Understanding the Interaction of Röntgen Radiation Employed in Computed Tomography/Cone Beam Computed Tomography Investigations of the Oral Cavity by Means of Surface-Enhanced Raman Spectroscopy Analysis of Saliva.

The use of Raman spectroscopy, particularly surface-enhanced Raman spectroscopy (SERS), offers a powerful tool for analyzing biochemical changes in biofluids. This study aims to assess the modifications occurring in saliva collected from patients before and after exposure to cone beam computed tomography (CBCT) and computed tomography (CT) imaging. SERS analysis revealed significantly amplified spectra in post-imaging samples compared to pre-imaging samples, with pronounced intensification of thiocyanate and opiorphin bands, which, together with proteins, dominated the spectra.

Condylar Parameters and Mandibular Movement Patterns in Bruxers Using an Optical Jaw Tracking System.

: Eccentric bruxism is a complex parafunctional activity that involves grinding of teeth and occurs more frequently during sleep. This study aimed to assess differences in condylar parameters (sagittal condylar inclination -SCI and Bennett angle -BA) and mandibular and condylar kinematics during functional and parafunctional movements in bruxers and non-bruxers and to assess a digital method for quantifying eccentric bruxism using an optical jaw tracking system (Modjaw). : The study group included subjects diagnosed with eccentric bruxism according to validated clinical diagnostic criteria.

A Vision-Guided Robotic System for Safe Dental Implant Surgery.

Recent advancements in dental implantology have significantly improved outcomes, with success rates of 90-95% over a 10-year period. Key improvements include enhanced preplanning processes, such as precise implant positioning, model selection, and optimal insertion depth. However, challenges remain, particularly in achieving correct spatial positioning and alignment of implants for optimal occlusion.

Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities.

The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, with a focus on identifying teeth at risk prior to radiotherapy. A YOLOv8 model was trained on a dataset of 1628 annotated panoramic radiographs and externally validated on 180 radiographs from multiple centers.