A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Protocol and Eligibility Criteria
2.2. Information Sources and Search Strategy
2.3. Study Selection and Data Extraction
2.4. Risk-of-Bias Assessment
2.5. Data Synthesis and Analysis
3. Results
3.1. Study Selection and Data Extraction
3.2. Risk of Bias Assessment
3.3. Meta-Analysis
3.4. Sensitivity Meta-Analysis
3.5. Publication Bias Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Study, Year | Country | Imaging Examination | Architecture | Number of Experts Involved in Manual Landmarking | Number of Training/Testing | Numbers of Landmark | MRE ± SD (mm) | SDR < 2 mm (%) | Results |
---|---|---|---|---|---|---|---|---|---|
Shahidi et al., 2014 [12] | Iran | CBCT | Image registration method using MATLAB software language | 3 | 8/20 | 14 | 3.40 ± 1.48 | NR | The mean errors for all 14 landmarks were less than 4 mm, and over 63% of them had a mean error of less than 3 mm when compared to manual measurements. |
Wang et al., 2018 [13] | China | Lateral cephalograms | Multiresolution decision tree regression voting using scale invariant feature transform-based patch features | 2 | 150/150 | 19 | 1.69 ± 1.43 | 73.37 | The algorithm’s average 73% successful detection rate, which falls within a precision range of 2.0 mm, was validated by the clinical database. |
Hwang et al., 2020 [14] | Korea | Lateral cephalograms | You Only Look Once, Version 3 (YOLOv3) | 2 | 1028/283 | 80 | 1.46 ± 2.97 | NR | Artificial intelligence achieved an accuracy in identifying cephalometric landmarks that was on par with that of human examinations. |
Muraev et al., 2020 [15] | Russia | Frontal cephalograms | ANN | 13 | 300/30 | 45 | 2.87 ± 0.99 | NR | The outcome of this study reveals that artificial neural networks attained accuracy levels comparable to human experts in identifying cephalometric landmarks. |
Kim, J. et al., 2021 [16] | Korea | Lateral cephalogram | A cascaded CNN | 2 | 440/100 | 20 | 1.36 ± 0.98 | 83.6 | The total automatic detection mistake was 1.36 ± 0.98 mm, and the average error for identifying each landmark ranged from 0.46 ± 0.37 mm for maxillary incisor crown tip to 2.09 ± 1.91 mm for distal root tip of the mandibular first molar. |
Kim, M. et al., 2021 [17] | Korea | Posteroanterior CBCT | A multi-stage CNN | 1 | 345/85 | 23 | 2.23 ± 2.02 | 60.88 | Automatic identification of cephalometric landmarks using CBCT synthesis did not achieve a clinically acceptable level of accuracy, as the error range fell short of the desired threshold of less than 2 mm. |
Gil et al., 2022 [18] | Korea | Posteroanterior cephalogram | A cascaded CNN | 1 | 2418/99 | 16 | 1.52 ± 1.13 | 83.3 | The cascade convolution neural network algorithm for automatically identifying posteroanterior cephalometric landmarks demonstrated potential as a viable alternative to manual identification. |
Le et al., 2022 [19] | Korea | Lateral cephalogram | The deep anatomical context feature learning model | 20 | 1193/10 | 41 | 1.87 ± 2.04 | 73.17 | The use of beginner-artificial intelligence collaboration was successful in identifying cephalometric landmarks. |
Blum et al., 2023 [20] | Germany | CBCT | Densilia® (Munich, Germanay): software using CNN algorithm | 4 | 931/114 | 35 | 2.73 ± 2.37 | NR | The level of accuracy achieved in automatic landmark detection falls within the clinically acceptable range, and it is comparable to the precision of manual landmark determination, while also requiring significantly less time. |
Han et al., 2024 [21] | Korea | Posteroanterior cephalogram | A cascaded CNN | 2+2 | 2150/377 | 9 | 1.26 ± 1.94 | 83.2 | The cascaded-convolutional neural network model can be considered a useful tool for automatically identifying midline landmarks and determining the extent of midline deviation in posteroanterior cephalograms of adult patients. |
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Lee, Y.; Pyeon, J.-H.; Han, S.-H.; Kim, N.J.; Park, W.-J.; Park, J.-B. A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis. Appl. Sci. 2024, 14, 7342. https://doi.org/10.3390/app14167342
Lee Y, Pyeon J-H, Han S-H, Kim NJ, Park W-J, Park J-B. A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis. Applied Sciences. 2024; 14(16):7342. https://doi.org/10.3390/app14167342
Chicago/Turabian StyleLee, Yoonji, Jeong-Hye Pyeon, Sung-Hoon Han, Na Jin Kim, Won-Jong Park, and Jun-Beom Park. 2024. "A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis" Applied Sciences 14, no. 16: 7342. https://doi.org/10.3390/app14167342