Evidence map›Paper›PMID 37869886›Full record

ArticleDento maxillo facial radiology2023

Assessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using the an artificial intelligence software.

Liciane Dos Santos Menezes, Thaísa Pinheiro Silva, Marcos Antônio Lima Dos Santos, Mariana Mendonça Hughes, Saulo Dos Reis Mariano Souza, Patrícia Miranda Leite Ribeiro, Paulo Henrique Luiz de Freitas, Wilton Mitsunari Takeshita

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Article in Dento maxillo facial radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

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6citing papers in PubMed, 1 pooled it
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1 · What the graph read from 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.

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Liciane Dos Santos MenezesDepartment of Oral Diagnosis, Federal University of Bahia, Bahia, Brazil.
Thaísa Pinheiro SilvaDepartment of Oral Diagnosis, Piracicaba Dental School, University of Campinas, São Paulo, Brazil.ORCID 0000-0002-7485-0206
Marcos Antônio Lima Dos SantosDepartment of Oral Diagnosis, University of São Paulo, São Paulo, Brazil.
Mariana Mendonça HughesDepartment of Dentistry, Federal University of Sergipe, Sergipe, Brazil.
Saulo Dos Reis Mariano SouzaDepartment of Dentistry, Federal University of Sergipe, Sergipe, Brazil.
Patrícia Miranda Leite RibeiroDepartment of Oral Diagnosis, Federal University of Bahia, Bahia, Brazil.
Paulo Henrique Luiz de FreitasDepartment of Dentistry, Federal University of Sergipe, Sergipe, Brazil.
Wilton Mitsunari TakeshitaDiagnosis and Surgery, São Paulo State University (Unesp), School of Dentistry, Araçatuba, São Paulo, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the reliability and reproducibility of an artificial intelligence (AI) software in identifying cephalometric points on lateral cephalometric radiographs considering four settings of brightness and contrast. METHODS AND MATERIALS: Brightness and contrast of 30 lateral cephalometric radiographs were adjusted into four different settings. Then, the control examiner (ECont), the calibrated examiner (ECal), and the CEFBOT AI software (AIs) each marked 19 cephalometric points on all radiographs. Reliability was assessed with a second analysis of the radiographs 15 days after the first one. Statistical significance was set at

resultsReliability of landmark identification was excellent for the human examiners and the AIs regardless of the type of brightness and contrast setting (mean intraclass correlation coefficient >0.89). When ECont and ECal were compared for reproducibility, there were more cephalometric points with significant differences on the x-axis of the image with the highest contrast and the lowest brightness, namely N(

conclusionsWhile the reliability of the AIs for cephalometric landmark identification was rated as excellent, low brightness and high contrast seemed to affect its reproducibility. The experienced human examiner, on the other hand, did not show such faulty reproducibility; therefore, the AIs used in this study is an excellent auxiliary tool for cephalometric analysis, but still depends on human supervision to be clinically reliable.

Indexed as

Artificial IntelligenceSoftwareCephalometryHumansRadiographyReproducibility of ResultsArtificial intelligenceCephalometryDental radiographyMachine learningRadiologyReproducibility of results

Identifiers

PMID37869886
PMCPMC10968755

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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.