Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis

Mesquita, Germana de Queiroz Tavares Borges; Vieira, Walbert de Andrade; Vidigal, Maria Tereza Campos; Travençolo, Bruno Augusto Nassif; Beaini, Thiago Leite; Spin‐Neto, Rubens; Paranhos, Luiz Renato; Brito, Rui Barbosa de

doi:10.1007/s10278-022-00766-w

Cited by 17 publications

(13 citation statements)

References 72 publications

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“…One aspect of the current review is the exclusion of diagnostic tools like conventional 2D radiographs, as previously purposed by a recent review [ 52 ], in favor of 3D imaging methods. CBCT and CT technologies can surely solve main problems related to bidimensional image analysis: loss of third dimension that results in anatomical structures overlapping, image distortion and non-real measurements quantifications [ 53 , 54 ].…”

Section: Discussionmentioning

confidence: 99%

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

et al. 2023

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Objectives The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images. Methods PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication. Results The initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I2 = 98.13%, τ2 = 1.018, p-value < 0.001); risk of bias was high due to the presence of issues for several domains per study. Meta-regression indicated a significant relation between mean error and year of publication (p value = 0.012). Conclusion Deep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done.

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Section: Discussionmentioning

confidence: 99%

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

et al. 2023

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“…Conversely. A recent systematic review and meta-analysis reported AI agreement rates of 79% and 90% for the thresholds of 2 and 3 mm, respectively, with a mean divergence of 2.05 compared to manual landmarking 19 . Another study showed that most studies did not exceed a 2-mm prediction error threshold in mean and that the mean proportion of landmarks detected within this 2-mm threshold was 80% 20 .…”

Section: Discussionmentioning

confidence: 99%

“…These studies have reported that deep learning algorithms exhibit high accuracy in detecting landmarks at shorter time, achieving precision levels within the range of 2.0 mm. Additionally, they have achieved successful detection rates (SDR), surpassing 70% and 90% for the respective thresholds of 2 and 3 mm 4 , 16 , 19 – 21 . However, most studies reporting the development and evaluation of automated cephalometric identification algorithms primarily focused on utilizing lateral cephalograms as their main target.…”

Section: Introductionmentioning

confidence: 99%

Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts

Lee,

Cho,

Ryu

et al. 2023

Sci Rep

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This study aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and compare its accuracy and reliability with those of expert human examiners. In total, 1032 PA cephalometric images were used for model training and validation. Two human expert examiners independently and manually identified 19 landmarks on 82 test set images. Similarly, the constructed artificial intelligence (AI) algorithm automatically identified the landmarks on the images. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the performance of the model. The performance of the model was comparable with that of the examiners. The MRE of the model was 1.87 ± 1.53 mm, and the SDR was 34.7%, 67.5%, and 91.5% within error ranges of < 1.0, < 2.0, and < 4.0 mm, respectively. The sphenoid points and mastoid processes had the lowest MRE and highest SDR in auto-identification; the condyle points had the highest MRE and lowest SDR. Comparable with human examiners, the fully automatic PA cephalometric landmark identification model showed promising accuracy and reliability and can help clinicians perform cephalometric analysis more efficiently while saving time and effort. Future advancements in AI could further improve the model accuracy and efficiency.

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“… 34 , 35 It has demonstrated the ability to accurately measure facial features, 36 assign severity grades for cleft palate, 37 and detect a large number of landmarks for cephalometric analysis. 38 , 39 Although it is already a hot topic in ear recognition, 35 , 40 this automatic landmark detection is yet to be applied to the evaluation of prominent ears. Automated algorithms based on three-dimensional imaging may also be used to accurately define the planes needed for measuring the auriculocephalic angle.…”

Section: Discussionmentioning

confidence: 99%

Objective evaluation of nonsurgical treatment of prominent ears: A systematic review

et al. 2023

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Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis

Cited by 17 publications

References 72 publications

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts

Objective evaluation of nonsurgical treatment of prominent ears: A systematic review

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