Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU‐Net

Haihua Zhu; Haojie Yu; Fan Zhang; Zheng Cao; Fuli Wu; Fudong Zhu

doi:10.1111/ipd.12964

journal article May 03, 2022

Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU‐Net

Haihua Zhu

International Journal of Paediatric Dentistry Vol. 32 No. 6 pp. 785-792 · Wiley

View at Publisher Save 10.1111/ipd.12964

Abstract

AbstractAimThe purpose of this research was to present an artificial intelligence (AI) model, which can automatically segment and detect ectopic eruption of first permanent molars (EMMs) in early mixed dentition on panoramic radiographs using the no‐new‐Net (nnU‐Net) model.DesignA total of 438 EMMs obtained from 285 panoramic radiographs were included in this study. An AI model based on nnU‐Net was trained to segment and detect EMMs. The performance of the model was evaluated by the intersection over union (IoU), precision, F1‐score, accuracy and FROC. Furthermore, the detecting performance of nnU‐Net was compared with that of three dentists with different years of experience using the McNemar chi‐squared test. The reliability of different dentists was evaluated by intraclass correlation coefficients (ICCs).ResultsThe nnU‐Net yielded an IoU of 0.834, a precision of 0.845, an F1‐score of 0.902 and an accuracy of 0.990, whereas the dentists yielded a mean IoU of 0.530, a mean precision of 0.539, a mean F1‐score of 0.699 and a mean accuracy of 0.811. The ICC of different dentists was 0.776. The statistical analysis of the McNemar chi‐squared test showed that the nnU‐Net results were statistically significant and superior to those of dentists (p < .05).ConclusionThis study validated an AI model based on nnU‐Net for automatically segmenting and detecting EMMs more consistently and accurately on panoramic radiography.

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Details

Published: May 03, 2022
Vol/Issue: 32(6)
Pages: 785-792
License: View

Authors

H

Haihua Zhu

Stomatology Hospital School of Stomatology Zhejiang University School of Medicine Zhejiang Provincial Clinical Research Center for Oral Diseases Key Laboratory of Oral Biomedical Research of Zhejiang Province Cancer Center of Zhejiang University Hangzhou China

H

Haojie Yu

Stomatology Hospital School of Stomatology Zhejiang University School of Medicine Zhejiang Provincial Clinical Research Center for Oral Diseases Key Laboratory of Oral Biomedical Research of Zhejiang Province Cancer Center of Zhejiang University Hangzhou China

F

Fan Zhang

College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Z

Zheng Cao

College of Computer Science and Technology Zhejiang University Hangzhou China

F

Fuli Wu

College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

F

Fudong Zhu

Stomatology Hospital School of Stomatology Zhejiang University School of Medicine Zhejiang Provincial Clinical Research Center for Oral Diseases Key Laboratory of Oral Biomedical Research of Zhejiang Province Cancer Center of Zhejiang University Hangzhou China

Funding

Natural Science Foundation of Zhejiang Province Award: LZY21F030002

Cite This Article

Haihua Zhu, Haojie Yu, Fan Zhang, et al. (2022). Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU‐Net. International Journal of Paediatric Dentistry, 32(6), 785-792. https://doi.org/10.1111/ipd.12964

Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU‐Net

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