Artificial intelligence serving pre-surgical digital implant planning: A scoping review

• Published in: Journal of dentistry 2024-04, Vol.143, p.104862, Article 104862
• Main Authors: Elgarba, Bahaaeldeen M., Fontenele, Rocharles Cavalcante, Tarce, Mihai, Jacobs, Reinhilde
• Format: Article
• Language: English
• Subjects: 3D imaging; Artificial Intelligence; Cone-beam computed tomography; Deep learning; Dental implant; Dental Implantation, Endosseous - methods; Dental Implants; Humans; Imaging, Three-Dimensional - methods; Implant dentistry; Software
• ISSN: 0300-5712; 1879-176X; 1879-176X
• DOI: 10.1016/j.jdent.2024.104862

To conduct a scoping review focusing on artificial intelligence (AI) applications in presurgical dental implant planning. Additionally, to assess the automation degree of clinically available pre-surgical implant planning software. A systematic electronic literature search was performed in five databases (PubMed, Embase, Web of Science, Cochrane Library, and Scopus), along with exploring gray literature web-based resources until November 2023. English-language studies on AI-driven tools for digital implant planning were included based on an independent evaluation by two reviewers. An assessment of automation steps in dental implant planning software available on the market up to November 2023 was also performed. From an initial 1,732 studies, 47 met eligibility criteria. Within this subset, 39 studies focused on AI networks for anatomical landmark-based segmentation, creating virtual patients. Eight studies were dedicated to AI networks for virtual implant placement. Additionally, a total of 12 commonly available implant planning software applications were identified and assessed for their level of automation in pre-surgical digital implant workflows. Notably, only six of these featured at least one fully automated step in the planning software, with none possessing a fully automated implant planning protocol. AI plays a crucial role in achieving accurate, time-efficient, and consistent segmentation of anatomical landmarks, serving the process of virtual patient creation. Additionally, currently available systems for virtual implant placement demonstrate different degrees of automation. It is important to highlight that, as of now, full automation of this process has not been documented nor scientifically validated. Clinical significance: Scientific and clinical validation of AI applications for presurgical dental implant planning is currently scarce. The present review allows the clinician to identify AI-based automation in presurgical dental implant planning and assess the potential underlying scientific validation.