Automated Skeletal Classification with Lateral Cephalometry Based on Artificial Intelligence
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.
Get full access to this article
View all access and purchase options for this article.
References
Arık SÖ, Ibragimov B, Xing L. 2017. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging (Bellingham). 4(1):014501.
Bishara SE, Fahl JA, Peterson LC. 1983. Longitudinal changes in the ANB angle and wits appraisal: clinical implications. Am J Orthod. 84(2):133–139.
Björk A. 1969. Prediction of mandibular growth rotation. Am J Orthod. 55(6):585–599.
Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schürmann F, Krejci I, Markram H. 2019. Caries detection with near-infrared transillumination using deep learning. J Dent Res. 98(11):1227–1233.
da Fontoura CS, Miller SF, Wehby GL, Amendt BA, Holton NE, Southard TE, Allareddy V, Moreno Uribe LM. 2015. Candidate gene analyses of skeletal variation in malocclusion. J Dent Res. 94(7):913–920.
da Silveira HL, Silveira HE. 2006. Reproducibility of cephalometric measurements made by three radiology clinics. Angle Orthod. 76(3):394–399.
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. 2009. Imagenet: a large-scale hierarchical image database. Miami (FL): 2009 IEEE Conference on Computer Vision and Pattern Recognition. p. 248–255.
Doff M, Hoekema A, Pruim G, Slater JH, Stegenga B. 2010. Long-term oral-appliance therapy in obstructive sleep apnea: a cephalometric study of craniofacial changes. J Dent. 38(12):1010–1018.
Durão AR, Pittayapat P, Rockenbach MIB, Olszewski R, Ng S, Ferreira AP, Jacobs R. 2013. Validity of 2D lateral cephalometry in orthodontics: a systematic review. Prog Orthod. 14(1):31.
Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. 2019. Deep learning for the radiographic detection of apical lesions.J Endod. 45(7):917–922.e5.
Fleiss J. 1971. Measuring nominal scale agreement among many raters. Psychol Bull. 76(5):378–382.
Gravely J, Benzies PM. 1974. The clinical significance of tracing error in cephalometry. Br J Orthod. 1(3):95–101.
He H, Garcia EA. 2008. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering. 21(9):1263–1284.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. 2017. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. p. 4700–4708. https://arxiv.org/abs/1608.06993
Jacobson A. 1975. The “wits” appraisal of jaw disharmony. Am J Orthod. 67(2):125–138.
Jarabak JA, Fizzel JR. 1972. Technique and treatment with light wire appliances. Am J Orthod Dentofacial Orthop. 64(3):317–318.
Kamoen A, Dermaut L, Verbeeck R. 2001. The clinical significance of error measurement in the interpretation of treatment results. Eur J Orthod. 23(5):569–578.
Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, Dörfer C, Schwendicke F. 2019. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 9(1):8495.
Lee H, Park M, Kim J. 2017. Cephalometric landmark detection in dental x-ray images using convolutional neural networks. Proceedings of the SPIE: Medical Imaging 2017: Computer-Aided Diagnosis. 10134:101341W.
Nishimoto S, Sotsuka Y, Kawai K, Ishise H, Kakibuchi M. 2019. Personal computer-based cephalometric landmark detection with deep learning, using cephalograms on the internet. J Craniofac Surg. 30(1):91–95.
Perez L, Wang J. 2017. The effectiveness of data augmentation in image classification using deep learning. https://arxiv.org/abs/1712.04621.
Steiner CC. 1953. Cephalometrics for you and me. Am J Orthod. 39(10):729–755.
Steiner CC. 1959. Cephalometrics in clinical practice. Angle Orthod. 29(1):8–29.
Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S. 2018. Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci Rep. 8:1727.
Torosdagli N, Liberton DK, Verma P, Sincan M, Lee JS, Bagci U. 2018. Deep geodesic learning for segmentation and anatomical landmarking. IEEE Trans Med Imaging. 38(4):919–931.
Trpkova B, Major P, Prasad N, Nebbe B. 1997. Cephalometric landmarks identification and reproducibility: a meta analysis. Am J Orthod Dentofacial Orthop. 112(2):165–170.
