Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study

Yuichi Mine, Yuko Iwamoto, Shota Okazaki, Kentaro Nakamura, Saori Takeda, Tzu-Yu Peng, Chieko Mitsuhata, Naoya Kakimoto, Katsuyuki Kozai, Takeshi Murayama

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment are desirable in children with supernumerary teeth.

AIM: This study aimed to apply convolutional neural network (CNN)-based deep learning to detecting the presence of supernumerary teeth in children during the early mixed dentition stage.

DESIGN: 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 analysed. The CNNs performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristics (ROC) curves, and area under the ROC curves for a cross-validation test dataset.

RESULTS: The VGG16-TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models showed similar performance.

CONCLUSION: CNN-based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.

Original languageEnglish
JournalInternational Journal of Paediatric Dentistry
DOIs
Publication statusE-pub ahead of print - Dec 13 2021

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