Deep learning based prediction of extraction difficulty for mandibular third molars

Yoo, Jeong-Hun; Yeom, Han-Gyeol; Shin, WooSang; Yun, Jong Pil; Lee, Jong Hyun; Jeong, Soo‐Hwan; Lim, Hun Jun; Lee, Jun; Kim, Bong-Chul

doi:10.1038/s41598-021-81449-4

Cited by 59 publications

(44 citation statements)

References 22 publications

(50 reference statements)

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“…253 preoperative PR(s) of patients who underwent third molar removal were retrospectively selected from the Department of Oral and Maxillofacial Surgery of Radboud University Nijmegen Medical Centre, Netherlands (mean age of 31.7 years, standard deviation of 12.7, age range of 16–80 years, 140 males and 113 females) 6 . The accumulated PR(s) were acquired with a Cranex Novus e device (Soredex, Helsinki, Finland), operated at 90 kV and 10 mA, using a CCD sensor.…”

Section: Methodsmentioning

confidence: 99%

“…Taking the numerous interactions between all those factors into account, it might be challenging to make the correct decision during an average presurgical consultation. An automated decision-making tool for third molar removal may have the potential to aid patients and surgeon to make the right choice 6 . The detection of pathologies associated with third molars on PR is the first step in the automation of M3 removal diagnostics.…”

Section: Introductionmentioning

confidence: 99%

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Classification of caries in third molars on panoramic radiographs using deep learning

Vinayahalingam

Kempers

Limon

et al. 2021

Sci Rep

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The objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of caries in third molars on panoramic radiographs using deep learning

Vinayahalingam

Kempers

Limon

et al. 2021

Sci Rep

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“…Few studies have used deep learning to classify the position of the mandibular third molar. Yoo et al [22] performed class, position, and Winter's classi cations of the mandibular third molar. The observed accuracy was 78.1% for class, 82.0% for position, and 90.2% for Winter's classi cation.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning-based Positioning Classification of the Mandibular Third Molars: Is Multi-task Deep Learning Useful?

Sukegawa

Matsuyama

Tanaka

et al. 2021

Preprint

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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 1,330 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, 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 deep learning study is the first 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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“…They are being applied in a variety of dental and maxillofacial fields. For instance, they are used to assess soft-tissue profiling and extraction difficulty for mandibular third molars [18,19]. In addition, Xiao et al proposed an end-to-end deep-learning framework to estimate patientspecific reference bony shape models for patients with orthognathic deformities [20].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric Radiography

et al. 2021

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The aim of this study was to reveal cranio-spinal differences between skeletal classification using convolutional neural networks (CNNs). Transverse and longitudinal cephalometric images of 832 patients were used for training and testing of CNNs (365 males and 467 females). Labeling was performed such that the jawbone was sufficiently masked, while the parts other than the jawbone were minimally masked. DenseNet was used as the feature extractor. Five random sampling crossvalidations were performed for two datasets. The average and maximum accuracy of the five crossvalidations were 90.43% and 92.54% for test 1 (evaluation of the entire posterior–anterior (PA) and lateral cephalometric images) and 88.17% and 88.70% for test 2 (evaluation of the PA and lateral cephalometric images obscuring the mandible). In this study, we found that even when jawbones of class I (normal mandible), class II (retrognathism), and class III (prognathism) are masked, their identification is possible through deep learning applied only in the cranio-spinal area. This suggests that cranio-spinal differences between each class exist.

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Deep learning based prediction of extraction difficulty for mandibular third molars

Cited by 59 publications

References 22 publications

Classification of caries in third molars on panoramic radiographs using deep learning

Classification of caries in third molars on panoramic radiographs using deep learning

A Deep Learning-based Positioning Classification of the Mandibular Third Molars: Is Multi-task Deep Learning Useful?

Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric Radiography

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