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. 2017. ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Honolulu (HI): 2017 IEEE Conference on Computer Vision and Pattern Recognition. p. 2097–2106.
Yamaguchi S, Lee C, Karaer O, Ban S, Mine A, Imazato S. 2019. Predicting the debonding of CAD/CAM composite resin crowns with AI. J Dent Res. 98(11):1234–1238.
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. 2016. Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2921–2929.
Zhu W, Lou Q, Vang YS, Xie X. 2017. Deep multi-instance networks with sparse label assignment for whole mammogram classification. Quebec City (Canada): International Conference on Medical Image Computing and Computer-Assisted Intervention. p. 603–611.
Supplementary Material
Please find the following supplemental material visualised and available to download via Figshare in the display box below. Where there are more than one item, you can scroll through each tab to see each separate item.
Please note all supplemental material carries the same license as the article it is here associated with
Summary
A supplemental appendix to this article is available online.
Resources
File (ds_10.1177_0022034520901519.pdf)
- Download
- 622.85 KB
Cite article
Cite article
Cite article
OR
Download to reference manager
If you have citation software installed, you can download article citation data to the citation manager of your choice
Information, rights and permissions
Information
Published In
Article first published online: January 24, 2020
Issue published: March 2020
Keywords
PubMed: 31977286
Authors
Metrics and citations
Metrics
Article usage*
Total views and downloads: 2630
*Article usage tracking started in December 2016
Altmetric
See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores
Articles citing this one
Receive email alerts when this article is cited
Web of Science: 78 view articles Opens in new tab
Crossref: 101
- State of the art and prospects for artificial intelligence in orthogna...
- Machine learning-based decision support system for orthognathic diagno...
- Deep Learning in Dental Radiographic Imaging
- An Automatic Grading System for Orthodontically Induced External Root ...
- Accuracy of artificial intelligence‐assisted growth prediction in skel...
- AI-based dental caries and tooth number detection in intraoral photos:...
- Machine Learning Effectively Diagnoses Mandibular Deformity Using Thre...
- Accuracy of posteroanterior cephalogram landmarks and measurements ide...
- AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning...
- Artificial Intelligence Applications in Orthodontics
- Artificial Intelligence in Orthognathic Surgery – A Narrative Review o...
- Evaluation of artificial intelligence model for crowding categorizatio...
- Deep learning-based prediction of mandibular growth trend in children ...
- Influence of growth structures and fixed appliances on automated cepha...
- Intra-oral scan segmentation using deep learning
- Current Progress and Challenges of Using Artificial Intelligence in Cl...
- Abnormal maxillary sinus diagnosing on CBCT ...
- Orthodontic craniofacial pattern diagnosis: cephalometric geometry and...
- Evaluation of AI Model for Cephalometric Landmark Classification (TG D...
- Ceph-Net: automatic detection of cephalometric landmarks on scanned la...
- Revealing the representative facial traits of different sagittal skele...
- Identification of Dental Implants with the Development of an Artificia...
- The knowledge, experience, and attitude on artificial intelligence-ass...
- Application of Artificial Intelligence in Orthodontics: Current State ...
- Evaluation of automated detection of head position on lateral cephalom...
- Accuracy in Teeth Structures Segmentation on MRI and CT Images
- Artificial intelligence‐assisted determination of available sites for ...
- A Comprehensive Review of Recent Advances in Artificial Intelligence f...
- Three-Dimensional Enlow’s Counterpart Analysis: Neutral Track
- Artificial Intelligence and Machine Learning for Automated Cephalometr...
- Exploring the Intersection of Artificial Intelligence and Clinical Hea...
- Automated Sagittal Skeletal Classification of Children Based on Deep L...
- Explainable deep learning-based clinical decision support engine for M...
- Automatic detection of adenoid hypertrophy on cone-beam computed tomog...
- A brief overview of artificial intelligence in dentistry: Current scop...
- Inteligencia artificial en diagnóstico, pronóstico y planificación del...
- Preciseness of artificial intelligence for lateral cephalometric measu...
- Artificial Intelligence Systems Assisting in the Assessment of the Cou...
- Artificial Intelligence Splint in Orthognathic Surgery for Skeletal Cl...
- Artificial intelligence in dentistry—A review
- Automated Evaluation of Upper Airway Obstruction Based on Deep Learnin...
