ReviewCureus2026
Artificial Intelligence for Cone-Beam Computed Tomography in Endodontics: A PRISMA-Aligned Narrative Synthesis of Evidence From the United States and Europe (2021-2026).
Review in Cureus, 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
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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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cone-beam computed tomography (CBCT) is the reference three-dimensional imaging modality for endodontic diagnosis and treatment planning, but its interpretation is time-consuming and varies between observers. Artificial intelligence (AI) and convolutional neural networks in particular have been applied to CBCT to automate root canal segmentation, periapical lesion detection, and morphologic classification. Currently, there is no focused synthesis of evidence from the United States and Europe. This review is a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-aligned narrative synthesis. PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and IEEE Xplore were searched for primary studies published between January 1, 2021, and April 30, 2026. Studies were included if they used AI with CBCT for endodontic applications and had a first or corresponding author from an institution in the United States or Europe, and quality was appraised against the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Quality Assessment of Diagnostic Accuracy Studies tailored to Artificial Intelligence (QUADAS-AI). Of 612 records screened, 17 studies were included, originating mainly from four research groups (the University of Pennsylvania, KU Leuven, the Medical University of Graz, and Stony Brook University), with two further single-study contributions. Tooth, pulp, and canal segmentation was the most mature task (Dice 0.85-0.97), and periapical lesion detection reached early clinical validation (sensitivity 0.80-0.97; specificity 0.84-1.00). No eligible studies from the United States or Europe were found on vertical root fracture detection, AI-based working length determination, American Association of Endodontists case difficulty assessment, or treatment outcome prediction. CBCT-based endodontic AI in these regions is advancing unevenly: segmentation and lesion detection are approaching clinical use, while several clinically important tasks remain unaddressed and external validation across devices is still limited.
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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.