Title: The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies.
Objectives: This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.
Background: According to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), scientific research data should be findable, accessible, interoperable, and reusable. The COVID-19 pandemic has led to massive research activities and an unprecedented number of topical publications in a short time. However, no evaluation has assessed whether this COVID-19-related research data has complied with FAIR principles (or FAIRness).
View Article and Find Full Text PDFBackground: Over the past decade, the adoption of screening programs, digital mammography, and magnetic resonance imaging (MRI) has increased early-stage breast cancer diagnosis rates. Mortality rates have decreased due to early detection and improved treatments, including personalized therapies. Accelerated partial-breast irradiation (APBI) is emerging as a convenient and effective treatment for some patients, with studies exploring its preoperative use.
View Article and Find Full Text PDFBackground/aim: To evaluate the efficacy of the combined cone-beam (CBCT)/3D-replicas protocol on the clinical and radiographic outcomes of autotransplanted molars.
Material And Methods: Controlled clinical trial registered ISRCTN13563091 from August 2019 to September 2022. Patients aged 13-22 years requiring permanent premolar extraction and having at least one non-erupted third molar were enrolled at the Institute of Stomatology, Stradins University, Riga, Latvia.