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2025, Number 6

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Rev ADM 2025; 82 (6)

The application of artificial intelligence in diagnosing periodontal disease: advances and perspectives.

García-Vega MF, García-Arévalo F, Flores PJC, González-Rascón A

Language: Spanish
References: 20
Page: 335-341
PDF size: 943.88 Kb.


ABSTRACT

Periodontitis is a disease that affects the global population and is characterized as a chronic inflammatory condition that compromises the patient's overall health. It is associated with both local and systemic factors. Artificial intelligence (AI) has proven to be a powerful tool for detecting periodontal disease through deep learning models such as convolutional neural networks (CNNs). This review examines the use of AI in dental imaging analysis, particularly through architectures like U-Net, ResNet, and hybrid models such as HYNETS, which allow for the segmentation of anatomical structures in radiographs and Cone Beam Computed Tomography (CBCT) to accurately classify bone loss and the stages of periodontitis. Among the main benefits are high diagnostic accuracy and significant advancements in the classification of periodontitis stages, supporting clinicians in optimizing clinical decisions, standardizing diagnosis, and reducing interprofessional variability. This literature review aims to analyze the application of deep learning and CNNs in the diagnosis and treatment of periodontal disease, highlighting their potential to enhance diagnostic precision, reduce clinical errors, and improve patient care through the integration of advanced technological tools.


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