Automatic machine learning-based classification of mandibular third molar impaction status

The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

“…Previous studies used deep learning to develop an automatic mandibular third molar impaction classification system with several architectures, such as ResNet-34 ( Yoo et al, 2021 ), VGG-16 ( Maruta et al, 2023 , Sukegawa et al, 2022a ), and YOLOv3 ( Celik, 2022 ) ( Table 1 ). Based on the review results in Table 1 , deep learning shows a high performance in the classification of mandibular ITM (>78.91 %) ( Yoo et al, 2021 ).…”

“…The use of data augmentation is one of the ideas proposed to increase the accuracy of deep learning. Flip, rotation, and adjustment of the brightness, sharpness, contrast and additional images are performed using paint software to mimic dental fillings or caries ( Maruta et al, 2023 ).…”

“…The performance indicator of deep learning for the classification ITM, such as sensitivity, specificity, precision, recall, and accuracy, cannot be compared with one another because they use the different classification indicators ( Table 2 ) ( Celik, 2022 , Sukegawa et al, 2022a , Yoo et al, 2021 ). Sukegawa et al, (2022a) and Maruta et al (2023) used the same classification indicator and deep learning architecture, thus, these studies can be compared. Using larger datasets, Sukegawa et al, (2022a) reported accuracy better than Maruta et al (2023) .…”

“… Sukegawa et al, (2022a) and Maruta et al (2023) used the same classification indicator and deep learning architecture, thus, these studies can be compared. Using larger datasets, Sukegawa et al, (2022a) reported accuracy better than Maruta et al (2023) .…”

“… Yoo et al (2021) determined the sensitivity and specificity using positive predictive value (PPV) calculations. Furthermore, the studies reviewed in this work did not use a consistent mathematical formula ( Ariji et al, 2022 , Buyuk et al, 2022 , Celik, 2022 , Choi et al, 2022 , Maruta et al, 2023 , Takebe et al, 2022 , Vinayahalingam et al, 2019 , Yoo et al, 2021 , Zhu et al, 2021 ). Celik, 2022 , Choi et al, 2022 , Takebe et al, 2022 , and Zhu et al (2021) , used PPV calculations to determine precision in their research.…”