Background: Recent advancements in dental implantology have significantly improved outcomes, with success rates of 90–95% over a 10-year period. Key improvements include enhanced preplanning processes, such as precise implant positioning, model selection, and optimal insertion depth. However, challenges remain, particularly in achieving correct spatial positioning and alignment of implants for optimal occlusion. These challenges are pronounced in patients with reduced bone substance or complex anthropometric features, where even minor misalignments can result in complications or defects. Methods: This paper introduces a vision-guided robotic system designed to improve spatial positioning accuracy during dental implant surgery. The system incorporates advanced force-feedback control to regulate the pressure applied to bone, minimizing the risk of bone damage. A preoperative CBCT scan, combined with real-time images from a robot-mounted camera, guides implant positioning. A personalized marker holder guide, developed from the initial CBCT scan, is used for patient–robot calibration. The robot-mounted camera provides continuous visual feedback of the oral cavity during surgery, enabling precise registration of the patient with the robotic system. Results: Initial experiments were conducted on a 3D-printed mandible using a personalized marker holder. Following successful patient–robot registration, the robotic system autonomously performed implant drilling. To evaluate the accuracy of the robotic-assisted procedure, further tests were conducted on 40 identical molds, followed by measurements of implant positioning. The results demonstrated improved positioning accuracy compared to the manual procedure. Conclusions: The vision-guided robotic system significantly enhances the spatial accuracy of dental implants compared to traditional manual methods. By integrating advanced force-feedback control and real-time visual guidance, the system addresses key challenges in implant positioning, particularly for patients with complex anatomical structures. These findings suggest that robotic-assisted implant surgery could offer a safer and more precise alternative to manual procedures, reducing the risk of implant misalignment and associated complications.

Background: Computer-assisted implant surgery (CAIS) aims to improve placement accuracy versus freehand drilling. We compared the three-dimensional accuracy of robot-guided CAIS (r-CAIS), dynamic navigation (d-CAIS), static-template guidance (s-CAIS), and freehand (FH) in clinical and in vitro settings. Methods: We searched PubMed/MEDLINE, Scopus, and Web of Science (1 January 2019–2025). Eligible populations were adults receiving conventional or zygomatic implants in vivo, plus validated in vitro human-jaw models using plan-versus-placement workflows; studies had to report study-level means with dispersion for ≥1 primary outcome with ≥5 implants per arm. Interventions were r-CAIS, d-CAIS, or s-CAIS; with a baseline as the freehand technique. Risk of bias used RoB 2 (RCTs), ROBINS-I (non-randomized clinical), and QUIN (in vitro). Because of heterogeneity in definitions and workflows, we performed a descriptive synthesis by modality (no meta-analysis). Registration: OSF. Results: Forty-three studies (7 RCTs, 10 non-randomized clinical, 26 in vitro) reported more than 4000 implants. Across studies, typical study-level means for global linear deviation clustered around < 1 mm (r-CAIS), ~1 mm (d-CAIS), ~1.3 mm (s-CAIS), and ~1.8 mm (FH). In clinical contexts, d-CAIS often showed slightly lower angular deviation than s-CAIS. Conclusions: CAIS improves accuracy versus freehand. d-CAIS and s-CAIS show similar linear accuracy, with d-CAIS frequently yielding slightly lower angular deviation; r-CAIS exhibits tight error clusters in our dataset, but limited comparative clinical evidence precludes superiority claims. Limitations: non-uniform registration/measurement, variable operator experience, and absence of meta-analysis.

Background: Accurate and stable instrument positioning is critical in dental implant procedures, particularly in anatomically constrained regions. Conventional navigation systems assume a static patient head, limiting adaptability in dynamic surgical conditions. This study proposes and validates a real-time motion compensation framework that integrates optical motion tracking with a collaborative robot to maintain tool alignment despite patient head movement. Methods: A six-camera OptiTrack Prime 13 system tracked rigid markers affixed to a 3D-printed human head model. Real-time head pose data were streamed to a Kuka LBR iiwa robot, which guided the implant handpiece to maintain alignment with a predefined target. Motion compensation was achieved through inverse trajectory computation and second-order Butterworth filtering to approximate realistic robotic response. Controlled experiments were performed using the MAiRA Pro M robot to impose precise motion patterns, including pure rotations (±30° at 10–40°/s), pure translations (±50 mm at 5–30 mm/s), and combined sinusoidal motions. Each motion profile was repeated ten times to evaluate intra-trial repeatability and dynamic response. Results: The system achieved consistent pose tracking errors below 0.2 mm, tool center point (TCP) deviations under 1.5 mm across all motion domains, and an average latency of ~25 ms. Overshoot remained minimal, with effective damping during motion reversal phases. The robot demonstrated stable and repeatable compensation behavior across all experimental conditions. Conclusions: The proposed framework provides reliable real-time motion compensation for dental implant procedures, maintaining high positional accuracy and stability in the presence of head movement. These results support its potential for enhancing surgical safety and precision in dynamic clinical environments.

Background/Objectives: Dynamic navigation and robot-assisted implant workflows depend on robust intraoral perception. Marker-based tracking introduces workflow complexity and is sensitive to occlusions, motivating markerless alternatives. This study evaluates whether a single-stage YOLO instance segmentation model (YOLO-seg) can provide a practical markerless perception layer for dental navigation, combining accurate per-tooth delineation with low, predictable inference latency. Methods: YOLO-seg was trained end to end on an intraoral RGB corpus of 400 training, 20 validation, and 100 testing images, combining a public source and a partner-hospital in-house set. A two-stage YOLO + SAM baseline was implemented for comparison. Segmentation quality was evaluated on a 50-image held-out clinical test set at three complementary levels (per-instance matching, per-class union, and global union), with paired Wilcoxon signed-rank tests, Cliff’s delta effect sizes, and 95% bootstrap confidence intervals. Runtime was assessed under matched inference-only and end-to-end conditions on N = 100 frames at a 640 × 640 resolution on an NVIDIA RTX A2000 GPU. Results: YOLO-seg significantly outperformed YOLO + SAM across all primary metrics, with very large effect sizes (Cliff’s delta: 0.76–0.94; Wilcoxon p < 10⁻⁸ on every metric except precision at IoU ≥ 0.5). YOLO-seg reached AP₅₀ = 0.716 and recall = 0.973 versus 0.383 and 0.398 for YOLO + SAM. Under matched inference-only timing, YOLO-seg ran at 27.08 ms per frame (36.9 FPS) versus 1302.78 ms (0.77 FPS), an approximately 48-fold latency gap intrinsic to the two-stage forward pass. Conclusions: YOLO-seg shows strong potential as a 2D perception module for dental navigation, balancing per-instance segmentation fidelity with real-time feasibility under the tested conditions. These results support its use as a 2D perception front-end for future integration with stereo-based 3D reconstruction and robot-assisted navigation; 3D registration accuracy, implant-placement error, and robotic execution remain outside the scope of the present study.