L-PRF Versus Sticky Bone Grafting of the Jumping Gap in AI-assisted Computer-Guided Socket Shield Immediate Implantation

the rationale of the current study is to address a focused and clinically relevant gap in socket shield therapy: which biologic modality best supports healing of the shield-implant gap when SST is performed under a standardized, digitally guided workflow. The study will compare three shield-implant gap filling modalities: (i) L-PRF alone (without membrane), (ii) sticky tooth (autogenous dentin graft +i- PRF), and (iii) sticky bone (particulate graft + i- PRF) under AI-assisted, patient-specific guided implant placement based on IOS/CBCT superimposition, with CBCT follow-up at immediate, 3 months, and 6 months. The working hypothesis is that biologically active, cohesive composites (sticky tooth and sticky bone) will provide superior hard- and soft-tissue dimensional stability compared with PRF alone by improving space maintenance and early wound stability in the shield-implant gap . The null hypothesis is that there will be no statistically significant differences between the three modalities in radiographic and digitally assessed clinical outcomes over the 6-month follow-up period .

The socket shield technique (SST), a form of partial extraction therapy, was introduced to address the facial-bundle-bone problem by intentionally retaining a buccal root fragment with its periodontal ligament to help maintain blood supply and support the facial plate. Among biologic options for filling peri-implant gaps, platelet-rich fibrin (PRF) is attractive because it is autologous, inexpensive, and provides a fibrin scaffold with a sustained release of growth factors that may support angiogenesis and early tissue maturation. At the cellular and molecular level, De Bruyn et al. (2024) demonstrated that leukocyte- and platelet-rich fibrin contains a complex cellular ecosystem and growth factor kinetics that can plausibly support regenerative wound healing, strengthening the biologic rationale for using L-PRF in challenging healing environment. A recent systematic review in Minerva Dental and Oral Science (2024/2025) also concluded that PRF may aid peri implant gap fill and soft-tissue healing in immediate implant contexts, but called for better standardized trials and clearer indications. Beyond PRF alone, two "composite" concepts have gained attention: sticky tooth and sticky bone. Sticky tooth generally refers to autogenous tooth-derived dentin granules combined with platelet concentrates to create a cohesive graft, leveraging dentin's mineral and collagen composition and its proposed bioactivity. In a histologic evaluation, van Orten et al. (2022) described tooth-derived granules mixed with PRF ("sticky tooth") as a feasible socket preservation approach with histologic evidence of graft integration patterns consistent with bone remodeling, supporting its potential as an autogenous biomaterial with low immunologic risk. Separately, tooth-derived grafts have been increasingly investigated as alternatives to xenografts/allografts, yet protocols vary widely in processing, particle size, sterilization, and mixing with blood products-creating a knowledge gap regarding standardization and reproducibility across centers. Sticky bone most commonly refers to particulate bone substitute (often xenograft or allograft) combined with injectable PRF (i-PRF) to form a moldable, fibrin-rich mass intended to improve handling, stabilize particles, and potentially enhance early biologic activity. In a randomized parallel-arm clinical trial, Tony et al. (2022) assessed sticky bone for horizontal ridge augmentation using CBCT outcomes, supporting that i-PRF-based composites can be evaluated quantitatively and may influence hard-tissue results depending on adjunctive membrane use. Collectively, these findings suggest a strong rationale to test whether sticky tooth and sticky bone can serve as optimized fillers for the shield-implant gap in SST-where space maintenance, clot stability, and early vascularization may be decisive. High-quality assessment of subtle contour changes requires accurate, reproducible measurement methods. Contemporary digital workflows combine CBCT with intraoral scanning (IOS) to enable three-dimensional planning, guided surgery, and longitudinal volumetric evaluation. A key technical step is accurate CBCT-IOS registration and superimposition. In a systematic review and meta-analysis, Zheng et al. (2025) concluded that automatic multimodal registration methods-particularly those incorporating AI-have improved efficiency and robustness, but performance can still be affected by landmark instability, artifact burden, and dataset diversity, indicating a need for clinically validated workflows in implant planning and follow-up. Similarly, Flügge et al. (2017) demonstrated that digital model registration accuracy is clinically relevant and must be managed carefully to avoid propagating errors into guided surgery and outcome assessment, supporting strict standardization in trials that use superimposition as a primary endpoint. Within SST specifically, Zhang et al. (2020) introduced the clinical concept of guided residual root preparation to reduce technique sensitivity and improve repeatability of shield preparation, indicating that guided approaches may address one of SST's main drawbacks-operator variability. Artificial intelligence is increasingly integrated into implant dentistry, particularly in image segmentation, automated registration, planning support, and outcome prediction. Altalhi et al. (2023) summarized current and emerging AI applications in implantology, including planning precision and the movement toward more automated, data-driven workflows, while also cautioning that validation and transparency are essential before widespread clinical dependence. In a foundational perspective, Schwendicke et al. (2020) argued that AI in dentistry offers clear opportunities in decision support and automation, but requires high-quality training data, rigorous evaluation, and careful governance to ensure safe clinical translation . In the context of the present work, AI-assisted planning after IOS superimposition could strengthen the standardization of implant positioning and patient-specific guide fabrication, thereby reducing confounding variability and allowing a clearer comparison of biologic gap-filling materials