Robotic-assisted dental implant surgery (r-CAIS) is setting a new benchmark in precision, outperforming conventional freehand methods as well as static (s-CAIS) and dynamic (d-CAIS) computer-assisted techniques.
These findings come from a systematic review and meta-analysis led by researchers at Xi’an Jiaotong University Hospital of Stomatology in China.
Their study has the potential to transform the future of dental implant procedures.
Drawing on two decades of clinical and in vitro studies, the research team conducted a thorough evaluation that redefines accuracy standards in dental surgery.
This pivotal analysis, published in the Journal of Oral Biology and Craniofacial Research, included data from PubMed, Google Scholar, Semantic Scholar and Cochrane Library, covering studies from January 2000 to January 2024.
Researchers applied strict inclusion criteria (PICOST) to select relevant studies. They analysed data from 134 implant models and 100 patients with partially or fully edentulous arches, focusing on deviations in implant placement at coronal, apical and angular levels.
The meta-analysis incorporated eight studies (four in vitro and four in vivo), with two from each category demonstrating a low risk of bias.
Robotic systems significantly improved placement accuracy across all parameters compared to freehand, s-CAIS, and d-CAIS techniques.
Robotic-assisted systems outperformed traditional methods, achieving reduced deviations in implant placement.
The YOMI robotic system, approved by the FDA in 2017, exemplifies the capabilities of robotic-assisted surgery.
YOMI uses a passive robotic arm with haptic and audio-visual guidance to constrain movements during the procedure, ensuring unparalleled precision.
Task-autonomous robots like Yekebot and Remebot are also gaining traction. These robots can execute pre-planned implant procedures under surgeon supervision.
Despite its advantages, robotic-assisted dental implant surgery faces hurdles.
Most data come from controlled environments, with fewer clinical validations factoring in real-world challenges like patient movement, blood, and saliva.
The steep price of robotic systems also poses a barrier to widespread adoption in routine clinical settings.
The researchers call for future studies to focus on cost-effectiveness and large-scale clinical trials to validate these findings further.
