Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis.
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 con...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 1158 - 1180 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164473094&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473094 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473094 161152664 164473094 164473094 10.1007/s10278-022-00766-w 164473094 ppf: 1158 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis. aug: au: de Queiroz Tavares Borges Mesquita, Germana Vieira, Walbert A. Vidigal, Maria Tereza Campos Travençolo, Bruno Augusto Nassif Beaini, Thiago Leite Spin-Neto, Rubens Paranhos, Luiz Renato de Brito Júnior, Rui Barbosa affil: Postgraduate Program in Dentistry, School of Dentistry, São Leopoldo Mandic, Campinas, São Paulo, Brazil sug: subj: Cephalometry Evaluation Artificial Intelligence Utilization Human Systematic Review Meta Analysis Embase Medline PubMed Gray Literature Dentistry Deep Learning Data Analysis Software Quality Assessment Confidence Intervals Descriptive Statistics Funding Source ab: 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. pubtype: Academic Journal doctype: meta analysis pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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