OBJECTIVES: The aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)-odontogenic keratocysts, dentigerous cysts, and periapical cysts-using dental panoramic radiography and cone beam...
OBJECTIVES: The purpose of this study was to automatically diagnose odontogenic cysts and tumors of both jaws on panoramic radiographs using deep learning. We proposed a novel framework of deep convolution neural network (CNN) with data augmentation ...
International journal of computer assisted radiology and surgery
33547985
PURPOSE: The differentiation of the ameloblastoma and odontogenic keratocyst directly affects the formulation of surgical plans, while the results of differential diagnosis by imaging alone are not satisfactory. This paper aimed to propose an algorit...
Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
37157075
Objective To evaluate the accuracy of different convolutional neural networks (CNN),representative deep learning models,in the differential diagnosis of ameloblastoma and odontogenic keratocyst,and subsequently compare the diagnosis results between m...
Oral surgery, oral medicine, oral pathology and oral radiology
38845306
OBJECTIVE: To evaluate the diagnostic capability of artificial intelligence (AI) for detecting and classifying odontogenic cysts and tumors, with special emphasis on odontogenic keratocyst (OKC) and ameloblastoma.
BACKGROUND: The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near simil...
Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
38807455
BACKGROUND: The purpose of this systematic review (SR) is to gather evidence on the use of machine learning (ML) models in the diagnosis of intraosseous lesions in gnathic bones and to analyze the reliability, impact, and usefulness of such models. T...
Journal of stomatology, oral and maxillofacial surgery
38719192
This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Beam Computer Tomography (CBCT) images and inflammatory biomarkers for distinguishing between Dentigerous Cysts (...
OBJECTIVES: Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image fea...
BACKGROUND: The study's objective was to develop an automated method for a histopathology recognition model for odontogenic keratocysts (OKC) and non-keratocyst (Non-KC) in jaw cyst sections stained with hematoxylin (H) and eosin (E) on a tiny bit of...