Deep learning for cephalometric landmark detection: systematic review and meta-analysis.
Falk SchwendickeAkhilanand ChaurasiaLubaina ArsiwalaJae-Hong LeeKarim ElhennawyPaul-Georg Jost-BrinkmannFlavio DemarcoJoachim KroisPublished in: Clinical oral investigations (2021)
Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective.