SynthesisAustralian endodontic journal : the journal of the Australian Society of Endodontology Inc2026
Artificial Intelligence for Working Length Determination in Endodontics: A Systematic Review and Meta-Analysis.
Synthesis in Australian endodontic journal : the journal of the Australian Society of Endodontology Inc, 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
This systematic review and meta-analysis evaluated the comparative performance of artificial intelligence (AI) models versus comparators for working-length determination using radiographic or impedance-based inputs. A comprehensive search across seven electronic databases was conducted up to 1 October 2025, identifying five eligible in vitro and ex vivo studies encompassing over 1765 teeth or radiographic images. All the included studies directly compared AI-based approaches with manual or conventional reference standards. An exploratory random-effects meta-analysis demonstrated higher odds of correct working length determination using AI-based methods compared to expert assessment. The risk of bias was moderate to high, primarily because of internal validation and the predominance of laboratory-based study designs. The overall certainty of evidence for the primary outcome was rated low. This first quantitative synthesis suggests that AI-based methods may enhance the consistency of working-length determination under controlled conditions; however, further well-designed clinical studies are required before routine clinical implementation.
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.