ReviewCureus2023
Artificial Intelligence and Machine Learning for Automated Cephalometric Landmark Identification: A Meta-Analysis Previewed by a Systematic Review.
Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Parameter-level comparison of artificial intelligence and manual cephalometric measurements: a systematic review and meta-analysis.Frontiers in dental medicine · 2026Pooled it
- Evaluation of artificial intelligence-based cephalometric analysis compared to the manual method.Progress in orthodontics · 2026Article
- Reliability of Automated Cephalometric Analysis: A Comparative Assessment of Stratification Strategies Based on Chronological Age Versus Dentition Stage.Dentistry journal · 2026Article
- Evaluation of cephalometric landmarks using artificial intelligence compared to the manual method.Head & face medicine · 2026Article
- Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.Scientific reports · 2025Article
- Article
- Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics.Journal of clinical medicine · 2024Article
- AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.Journal of clinical medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital dentistry has become an integral part of our practice today, with artificial intelligence (AI) playing the predominant role. The present systematic review was intended to detect the accuracy of landmarks identified cephalometrically using machine learning and artificial intelligence and compare the same with the manual tracing (MT) group. According to the PRISMA-DTA guidelines, a scoping evaluation of the articles was performed. Electronic databases like Doaj, PubMed, Scopus, Google Scholar, and Embase from January 2001 to November 2022 were searched. Inclusion and exclusion criteria were applied, and 13 articles were studied in detail. Six full-text articles were further excluded (three articles did not provide a comparison between manual tracing and AI for cephalometric landmark detection, and three full-text articles were systematic reviews and meta-analyses). Finally, seven articles were found appropriate to be included in this review. The outcome of this systematic review has led to the conclusion that AI, when employed for cephalometric landmark detection, has shown extremely positive and promising results as compared to manual tracing.
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.