Deep learning for cephalometric landmark detection: systematic review and meta-analysis

• Published in: Clinical oral investigations Vol. 25; no. 7; pp. 4299 - 4309
• Main Authors: Schwendicke, Falk, Chaurasia, Akhilanand, Arsiwala, Lubaina, Lee, Jae-Hong, Elhennawy, Karim, Jost-Brinkmann, Paul-Georg, Demarco, Flavio, Krois, Joachim
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-07-2021; Springer Nature B.V
• Subjects: Accuracy; Automation; Cephalometry; Deep Learning; Dentistry; Medicine; Meta-analysis; Neural networks; Orthodontics; Radiography; Reproducibility of Results; Review; Reviews; Systematic review; Systematic review; Convolutional neural networks; Artificial intelligence; Evidence-based medicine; Orthodontics; Meta-analysis
• ISSN: 1432-6981; 1436-3771; 1436-3771
• DOI: 10.1007/s00784-021-03990-w

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.