Artificial Intelligence and Machine Learning for Automated Cephalometric Landmark Identification: A Meta-Analysis Previewed by a Systematic Review

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 comp...

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Published inCurēus (Palo Alto, CA) Vol. 15; no. 6; p. e40934
Main Authors Rauniyar, Sabita, Jena, Sanghamitra, Sahoo, Nivedita, Mohanty, Pritam, Dash, Bhagabati P
Format Journal Article
LanguageEnglish
Published United States Cureus Inc 25.06.2023
Cureus
Subjects
Accuracy
Algorithms
Artificial intelligence
Automation
Deep learning
Dentistry
Healthcare Technology
Machine learning
Meta-analysis
Orthodontics
Radiology
Systematic review
X-rays
South Korea
United States > US
Taiwan
Spain
manual tracing
automated cephalometric landmark
cellular neural network
machine learning
artificial intelligence
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Summary: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.
Bibliography:ObjectType-Article-2
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ObjectType-Review-1
ISSN:2168-8184
2168-8184
DOI:10.7759/cureus.40934