Recent Advances in Intraoral Scanners.

Abstract

Intraoral scanners (IOSs) have emerged as a cornerstone technology in digital dentistry. This article examines the recent advancements and multifaceted applications of IOSs, highlighting their benefits in patient care and addressing their current limitations. The IOS market has seen a competitive surge. Modern IOSs, featuring continuous image capture and advanced software for seamless image stitching, have made the scanning process more efficient. Patient comfort with IOS procedures is favorable, mitigating the discomfort associated with conventional impression taking. There has been a shift toward open data interfaces, notably enhancing interoperability. However, the integration of IOSs into large dental institutions is slow, facing challenges such as compatibility with existing health record systems and extensive data storage management. IOSs now extend beyond their use in computer-aided design and manufacturing, with software solutions transforming them into platforms for diagnostics, patient communication, and treatment planning. Several IOSs are equipped with tools for caries detection, employing fluorescence technologies or near-infrared imaging to identify carious lesions. IOSs facilitate quantitative monitoring of tooth wear and soft-tissue dimensions. For precise tooth segmentation in intraoral scans, essential for orthodontic applications, developers are leveraging innovative deep neural network-based approaches. The clinical performance of restorations fabricated based on intraoral scans has proven to be comparable to those obtained using conventional impressions, substantiating the reliability of IOSs in restorative dentistry. In oral and maxillofacial surgery, IOSs enhance airway safety during impression taking and aid in treating conditions such as cleft lip and palate, among other congenital craniofacial disorders, across diverse age groups. While IOSs have improved various aspects of dental care, ongoing enhancements in usability, diagnostic accuracy, and image segmentation are crucial to exploit the potential of this technology in optimizing patient care.