BACKGROUND: It is impossible to use the routine skeletal parts for gender identification if the skeleton of unknown human remains is obtained in a fragmented and incomplete state. The alternative is to use other parts of the skeleton for gender ident...
OBJECTIVES: The aim of this study was to compare the diagnostic performance of a deep learning algorithm with that of radiologists in diagnosing maxillary sinusitis on Waters' view radiographs.
International journal of computer assisted radiology and surgery
32676871
PURPOSE: The analysis of the maxillary sinus (MS) can provide an assessment for many clinical diagnoses, so accurate CT image segmentation of the MS is essential. However, common segmentation methods are mainly done by experienced doctors manually, a...
OBJECTIVE: The first aim of this study was to determine the performance of a deep learning object detection technique in the detection of maxillary sinuses on panoramic radiographs. The second aim was to clarify the performance in the classification ...
THE AIM OF THE STUDY: Was to compare manual, semi-automatic and automatic methods for determining the maxillary sinus volume using cone beam computed tomography (CBCT).
This study was conducted to develop a convolutional neural network (CNN)-based model to predict the sex and age of patients by identifying unique unknown features from paranasal sinus (PNS) X-ray images.We employed a retrospective study design and us...