DIGITAL TECHNOLOGIES IN DENTISTRY: COMPREHENSIVE ADVANCES IN DIAGNOSTICS, TREATMENT PLANNING, AND PROSTHETIC REHABILITATION

Аннотация

Digital technologies have become indispensable in modern dentistry, facilitating unprecedented improvements in diagnostic precision, treatment planning, and prosthetic fabrication. This review provides a comprehensive analysis of the current state and future prospects of digital dentistry, focusing on artificial intelligence (AI) applications in diagnostics, three-dimensional (3D) data acquisition through laboratory and intraoral scanning systems, computer-aided design and manufacturing (CAD/CAM) workflows, and the integration of augmented and virtual reality (AR/VR) technologies. Technical principles, clinical applications, advantages, and limitations are critically examined. The challenges of cost, training, data interoperability, and clinical implementation are discussed, alongside emerging innovations that promisetofurther enhancepatient care and clinical efficiency. Keywords: digital dentistry, artificial intelligence, intraoral scanner, laboratory scanner, blue light scanning, CAD/CAM, prosthetic dentistry, augmented reality, virtual reality, dental diagnostics, 3Dimaging, digital workflow. Introduction. The landscape of dental practice has undergone a profound transformation driven by the emergence and maturation of digital technologies. Traditional dental workflows, characterized by manual impression taking, subjective diagnostic interpretation, and labor-intensive prosthetic fabrication, are increasingly supplanted by digital processes that optimize accuracy, reproducibility, and efficiency. This transformation is propelled by advances in artificial intelligence (AI), optical and laser-based scanning technologies, computer-aided design and manufacturing (CAD/CAM), and immersive visualization tools such as augmented reality (AR) and virtual reality (VR). Artificial intelligence has revolutionized diagnostic dentistry by enabling automated analysis of complex clinical data. Machine learning algorithms, particularly convolutional neural 98,7:ROTCAFTCAPMI.SECNEICSLACIDEM ,5202 , yluJ,5emuloV gro.srehsilbupcimedaca.www networks (CNNs), have demonstrated remarkable capability in interpreting radiographic images, including periapical, bitewing, panoramic, and cone-beam computed tomography (CBCT) scans. These networks are trained on large, annotated datasets to identify subtle radiolucencies indicative of carious lesions, periapical pathologies, cystic formations, and neoplastic changes. Beyond detection, AI systems can quantify lesion dimensions, assess bone density, and even predict disease progression based on longitudinal data. Such capabilities not only reduce inter- and intra-observer variability but also augment clinician decision-making byproviding objective, reproducible assessments. The integration of AI into diagnostic workflows extends to risk assessment and treatment planning. By analyzing patient demographics, medical history, genetic predispositions, and behavioral factors, AI models generate individualized risk profiles for dental caries, periodontal disease, and implant failure. This predictive analytics approach supports personalized preventive strategies and optimized therapeutic interventions, aligning with the broader trend towards precision medicine in dentistry. However, the clinical adoption of AI tools is tempered by challenges including data heterogeneity, algorithm transparency, and regulatory considerations. The need for standardized datasets, explainable AI models, and robust validationstudies remains paramount toensure safe and effectiveimplementation. Discussion. Accurate three-dimensional (3D) data acquisition is a cornerstone of digital dentistry, enabling precise anatomical mapping essential for diagnosis, treatment planning, and prosthetic design. Laboratory scanners digitize physical dental casts or impressions, employing technologies such as structured light projection, laser triangulation, and contact probing. Among these, structured blue light scanners have gained prominence due to their superior spatial resolution, reduced susceptibility to ambient light interference, and rapid data capture rates. The shorter wavelength of blue light (~450 nm) allows for finer surface detail detection compared to traditional white light or infrared laser scanning. This enhanced resolution is critical when fabricating restorations with intricate morphology or when planningimplant placement inanatomically complex regions. Despite their high accuracy, laboratory scanners necessitate physical impressions or models as an initial step, which introduces potential errors related to impression distortion, material shrinkage, and handling. The digitization of these models, while precise, cannot fully compensate for inaccuracies introduced earlier in the workflow. Moreover, the time and labor involved in impression takingand model fabrication maylimitefficiency gains. 98,7:ROTCAFTCAPMI.SECNEICSLACIDEM ,5202 , yluJ,5emuloV gro.srehsilbupcimedaca.www Intraoral scanners (IOS) have revolutionized i