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Impact of artificial intelligence-based automated segmentation on the accuracy of robotic computer-aided implant
Ningbo Zhao1, Rongrong Zhu2, Jianfei Liang3
1Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China; Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Disease, College of Stomatology, Xi'an Jiaotong University, Xi'an, China; Department of Advanced Implant Dentistry, College of Stomatology, Xi'an Jiaotong University, Xi'an, China; Department of Oral Surgery and Stomatology, School of Dental Medicine, University of Bern, Bern, Switzerland.
Objectives:
This retrospective study aimed to evaluate the impact of artificial intelligence (AI)-based automated segmentation (AS) on the accuracy of robotic computer-aided implant surgery (r-CAIS).
Methods:
Patients who underwent r-CAIS were enrolled. Preoperative CBCT images were segmented using either threshold-based SS approach (SS group) or the AI-based AS approach (AS group). Osteotomy preparation and implant placement were performed using a semi-active robot. Registration errors and deviations between planned and postoperatively placed implant positions were calculated. Intergroup comparisons were performed using Wilcoxon Mann-Whitney U tests, with P < 0.05 considered significant.
Results:
A total of 140 patients (79 females, 61 males) receiving 224 implants were included (SS group: 69 patients, 107 implants; AS group: 71 patients, 117 implants). The registration error in the AS group (0.10 ± 0.04 mm) was significantly lower than that in the SS group (0.12 ± 0.04 mm) (P < 0.05). The AS group demonstrated lower deviations at the platform (0.51 ± 0.16 vs.0.56 ± 0.18 mm), apex (0.60 ± 0.23 vs. 0.66 ± 0.23 mm), and angulation (1.13 ± 0.59° vs. 1.19 ± 0.53°), with a statistically significant difference observed in global platform deviation (P < 0.05).
Conclusions:
r-CAIS demonstrated consistently high accuracy, which was further improved by AI-based AS. This improvement may be associated with enhanced registration precision achieved through AI-based AS.
Clinical Significance:
These findings support the potential clinical application of AI-based AS in r-CAIS.
