dc.contributor.author
Schwendicke, Falk
dc.contributor.author
Chaurasia, Akhilanand
dc.contributor.author
Arsiwala, Lubaina
dc.contributor.author
Lee, Jae-Hong
dc.contributor.author
Elhennawy, Karim
dc.contributor.author
Jost-Brinkmann, Paul-Georg
dc.contributor.author
Demarco, Flavio
dc.contributor.author
Krois, Joachim
dc.date.accessioned
2023-08-09T10:59:41Z
dc.date.available
2023-08-09T10:59:41Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/40385
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-40106
dc.description.abstract
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.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Artificial intelligence
en
dc.subject
Convolutional neural networks
en
dc.subject
Evidence-based medicine
en
dc.subject
Meta-analysis
en
dc.subject
Orthodontics
en
dc.subject
Systematic review
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Deep learning for cephalometric landmark detection: systematic review and meta-analysis
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.doi
10.1007/s00784-021-03990-w
dcterms.bibliographicCitation.journaltitle
Clinical Oral Investigations
dcterms.bibliographicCitation.number
7
dcterms.bibliographicCitation.originalpublishername
Springer Nature
dcterms.bibliographicCitation.pagestart
4299
dcterms.bibliographicCitation.pageend
4309
dcterms.bibliographicCitation.volume
25
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.funding
Springer Nature DEAL
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
34046742
dcterms.isPartOf.issn
1432-6981
dcterms.isPartOf.eissn
1436-3771