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.

Atherosclerosis represents the restriction of blood flow in the heart muscle and is one of the main causes of death in the world. The assessment of atherosclerosis is challenging and is currently evaluated by the Fractional Flow Reserve (FFR) and the Quantitative Flow Ratio (QFR). Both exams are based on angiography, which is the gold standard for geometrical assessment. This study presents a pipeline to automatically determine the presence of narrowing in the right coronary artery (RCA) angiography exams, segmenting the artery silhouette, selecting regions of interest (ROIs) followed by a classification model. Initial results suggest a valid sequence of steps to classify the lesion, but require some improvements in the network architecture for better classification accuracy.