Background: Affected third molar extractions are some of the most commonly done dentoalveolar surgeries and yet traditional methods have significant complication rates such as inferior alveolar nerve (IAN) injury, excess bleeding, and prolonged healing. Trajectory planning by means of artificial intelligence (AI) coupled with robotic surgical support has provided a paradigm shift towards precision-guided oral surgery.
Purpose: To determine the clinical effectiveness of the pre-operative AI trajectory planning and robot-assisted extraction of affected third molars, evaluation of surgical accuracy, intraoperative blood loss, post-surgery recovery, and comorbidity rates, and cost benefit analysis to adopt the AI implementation in the Indian dental clinic.
Materials and Methods: The study is a prospective study, which involved 25 participants who were to be surgically extracted of impacted mandibular third molars (Pell and Gregory Class II/III, Position B/C) at a tertiary dental center in India between January 2024 and December 2024. The pre-operative cone-beam computed tomography (CBCT) images were simulated using a commercially available AI trajectory planning module. Extractions were done with the help of robots and compared to institutional historical controls (n=25) to which the conventional methods were applied. The main outcomes were off-track recording (mm), intraoperative blood loss (mL), operative time (min), postoperative pain (VAS), swelling, trismus, injury rate of nerve and wound healing score at 1 and 4 weeks. The clinic perspective of the cost-benefit analysis was undertaken.
Results: The mean trajectory difference in the AI-robotic assisted cases was 0.42 + 0.18 mm. (p = 0.001). The intraoperative blood loss was much reduced (18.6 + 6.2 mL vs. 42.3 + 12.8 mL). The time of operation was also similar (28.4 ± 5.1 min vs. 26.1 ± 7.3 min; p=0.21). There were significant differences in the postoperative VAS pain levels at 24 hours (3.1 + 1.2 vs. 5.4 + 1.6; p= 0.001). There were no IAN injuries in AI-robotic group and 2 (8%) in controls. The cost-benefit analysis showed that the break-even point was possible in 18-24 months in the case of high-volume Indian clinics.
Conclusion: AI trajectory planning during pre-operation with the aid of robots is associated with a significant risk of complications, blood loss, and postoperative morbidity reduction in the complex impacted third molar surgery. The technology proves to be economical in the case of Indian clinics that have sufficient case volumes.
Keywords: Artificial intelligence; robotic surgery; impacted third molar; trajectory planning; oral surgery; and cost-benefit analysis and India.
How to cite this article: Mishra S, Nayak BB, Mohanty S, Samanta A, Reducing Complications in Impacted Third Molar Surgery Through Pre-Operative AI Trajectory Planning and Robotic Assistance: A 25-Case Prospective Study With Cost-Benefit Analysis for Indian Clinics. Int J Drug Deliv Technol. 2026;16(7s): 26-34; DOI: 10.25258/ijddt.16.7s.5
Source of support: Nil
Conflict of interest: None