TY - JOUR
T1 - Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars
AU - Sukegawa, Shintaro
AU - Matsuyama, Tamamo
AU - Tanaka, Futa
AU - Hara, Takeshi
AU - Yoshii, Kazumasa
AU - Yamashita, Katsusuke
AU - Nakano, Keisuke
AU - Takabatake, Kiyofumi
AU - Kawai, Hotaka
AU - Nagatsuka, Hitoshi
AU - Furuki, Yoshihiko
N1 - Funding Information:
This work was indirectly supported by JSPS KAKENHI (Grant Number JP19K19158).
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Pell and Gregory, and Winter’s classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014–2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter’s classifications for specific respective tasks.
AB - Pell and Gregory, and Winter’s classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014–2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter’s classifications for specific respective tasks.
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U2 - 10.1038/s41598-021-04603-y
DO - 10.1038/s41598-021-04603-y
M3 - Article
C2 - 35027629
AN - SCOPUS:85123077521
VL - 12
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 684
ER -