Objectives: Artificial intelligence has generated a significant impact in the health field. The aim of this study was to perform the training and validation of a convolutional neural network (CNN)-based model to automatically classify six clinical representation categories of oral lesion images. Method: The CNN model was developed with the objective of automatically classifying the images into six categories of elementary lesions: (1) papule/nodule; (2) macule/spot; (3) vesicle/bullous; (4) erosion; (5) ulcer and (6) plaque. We selected four architectures and using our dataset we decided to test the following architectures: ResNet-50, VGG16, InceptionV3 and Xception. We used the confusion matrix as the main metric for the CNN evaluation and discussion. Results: A total of 5069 images of oral mucosa lesions were used. The oral elementary lesions classification reached the best result using an architecture based on InceptionV3. After hyperparameter optimization, we reached more than 71% correct predictions in all six lesion classes. The classification achieved an average accuracy of 95.09% in our dataset. Conclusions: We reported the development of an artificial intelligence model for the automated classification of elementary lesions from oral clinical images, achieving satisfactory performance. Future directions include the study of including trained layers to establish patterns of characteristics that determine benign, potentially malignant and malignant lesions.

Objective: To determine the oral health conditions of the elderly patients hospitalized and restricted to the bed and to correlate with the development of infections at a distance, especially pneumonia. To verify if the daily follow-up of the dental surgeon improves oral health and reduces the occurrence of respiratory infection in hospitalized elderly patients. Methods: Quasi-experimental study with hospitalized and bed restricted elderly, two simple randomized groups, one group was evaluated and monitored daily by the dental surgeon, the other group was evaluated and received guidance on health care and oral hygiene and of dental prostheses. Results: Both groups presented poor health and oral hygiene and prosthetic conditions. There was improvement in the oral health conditions of both groups. Seven percent of the patients developed pneumonia during the period of hospital stay and the oral health of these patients were considered regular or bad. Conclusion: The performance of the dental surgeon promoted improvements in the oral health of both groups in the first evaluation, with no statistical difference between the group that received daily follow-up and the group that received guidelines, so it was concluded that oral health care should be a behavior of the team multidisciplinary, dentistry should intervene if the changes interfere in the clinical condition of the patient and offer training and support to the multidisciplinary team that is in the first line of care with hospitalized patients.