- Hyper Automation and Augmented Intelligence
- Personalized dental medicine, artificial intelligence, and their relev...
- Artificial Intelligence Applicability in Orthodontics: Quo Vadis Ortho...
- Machine Learning Predictive Model as Clinical Decision Support System ...
- Applications of Artificial Intelligence in Dentistry
- Effect of prior knowledge about treatment on cephalometric measurement...
- Artificial intelligence in medico-dental diagnostics of the face: a na...
- Deep convolutional neural network-based skeletal classification of cep...
- Effectiveness of cone-beam computed tomography-generated cephalograms ...
- Development, Application, and Performance of Artificial Intelligence i...
- Construction of an end‐to‐end regression neural network for the determ...
- Estado del arte de inteligencia artificial en ortodoncia. Revisión nar...
- Automated calibration system for length measurement of lateral cephalo...
- Trends and Application of Artificial Intelligence Technology in Orthod...
- Advancements in Dentistry with Artificial Intelligence: Current Clinic...
- Dental Lesion Segmentation Using an Improved ICNet Network with Attent...
- Applications of artificial intelligence and machine learning in orthog...
- Deep Learning–Based Prediction of the 3D Postorthodontic Facial Change...
- Potential and impact of artificial intelligence algorithms in dento-ma...
- Cephalometric Skeletal Structure Classification Using Convolutional Ne...
- Convolutional neural network-based automatic cervical vertebral matura...
- Predicting patient experience of Invisalign treatment: An analysis usi...
- DİŞ HEKİMLİĞİ UYGULAMALARINDA YAPAY ZEKÂ KULLANIMI
- Artificial intelligence for caries and periapical periodontitis detect...
- Caries Detection from Dental Images using Novel Maximum Directional Pa...
- Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A...
- Automatic Classification for Sagittal Craniofacial Patterns Based on D...
- Deep learning for the classification of cervical maturation degree and...
- Automatic detection of anteriorly displaced temporomandibular joint di...
- Artificial Intelligence for Classifying and Archiving Orthodontic Imag...
- Fully automated identification of cephalometric landmarks for upper ai...
- Accuracy of one-step automated orthodontic diagnosis model using a con...
- AIM in Dentistry
- Deep learning for categorization of endodontic lesion based on radiogr...
- Deep learning-based evaluation of the relationship between mandibular ...
- A machine learning approach to determine the prognosis of patients wit...
- Current applications and development of artificial intelligence for di...
- Comparison between cephalometric measurements using digital manual and...
- Evaluation of Lip Morphology and Nasolabial Angle in Non-Syndromic Cle...
- Review of the role of Artificial Intelligence in dentistry: Current ap...
- Artificial Intelligence and Ethics in Dentistry: A Scoping Review
- Deep learning based prediction of extraction difficulty for mandibular...
- Realistic high-resolution lateral cephalometric radiography generated ...
- A deep learning approach to automatic gingivitis screening based on cl...
- Deep learning based prediction of necessity for orthognathic surgery o...
- Applications of artificial intelligence and machine learning in orthod...
- Clinical applicability of automated cephalometric landmark identificat...
- Automated Radiographic Evaluation of Adenoid Hypertrophy Based on VGG-...
- Three-Dimensional Facial Soft Tissue Changes After Orthognathic Surger...
- Localization of cementoenamel junction in intraoral ultrasonographs wi...
- Machine learning and orthodontics, current trends and the future oppor...
- Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in ...
- Promises and perils of artificial intelligence in dentistry
- Artificial intelligence-driven novel tool for tooth detection and segm...
- AIM in Dentistry
- A Novel Method for Cephalometric Landmark Regression Using Convolution...
- A scoping review of transfer learning research on medical image analys...
- Developments, application, and performance of artificial intelligence ...
- Artificial Intelligence in dentistry: Concepts, Applications and Resea...
- Automated cephalometric landmark detection with confidence regions usi...
Figures and tables
Figures & Media
Tables
View Options
Get access
Access options
If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:
loading institutional access options
Alternatively, view purchase options below:
Purchase 24 hour online access to view and download content.
Access journal content via a DeepDyve subscription or find out more about this option.
