Segmentation-Based Multi-Class Detection and Radiographic Charting of Periodontal and Restorative Conditions on Bitewing Radiographs Using Deep Learning.

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

3 citing papers in PubMed.

1. Deep Learning for the Assessment of Alveolar Bone Loss on Intraoral Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026
   Review
2. Deep Learning-Based Detection of Overhanging Restorations and Calculation of Radiographic Alveolar Bone Loss Percentage with YOLOv11 and YOLOv12.Journal of imaging informatics in medicine · 2026
   Article
3. Comparative performance of one-stage and two-stage deep learning models for instance segmentation of overhanging dental restorations on bitewing radiographs.Scientific reports · 2026
   Article

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

PubMed holds no abstract for this paper.

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