Evaluation of a multi-stage convolutional neural network-based fully automated landmark identification system using cone-beam computed tomographysynthesized posteroanterior cephalometric images
김민정, Liu Yi, 오송희, 안효원, 김성훈, Nelson Gerald,
소속 상세정보
김민정 ( Kim Min-Jung ) - Kyung Hee University Graduate School Department of Orthodontics
( Liu Yi ) - Peking University School of Stomatology Department of Orthodontics
오송희 ( Oh Song-Hee ) - Kyung Hee University Graduate School Department of Oral and Maxillofacial Radiology
안효원 ( Ahn Hyo-Won ) - Kyung Hee University Graduate School Department of Orthodontics
김성훈 ( Kim Seong-Hun ) - Kyung Hee University Graduate School Department of Orthodontics
( Nelson Gerald ) - University of California SanFrancisco Department of Orofacial Science Division of Orthodontics
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.
키워드
Artificial intelligence; Convolutional neural networks; Posteroanterior cephalometrics; Cone-beam computed tomography
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