Convolutional neural network may improve classification accuracy of molar morphology

Convolutional neural networks may be effective at detecting fused-rooted mandibular second molars on two-dimensional X-rays prior to root canal treatment, according to a study published in the Journal of Endodontics.

Researchers used the micro-computed tomography reconstruction images and two-dimensional X-ray projection images of mandibular second molars. The molars’ ground truth classifications — including 109 merging, 119 symmetrical and 43 asymmetrical molar types — were determined by the micro-CT reconstruction images.

Further, traditional augmentation techniques were used to amplify the X-ray images, and several pretrained models (VGG19, ResNet50 and EfficientNet-b5) were employed to identify the molars’ root canal morphologies. The researchers then compared the classification of the molars performed by endodontic residents with those performed by the convolutional neural networks.

They found that the ResNet18 model in combination with multiangle projection had the highest overall accuracy compared with the other models and was able to effectively identify apical separation, asymmetrical cervical isthmus, apical isthmus and apical merge. Additionally, the convolutional neural networks outperformed endodontic residents in classifying root canal morphology.

The researchers concluded that convolutional neural networks may have the potential to aid clinicians in the diagnosis of fused-rooted mandibular second molars.

Read more: Journal of Endodontics

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