Convolutional neural network misclassification analysis in oral lesions: an error evaluation criterion by image characteristics.

Objective: This retrospective study analyzed the errors generated by a convolutional neural network (CNN) when performing automated classification of oral lesions according to their clinical characteristics, seeking to identify patterns in systemic errors in the intermediate layers of the CNN.

Study Design: A cross-sectional analysis nested in a previous trial in which automated classification by a CNN model of elementary lesions from clinical images of oral lesions was performed. The resulting CNN classification errors formed the dataset for this study.

Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images.

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.

Functional Coronary Artery Assessment: a Systematic Literature Review.

Cardiovascular diseases represent the number one cause of death in the world, including the most common disorders in the heart's health, namely coronary artery disease (CAD). CAD is mainly caused by fat accumulated in the arteries' internal walls, creating an atherosclerotic plaque that impacts the blood flow functional behavior. Anatomical plaque characteristics are essential but not sufficient for a complete functional assessment of CAD.