Deep Learning-Based Classification of Root Fusion Morphology in Maxillary Second Molars on Cone-Beam Computed Tomography: Impact of Image Preprocessing

Maxillary second molars with root fusion exhibit considerable anatomical variability that complicates dental treatment planning. Cone-beam computed tomography (CBCT) has become an essential imaging modality for evaluating such complex morphology. This study aimed to develop a deep learning (DL)-based computer-aided diagnosis (CAD) system for the automated classification of root fusion types in maxillary second molars, and to systematically evaluate the impact of image preprocessing strategies on model performance. CBCT images from 172 maxillary second molars (3188 two-dimensional slices) were collected and classified into seven root fusion types. A ResNet50 convolutional neural network was trained with tooth-level 5-fold cross-validation. The effects of grayscale bit depth (4096 vs. 256 levels), preprocessing mode (original, binarized, and segmented images), and contrast enhancement techniques (histogram equalization [HE] and contrast-limited adaptive histogram equalization [CLAHE] with varying parameters) on classification accuracy were systematically compared. The model trained on segmented, non-binarized images with 4096 grayscale levels achieved the highest overall accuracy of 91.6%. In descending order of performance: CLAHE-1 (90.5%), CLAHE-3 (90.5%), CLAHE-40 (88.2%), and HE (83.9%). The unprocessed original image group yielded the lowest accuracy (57.5%). A ResNet50-based DL system can effectively classify complex root fusion morphology in maxillary second molars. Preserving the natural radiopacity gradient of CBCT images, rather than applying artificial contrast enhancement, was critical for optimal model performance. These findings provide a reference for preprocessing strategy selection in dental CBCT-based AI systems.