Evaluating CT/xray imaging-correlated impedance-based metrics for predictive surgical osseointegration and injury prevention

Rytek Medical Inc. received a $220,000 Project Grant from the National Institute of Dental and Craniofacial Research (NIDCR) under the Oral Diseases and Disorders Research program (CFDA 93.121), awarded on August 1, 2025, with a completion date of July 31, 2027. The grant supports the development and evaluation of computed tomography (CT) and X-ray imaging-correlated impedance-based metrics designed to predict surgical osseointegration success and prevent injury during dental implant placement procedures. This technology addresses a significant clinical gap in current practice, where cone beam CT (CBCT) imaging limitations result in approximately 10-20% iatrogenic injury rates to critical anatomical structures such as nerves and sinuses during implant surgery.

The project delivers an advanced diagnostic technology solution that leverages electrical impedance measurements correlated with medical imaging to improve bone quality characterization at implant sites, particularly for trabecular cancellous bone assessment. By providing more accurate preoperative bone integrity evaluation and real-time surgical guidance metrics, the technology aims to enhance implant selection, reduce surgical complications, and improve long-term osseointegration outcomes. This development is particularly relevant given the projected 23% increase in dental implant demand through 2026, with significant growth anticipated among elderly populations (65-74 years) who experience greater bone quality variability and associated surgical risks.

Abstract

Clinical importance: almost 15 million dental implants were placed worldwide in 2021, with 3 million of these procedures taking place in the united states. Implants have been linked to maintaining proper nutrition intake, chewing ability, and quality of life, particularly essential for a rapidly aging population. The demand for implants is projected to increase up to 23% by 2026, with the largest rise expected among those aged 65-74 years - patients with the highest bone quality variability. The procedure itself requires an osteotomy be drilled into the mandible or maxilla (depending on site of implant) so that the implant can be seated within the boney structures of the jaw. A significant challenge to creating the initial osteotomy is ensuring that no critical structures (i.e., nerve or sinus) near to the bone are breached leading to severe post-procedure morbidities. Further, understanding patient- specific bone integrity at the implant site is critical for proper implant selection and use of adjunctive procedures such as bone densification to ensure successful osseointegration and long-term stability. Clinical limitations: despite the widespread use of cone beam CT (CBCT) for preoperative anatomic assessment, significant challenges remain in accurately placing the implant and quantifying bone quality from hounsfield (HU) measurements. Specifically, CBCT struggles to visualize boney microstrucutres, particularly in trabecular cancellous bone which has increasingly been recognized as critical for implant loading, stability and overall osseointegration. Further, while pre-operative imaging and use of guidance has helped significantly improve surgical outcomes, iatrogenic injury to high-risk structures still occurs in ~10-20% of the cases, resulting in significant short- and long-term morbidity. Specific objectives: this proposal aims to leverage a novel multimodal dataset uniquely integrating radiomics, impediomics and intraoperative surgical tracking data. Features from more 600 unique drill depths within 105 osteotomies from an in vivo large animal model yielded a >20K impedance feature dataset with novel insights including ultra-high resolution micro-CT scans of cortical and trabecular bone microarchitecture, impedance-based electrical property features collected in real-time while drilling, and intentional breaches of high-risk nerve and sinus structures. This dataset presents a unique opportunity to evaluate novel potential imaging-correlated impedance-based metrics for predictive surgical osseointegration and injury prevention. We aim to 1) expand this dataset through additional advanced processing and feature extraction to derive impedance-based impediomics" and radiomics features (aim 1), 2) explore machine learning models, including multi-feature approaches to detect high-risk anatomic interfaces that could provide real-time feedback during osteotomy to mitigate iatrogenic injury risk (aim 2), and 3) leverage our novel impedance features and unique ultrahigh-resolution micro-CT images to develop potential metrics for precise site-specific bone quality characterization at prospective implant sites. Such a capability could be transformative for delivering personalized treatment plans that maximize outcomes while mitigating complications for dental implant patients.