Available online 21 December 2023, 101965

Electronic search used Embase, IEEE Xplore, LILACS, MedLine (via PubMed), SciELO, Scopus, Web of Science databases, as well as OpenGrey and ProQuest. The search included studies published till November 2021 in any language. Studies written in languages other than English or Portuguese were translated. After removing duplicates, the selection of the studies proceeded by two reviewers independently. Disagreements were resolved with the help of a third reviewer. A reviewer was responsible for the data extraction from the selected studies and a second reviewer did a cross-examination to test the agreement. The risk of individual bias in the eligible studies was assessed independently by two of the authors using QUADAS-2 which includes four domains: patient selection, index test, reference standard, and flow and timing; each of the four domains can be judged as "high risk", “uncertain risk,” or “low risk”. The reviewers resolved the conflict by discussion or by resorting to a third reviewer if the matter is not settled between them.

The key study factor was the identification of cephalometric landmarks' from digital images (2D and 3D) by (AI) applications (deep learning and handcrafted) compared to manual identification by experts which is the standard for cephalometric landmarks identification.

Three main outcome measures were investigated; the agreement (%) of the automatic (AI) and the manual cephalometric landmark identification (2mm and 3mm margin of error) and the divergence (mm) between the identification of the landmarks by the automatic (AI) and the manual methods.

Forty studies were included in the qualitative analysis. Only three studies were evaluated as having a low risk of bias for all domains. Most studies presented a high risk of bias for patient selection and reference tests [32/40 (80%) and 26/40(65%) respectively]. Twenty-nine studies were included in the meta-analyses.

Random-effects meta-analyses, considering a 2mm margin of error, the meta-analysis showed agreement=79% (95% CI:76–82%) and high heterogeneity (I2=99%). Considering a 3mm margin

The evidence that supports the application of (AI) for automatic detection of cephalometric landmarks is of a very low level of certainty. However, because of the promising prospect of the application further standardized studies are needed. A limitation that needs consideration is the lack of a gold standard to identify cephalometric landmarks.

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