# name
The accuracy of algorithms used by artificial intelligence in cephalometric points detection: a systematic review

# metadata.dc.description.abstract[].value
Our findings suggest that CNNs represent the most promising AI form for detecting cephalometric landmarks in 2D lateral cranial teleradiography, offering lower error rates and higher reproducibility compared to other AI types reviewed. However, due to significant heterogeneity in study designs, data collection, and performance metrics, a definitive quantitative comparison was not feasible. While AI demonstrates faster and more reproducible results than manual tracing, no algorithms currently match the precision of human professionals. Future research should aim to standardize evaluation criteria and datasets to enable a more robust comparison of AI methods.

# metadata.dc.rights[].value
cc-by (c)  Ribas-Sabartés, J. et al., 2024

# metadata.dc.title[].value
The accuracy of algorithms used by artificial intelligence in cephalometric points detection: a systematic review