Identification of root canal morphology in fused-rooted mandibular second molars from X-ray images based on deep learning.

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Abstract

Understanding the intricate anatomical morphology of fused-rooted mandibular second molars (MSMs) is essential for root canal treatment. The present study utilized a deep learning approach to identify the three-dimensional root canal morphology of MSMs from two-dimensional X-ray images.A total of 271 fused-rooted MSMs were included in the study. Micro-computed tomography (micro-CT) reconstruction images and two-dimensional X-ray projection images were obtained. The ground truth of three-dimensional root canal morphology was determined through micro-CT images, which were classified into merging, symmetrical, and asymmetrical types. To amplify the X-ray image dataset, traditional augmentation techniques from the python package Augmentor and a multi-angle projection method were employed. Identification of root canal morphology was conducted using the pre-trained VGG19, ResNet18, ResNet50, and EfficientNet-b5 on X-ray images. The classification results from convolutional neural networks (CNNs) were then compared with those performed by endodontic residents.The multi-angle projection augmentation method outperformed the traditional approach in all CNNs except for EfficientNet-b5. ResNet18 combined with the multi-angle projection method outperformed all other combinations, with an overall accuracy of 79.25%. In specific classifications, accuracies of 81.13%, 86.79%, and 90.57% were achieved for merging, symmetrical, and asymmetrical types, respectively. Notably, CNNs surpassed endodontic residents in classification performance; the average accuracy for endodontic residents were only 60.38% (P< 0.05).CNNs were more effective than endodontic residents in identifying the three-dimensional root canal morphology of MSMs. The result indicates that CNNs possess the capacity to employ two-dimensional images effectively in aiding three-dimensional diagnoses.Copyright © 2024. Published by Elsevier Inc.