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With the rapid expansion of artificial intelligence across clinical disciplines, a variety of artificial neural networks (ANNs) have become indispensable tools for endowing computer systems with advanced analytical power. Dentistry, as an information‑rich branch of medicine, routinely generates and must interpret large, complex datasets from imaging and diagnostic records. Consequently, researchers have increasingly directed their attention toward intelligent, automated techniques for analyzing dental data. This study therefore surveys and synthesizes the methods that have been applied to the intelligent and automated analysis of such data, highlighting the prevailing trends in current literature.The majority of the examined investigations relied on panoramic radiographic images of the teeth orthopantomograms (OPG) as their primary source material. Three overarching technical objectives repeatedly emerged: first, tooth diagnosis, meaning the reliable separation and identification of each individual tooth from its neighbors; second, sample segmentation, that is, the piece‑by‑piece analysis of visual information within the image; and third, semantic segmentation, namely, the contextual interpretation of information extracted from the radiograph. Depending upon which of these objectives was pursued, researchers selected different neural‑network architectures and configurations. Across the reviewed corpus, input images were typically subjected to preprocessing steps such as normalization, noise reduction, and contrast enhancement before being supplied to a neural network for training, thereby preparing the data for subsequent machine interpretation. In several instances, the raw output produced by the neural network underwent additional post‑processing, a stage designed to refine the preliminary results and enhance overall accuracy. The comparative analysis presented here concentrates on how effectively the various neural‑network models fulfilled the three technical objectives described above. The surveyed articles reveal two dominant analytical approaches. In the intelligent problem‑solving paradigm, convolutional neural networks (CNNs) overwhelmingly predominate. Conversely, in the automated paradigm, investigators favor classical, non‑learning algorithmic techniques. Work employing ANNs consistently emphasizes image comprehension, segmentation, feature extraction, feature classification, network modeling, and careful variable tuning to promote effective learning that aligns with each study’s stated objectives.
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