Evaluation of a multi-stage convolutional neural network-based fully automated landmark identification system using cone-beam computed tomographysynthesized posteroanterior cephalometric images
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.
Abstract
Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.
참고문헌 (23)
- Major PW, "Landmark identification error in posterior anterior cephalometrics", Angle Orthod, 64, pp.447~454 (1994)
- Ulkur F, "Landmark errors on posteroanterior cephalograms", Am J Orthod Dentofacial Orthop, 150, pp.324~331 (2016)
- Eui-Ri Na, "A comparative study of the reproducibility of landmark identification on posteroanterior and anteroposterior cephalograms generated from cone-beam computed tomography scans", 대한치과교정학회지, 49(1), pp.41~48 (2019), 대한치과교정학회[DOI]
- Sicurezza E, "Accuracy of landmark identification on postero-anterior cephalograms", Prog Orthod, 13, pp.132~140 (2012)
- Pirttiniemi P, "Combined effects of errors in frontal-view asymmetry diagnosis", Eur J Orthod, 18, pp.629~636 (1996)
- Major PW, "Effect of head orientation on posterior anterior cephalometric landmark identification", Angle Orthod, 66, pp.51~60 (1996)
- Smektała T, "Experimental and clinical assessment of three-dimensional cephalometry : a systematic review", J Craniomaxillofac Surg, 42, pp.1795~1801 (2014)
- Kamoen A, "The clinical significance of error measurement in the interpretation of treatment results", Eur J Orthod, 23, pp.569~578 (2001)
- Gribel BF, "Accuracy and reliability of craniometric measurements on lateral cephalometry and 3D measurements on CBCT scans", Angle Orthod, 81, pp.26~35 (2011)
- Gribel BF, "From 2D to 3D : an algorithm to derive normal values for 3-dimensional computerized assessment", Angle Orthod, 81, pp.3~10 (2011)
- Meiyappan N, "Natural head position : an overview", J Pharm Bioallied Sci, 7(Suppl 2), pp.S424~S427 (2015)
- El-Mangoury NH, "Landmark identification in computerized posteroanterior cephalometrics", Am J Orthod Dentofacial Orthop, 91, pp.57~61 (1987)
- Leonardi R, "Landmark identification error in posteroanterior cephalometric radiography. A systematic review", Angle Orthod, 784, pp.761~765 (2008)
- Shokri A, "Effect of changing the head position on accuracy of transverse measurements of the maxillofacial region made on cone beam computed tomography and conventional posterioranterior cephalograms", Dentomaxillofac Radiol, 46, pp.20160180~ (2017)
- Oshagh M, "Effects of image enhancement on reliability of landmark identification in digital cephalometry", Indian J Dent Res, 24, pp.98~103 (2013)
- Arık SÖ, "Fully automated quantitative cephalometry using convolutional neural networks", J Med Imaging (Bellingham), 4, pp.014501~ (2017)
- Park JH, "Automated identification of cephalometric landmarks : part 1-comparisons between the latest deep-learning methods YOLOV3 and SSD", Angle Orthod, 89, pp.903~909 (2019)
- Hwang HW, "Automated identification of cephalometric landmarks: part 2-might it be better than human?", Angle Orthod, 90, pp.69~76 (2019)
- Takahashi R, "Multi-stage convolutional neural networks for robustness to scale transformation", Paper presented at: 2017 International Symposium on Nonlinear Theory and Its Applications, pp.692~695 (2017)
- Anwar SM, "Medical image analysis using convolutional neural networks : a review", J Med Syst, 42, pp.226~ (2018)
- Yadav SS, "Deep convolutional neural network based medical image classification for disease diagnosis", J Big Data, 6, pp.113~ (2019)
- Schwendicke F, "Convolutional neural networks for dental image diagnostics : a scoping review", J Dent, 91, pp.103226~ (2019)
- Park JJ, "Convolutional-neural-network-based diagnosis of appendicitis via CT scans in patients with acute abdominal pain presenting in the emergency department", Sci Rep, 10, pp.9556~ (2020)