstrated a 36-fold improvement in assessment speed and approximately a 7.5 % increase in diagnostic accuracy compared to manual evaluation by dental experts. Conclusion: The proposed automated framework for assessing peri-implant bone loss severity in periapical radiographs shows strong potential to assist clinical dental practices. It is a reliable second opinion to support clinical decision-making and improve diagnostic efficiency.
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.