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Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis

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Abstract

Using computer vision through artificial intelligence (AI) is one of the main technological advances in dentistry. However, the existing literature on the practical application of AI for detecting cephalometric landmarks of orthodontic interest in digital images is heterogeneous, and there is no consensus regarding accuracy and precision. Thus, this review evaluated the use of artificial intelligence for detecting cephalometric landmarks in digital imaging examinations and compared it to manual annotation of landmarks. An electronic search was performed in nine databases to find studies that analyzed the detection of cephalometric landmarks in digital imaging examinations with AI and manual landmarking. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using QUADAS-2. Random-effects meta-analyses determined the agreement and precision of AI compared to manual detection at a 95% confidence interval. The electronic search located 7410 studies, of which 40 were included. Only three studies presented a low risk of bias for all domains evaluated. The meta-analysis showed AI agreement rates of 79% (95% CI: 76–82%, I2 = 99%) and 90% (95% CI: 87–92%, I2 = 99%) for the thresholds of 2 and 3 mm, respectively, with a mean divergence of 2.05 (95% CI: 1.41–2.69, I2 = 10%) compared to manual landmarking. The menton cephalometric landmark showed the lowest divergence between both methods (SMD, 1.17; 95% CI, 0.82; 1.53; I2 = 0%). Based on very low certainty of evidence, the application of AI was promising for automatically detecting cephalometric landmarks, but further studies should focus on testing its strength and validity in different samples.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We are thankful for the support of Conselho Nacional de Desenvolvimento Científico e Tecnológico—Brazil (CNPq) and of Fundação de Amparo à Pesquisa do Estado de Minas Gerais – Brazil (FAPEMIG).

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001.

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Authors and Affiliations

  1. Postgraduate Program in Dentistry, School of Dentistry, São Leopoldo Mandic, Campinas, São Paulo, Brazil

    Germana de Queiroz Tavares Borges Mesquita & Rui Barbosa de Brito Júnior

  2. Department of Restorative Dentistry, Endodontics Division, School of Dentistry of Piracicaba, State University of Campinas, Piracicaba, São Paulo, Brazil

    Walbert A. Vieira

  3. School of Dentistry, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil

    Maria Tereza Campos Vidigal

  4. School of Computing, Federal University of Uberlândia, Uberlândia, Brazil

    Bruno Augusto Nassif Travençolo

  5. Department of Preventive and Community Dentistry, School of Dentistry, Federal University of Uberlândia, Campus Umuarama Av. Pará, 1720, Bloco 2G, sala 1, 38405-320, Uberlândia, Minas Gerais, Brazil

    Thiago Leite Beaini & Luiz Renato Paranhos

  6. Department of Dentistry and Oral Health, Section for Oral Radiology, Aarhus University, Aarhus C, Denmark

    Rubens Spin-Neto

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Contributions

GQTBM: Conception and design, acquisition of data, analysis and interpretation of data, drafting the manuscript, final approval. WAV: Acquisition of data, analysis and interpretation of data, drafting the manuscript, final approval. MTCV: Acquisition of data, drafting the manuscript, final approval. BANT: Drafting the manuscript, final approval. TLB: Drafting the manuscript, final approval. RSN: Drafting the manuscript, final approval. LRP: Drafting the manuscript, final approval. RBBJ: Conception and design, drafting the manuscript, final approval.

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Correspondence to Luiz Renato Paranhos.

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de Queiroz Tavares Borges Mesquita, G., Vieira, W.A., Vidigal, M.T.C. et al. Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis. J Digit Imaging 36, 1158–1179 (2023). https://doi.org/10.1007/s10278-022-00766-w

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