Classification of Periodontal and Health Oral Samples Using Deep Learning

Periodontal disease is common among older adults, with around 50% of all adults aged 30 years and older having some form of gum disease. Although the disease might seem minor, without early or proper diagnosis, the disease can affect all supporting structures of the teeth, including gums, periodontal ligament, and alveolar bone. This research can help find patterns common among people with periodontal disease and possibly prevent the effects of it in the elderly. Here, we explored the deep learning capabilities of convolutional neural networks (CNN) and VGG16 models for detecting disease-associated patterns derived from 16S rRNA gene sequences of plaque samples. The CNN model achieved a 90.2% accuracy for differentiating periodontal disease from health samples, with 76% precision and 86% recall. This model could lead to earlier detection and more effective treatment of periodontal disease, ultimately improving patients’ dental health outcomes and quality of life.