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Automated Skeletal Classification with Lateral Cephalometry Based on Artificial Intelligence

Title
Automated Skeletal Classification with Lateral Cephalometry Based on Artificial Intelligence
Authors
Yu, H. J.Cho, S. R.Kim, M. J.Kim, W. H.Kim, J. W.Choi, J.
Ewha Authors
김민지김진우
SCOPUS Author ID
김민지scopusscopus; 김진우scopus
Issue Date
2020
Journal Title
JOURNAL OF DENTAL RESEARCH
ISSN
0022-0345JCR Link

1544-0591JCR Link
Citation
JOURNAL OF DENTAL RESEARCH vol. 99, no. 3, pp. 249 - 256
Keywords
deep learningorthodonticsdiagnosisorthognathic surgerydiagnostic imagingneural networks
Publisher
SAGE PUBLICATIONS INC
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Lateral cephalometry has been widely used for skeletal classification in orthodontic diagnosis and treatment planning. However, this conventional system, requiring manual tracing of individual landmarks, contains possible errors of inter- and intravariability and is highly time-consuming. This study aims to provide an accurate and robust skeletal diagnostic system by incorporating a convolutional neural network (CNN) into a 1-step, end-to-end diagnostic system with lateral cephalograms. A multimodal CNN model was constructed on the basis of 5,890 lateral cephalograms and demographic data as an input. The model was optimized with transfer learning and data augmentation techniques. Diagnostic performance was evaluated with statistical analysis. The proposed system exhibited >90% sensitivity, specificity, and accuracy for vertical and sagittal skeletal diagnosis. Clinical performance of the vertical classification showed the highest accuracy at 96.40 (95% CI, 93.06 to 98.39; model III). The receiver operating characteristic curve and the area under the curve both demonstrated the excellent performance of the system, with a mean area under the curve >95%. The heat maps of cephalograms were also provided for deeper understanding of the quality of the learned model by visually representing the region of the cephalogram that is most informative in distinguishing skeletal classes. In addition, we present broad applicability of this system through subtasks. The proposed CNN-incorporated system showed potential for skeletal orthodontic diagnosis without the need for intermediary steps requiring complicated diagnostic procedures.
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DOI
10.1177/0022034520901715
Appears in Collections:
의과대학 > 의학과 > Journal papers
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