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Falk Schwendicke, Akhilanand Chaurasia, Lubaina T. Arsiwala-Scheppach, Jae‐Hong Lee, Karim Elhennawy, Paul‐Georg Jost‐Brinkmann, Flávio Fernando Demarco, Joachim Krois
OBJECTIVES: Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. METHODS: Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498). DATA: From 321 identified records, 19 studies (published 2017-2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7-93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (-0.581; 95 CI: -1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824). CONCLUSIONS: DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed. CLINICAL SIGNIFICANCE: 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.
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Schwendicke, F., Chaurasia, A., Arsiwala-Scheppach, L. T., Lee, J., Elhennawy, K., Jost‐Brinkmann, P., Demarco, F. F., & Krois, J. (2021). Deep learning for cephalometric landmark detection: systematic review and meta-analysis. Clinical Oral Investigations, 25(7), 4299–4309. https://doi.org/10.1007/s00784-021-03990-w
Schwendicke, Falk, et al. "Deep learning for cephalometric landmark detection: systematic review and meta-analysis." Clinical Oral Investigations, vol. 25, no. 7, 2021, pp. 4299–4309, https://doi.org/10.1007/s00784-021-03990-w.
Schwendicke, Falk, Akhilanand Chaurasia, Lubaina T. Arsiwala-Scheppach, Jae‐Hong Lee, Karim Elhennawy, Paul‐Georg Jost‐Brinkmann, Flávio Fernando Demarco, and Joachim Krois. "Deep learning for cephalometric landmark detection: systematic review and meta-analysis." Clinical Oral Investigations 25, no. 7 (2021): 4299–4309. https://doi.org/10.1007/s00784-021-03990-w.
Schwendicke F., Chaurasia A., Arsiwala-Scheppach L. T. [и др.] Deep learning for cephalometric landmark detection: systematic review and meta-analysis // Clinical Oral Investigations. — 2021. — Vol. 25, № 7. — P. 4299–4309. — URL: https://doi.org/10.1007/s00784-021-03990-w
@article{schwendicke2021deep,
title = {{Deep learning for cephalometric landmark detection: systematic review and meta-analysis}},
author = {Falk Schwendicke and Akhilanand Chaurasia and Lubaina T. Arsiwala-Scheppach and Jae‐Hong Lee and Karim Elhennawy and Paul‐Georg Jost‐Brinkmann and Flávio Fernando Demarco and Joachim Krois},
journal = {Clinical Oral Investigations},
year = {2021},
volume = {25},
number = {7},
pages = {4299--4309},
doi = {10.1007/s00784-021-03990-w},
url = {https://doi.org/10.1007/s00784-021-03990-w}
}TY - JOUR AU - Schwendicke, Falk AU - Chaurasia, Akhilanand AU - Arsiwala-Scheppach, Lubaina T. AU - Lee, Jae‐Hong AU - Elhennawy, Karim AU - Jost‐Brinkmann, Paul‐Georg AU - Demarco, Flávio Fernando AU - Krois, Joachim TI - Deep learning for cephalometric landmark detection: systematic review and meta-analysis JO - Clinical Oral Investigations VL - 25 IS - 7 SP - 4299 EP - 4309 PY - 2021 DA - 2021/05/27 DO - 10.1007/s00784-021-03990-w UR - https://doi.org/10.1007/s00784-021-03990-w ER -
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