Segmentation of Pulp and Pulp Stones with Automatic Deep Learning in Panoramic Radiographs: An Artificial Intelligence Study.

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

5 citing papers in PubMed.

1. Artificial intelligence in the detection of dental pulp calcifications: a scoping review.BMC oral health · 2026
   Article
2. Performance Validation of ORTHOSEG, a Novel Artificial Intelligence Tool for the Segmentation of Orthopantomographs and Intra-Oral X-Rays.Clinics and practice · 2026
   Article
3. Comparison of Conventional Root Tip Resection with Digitally Guided Resection-An In Vitro Study.Dentistry journal · 2025
   Article
4. Comparative evaluation of CNN architectures for detection of dental pulp calcifications on CBCT slices.Science progress
   Article
5. Transfer learning models in the detection of pulp calcifications- A preliminary study.Journal of oral biology and craniofacial research
   Article

Corrections and comments

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Authors and funding

6 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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