Artificial Intelligence and Data-Driven Innovations in Smart Oral Healthcare and Dental Imaging Systems: A Systematic Literature Review
Dental imaging; Cone-beam computed tomography; Deep learning; CBCT segmentation; Digital dentistry
AuthorsAbstractThe technologies of artificial intelligence and data-driven have turned out to be instrumental in transforming the smart oral healthcare and dental imaging systems, improving diagnostic accuracy, simplifying workflow, and enabling digital treatment planning. The objective of this systematic literature review was to assess the use, performance and clinical relevance of AI driven innovations for dental imaging and smart oral care systems. The key issues which were examined were cone-beam computed tomography segmentation, automated dental imaging analysis, multimodal image fusion, creation of virtual patient and AI-assisted implant-planning systems. Systematic Search Strategy was performed based on PubMed and other scientific sources according to PRISMA framework. The following criteria were used to include the studies: An original research study, validation research study, comparative research study, systematic review or meta-analysis that have reported on the application of AI in dentistry or oral radiology. The number of studies for the qualitative synthesis was 10. The results showed that deep learning and CNN models performed high segmentation accuracy, faster processing time and better workflow reproducibility and minimised operator dependency. Multimodal integration, such as CBCT, intraoral scanning and facial imaging, further enhanced the creation of comprehensive digital dentistry ecosystems. However, due to the mixed nature of the datasets, explainability, standardization and regulatory issues are yet major hurdles to achieve broad implementation. Overall, the technologies developed with AI have significant promise in the future for supporting intelligent, personalized, and data-driven smart oral health systems.
1. Introduction Smart oral healthcare is the result of the accelerated shift of traditional dentistry towards digitally empowered, data informed and technology assisted clinical practice. Digital dentistry has become a combination of imaging systems, intraoral scanning, artificial intelligence, automated diagnostic systems, and virtual treatment planning to enhance clinical accuracy and patient-centered care. AI has gained significance as a part of this change as it aids with diagnosis, prediction, treatment planning, and workflow automation in a variety of dental disciplines (Ahmed et al., 2021; Khanagar et al., 2021). Predominantly, previous digital and diagnostic research also suggests that the dental practice becomes more reliant on high quality imaging, automated interpretation, and computer-assisted decision making systems (Hung et al., 2020; Kim et al., 2020). Cone-beam computed tomography has emerged as one of the most significant imaging modalities in contemporary oral health care due to the ability of the modality to give three dimensional visualization of the teeth, alveolar bone, jaws, airway structures, and maxillofacial structures. CBCT has also been extensively applied in orthodontics, implantology, oral surgery, endodontics and restorative planning due to its ability to provide an anatomical ••••••••••••••••••••••••••••••••EJPRD
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