Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study.
artificial intelligence
convolutional neural network
deep learning
supernumerary teeth
transfer learning
Journal
International journal of paediatric dentistry
ISSN: 1365-263X
Titre abrégé: Int J Paediatr Dent
Pays: England
ID NLM: 9107511
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
revised:
05
11
2021
received:
14
07
2021
accepted:
28
11
2021
pubmed:
15
12
2021
medline:
17
8
2022
entrez:
14
12
2021
Statut:
ppublish
Résumé
Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth. This study aimed to apply convolutional neural network (CNN)-based deep learning to detect the presence of supernumerary teeth in children during the early mixed dentition stage. Three CNN models, AlexNet, VGG16-TL, and InceptionV3-TL, were employed in this study. A total of 220 panoramic radiographs (from children aged 6 years 0 months to 9 years 6 months) including supernumerary teeth (cases, n = 120) or no anomalies (controls, n = 100) were retrospectively analyzed. The CNN performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the ROC curves for a cross-validation test dataset. The VGG16-TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models had similar performance. CNN-based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.
Sections du résumé
BACKGROUND
BACKGROUND
Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth.
AIM
OBJECTIVE
This study aimed to apply convolutional neural network (CNN)-based deep learning to detect the presence of supernumerary teeth in children during the early mixed dentition stage.
DESIGN
METHODS
Three CNN models, AlexNet, VGG16-TL, and InceptionV3-TL, were employed in this study. A total of 220 panoramic radiographs (from children aged 6 years 0 months to 9 years 6 months) including supernumerary teeth (cases, n = 120) or no anomalies (controls, n = 100) were retrospectively analyzed. The CNN performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the ROC curves for a cross-validation test dataset.
RESULTS
RESULTS
The VGG16-TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models had similar performance.
CONCLUSION
CONCLUSIONS
CNN-based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.
Types de publication
Journal Article
Langues
eng
Pagination
678-685Subventions
Organisme : the Ministry of Education, Culture, Sports, Science and Technology of Japan
ID : 20K18604
Informations de copyright
© 2021 BSPD, IAPD and John Wiley & Sons Ltd.
Références
Brook AH, Griffin RC, Townsend G, Levisianos Y, Russell J, Smith RN. Variability and patterning in permanent tooth size of four human ethnic groups. Arch Oral Biol. 2009;54:S79-85.
Rajab LD, Hamdan MA. Supernumerary teeth: review of the literature and a survey of 152 cases. Int J Paediatr Dent. 2002;12:244-254.
Proff P, Fanghänel J, Allegrini S Jr, Bayerlein T, Gedrange T. Problems of supernumerary teeth, hyperdontia or dentes supernumerarii. Ann Anat. 2006;188:163-169.
Anthonappa RP, King NM, Rabie AB, Mallineni SK. Reliability of panoramic radiographs for identifying supernumerary teeth in children. Int J Paediatr Dent. 2012;22:37-43.
Anthonappa RP, King NM, Rabie AB. Prevalence of supernumerary teeth based on panoramic radiographs revisited. Pediatr Dent. 2013;35:257-261.
Hutson M. AI glossary: artificial intelligence, in so many words. Science. 2017;357:19.
Mine Y, Suzuki S, Eguchi T, Murayama T. Applying deep artificial neural network approach to maxillofacial prostheses coloration. J Prosthodont Res. 2020;64:296-300.
Kuwada C, Ariji Y, Fukuda M, et al. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130:464-469.
Cantu AG, Gehrung S, Krois J, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent. 2020;100:103425.
Murata M, Ariji Y, Ohashi Y, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol. 2019;35:301-307.
Lee KS, Kwak HJ, Oh JM, et al. Automated detection of TMJ osteoarthritis based on artificial intelligence. J Dent Res. 2020;99:1363-1367.
Takeda S, Mine Y, Yoshimi Y, Ito S, Tanimoto K, Murayama T. Landmark annotation and mandibular lateral deviation analysis of posteroanterior cephalograms using a convolutional neural network. J Dent Sci. 2021;16:957-963.
Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;2015(8):h5527.
Mongan J, Moy L, Kahn CE Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell. 2020;2:e200029.
Python. Accessed May 9, 2019. https://www.python.org/
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1097-1105.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint 2014;1409.1556.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. Proc IEEE Comput Soc Conf Comput vis Pattern Recognit. 2016;2818-2826.
Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput vis. 2015;115:211-252.
Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99:769-774.
American Academy of Pediatric Dentistry. Prescribing dental radiographs for infants, children, adolescents, and individuals with special health care needs. Pediatr Dent. 2018;40:213-215.
You W, Hao A, Li S, Wang Y, Xia B. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health. 2020;20:141.
Kılıc MC, Bayrakdar IS, Çelik Ö, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021;50(6):20200172.
Waring J, Lindvall C, Umeton R. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artif Intell Med. 2020;104:101822.
Constantinescu EC, Udriștoiu AL, Udriștoiu ȘC, et al. Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images. Med Ultrason. 2021;23:135-139.
Yamada A, Oyama K, Fujita S, et al. Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary? Int J Comput Assist Radiol Surg. 2019;14:1295-1301.