Vascular proliferations of soft tissues are a diverse and morphologically complex group of lesions that are difficult to diagnose. This report presents a case of oral epithelioid hemangioma, highlighting relevant morphological and immunohistochemical features that could help distinguish this condition from other neoplasms.

Objective: To analyse the automatic classification performance of a convolutional neural network (CNN), Google Inception v3, using tomographic images of odontogenic keratocysts (OKCs) and ameloblastomas (AMs). Methods: For construction of the database, we selected axial multidetector CT images from patients with confirmed AM (n = 22) and OKC (n = 18) based on a conclusive histopathological report. The images (n = 350) were segmented manually and data augmentation algorithms were applied, totalling 2500 images. The k-fold × five cross-validation method (k = 2) was used to estimate the accuracy of the CNN model. Results: The accuracy and standard deviation (%) of cross-validation for the five iterations performed were 90.16 ± 0.95, 91.37 ± 0.57, 91.62 ± 0.19, 92.48 ± 0.16 and 91.21 ± 0.87, respectively. A higher error rate was observed for the classification of AM images. Conclusion: This study demonstrated a high classification accuracy of Google Inception v3 for tomographic images of OKCs and AMs. However, AMs images presented the higher error rate.

Os autores relatam um caso clínico de um cisto dentígero em região anterior de mandíbula, juntamente com suas características clínicas, radiográficas e histológicas e seu tratamento cirúrgico.

No abstract