MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641111475 A) filed by Saveetha Institute Of Medical And Technical Sciences on September 17, 2026, for Implant Ai.
Inventors include Blesson. S; Dr Ramya Mohan; Dr. S Sangeetha; and Dr. J Joselin Sheela.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: THE FIELD OF INVENTION (DENTAL AND MAXILLOFACIAL ARTIFICIAL INTELLIGENCE) The present invention relates to the field of artificial intelligence in dental and maxillofacial surgery, specifically pertaining to AI-Based Implant Site Assessment and Planning Using Cone Beam Computed Tomography (CBCT). The invention encompasses computer-aided diagnosis, deep learning-based medical image analysis, and intelligent pre-surgical planning systems for dental implantology. The platform integrates advanced AT algorithms with CBCT imaging technology to provide precise, automated evaluation of anatomical structures to support dental implant placement decisions. BACKGROUND OF THE INVENTION Dental implant surgery is a precise and technically demanding procedure that requires thorough pre-surgical assessment of bone quality, volume, and anatomical relationships at the intended implant site. Traditionally, this assessment has relied heavily on the clinical expertise of surgeons using two-dimensional radiographic imaging and manual interpretation of CBCT scans, which introduces variability, potential errors, and time-intensive workflows. With the increasing adoption of CBCT imaging in dental practice, there is an abundance of three-dimensional imaging data available; however, the manual analysis of this data remains a significant bottleneck. Surgeons must visually interpret complex 3D volumes, assess bone density (measured in Hounsfield units), measure available bone dimensions, and identify proximity to critical anatomical structures such as the inferior alveolar nerve, maxillary sinus, and adjacent tooth roots. Errors in this assessment can lead to implant failure, nerve damage, or sinus perforation, resulting in significant patient morbidity and increased healthcare costs. Existing software tools offer some visualization capabilities but largely depend on manual measurements and surgeon judgment, lacking intelligent automation or AI-driven recommendations. Therefore, there exists a critical need for an intelligent, automated system that can reliably analyze CBCT data, assess implant site suitability, and guide surgeons with accurate, data-driven planning support. SUMMARY OF THE INVENTION The present invention, Implant AT, provides an intelligent AI-based platform for automated assessment and planning of dental implant sites using CBCT imaging data. The system processes three-dimensional CBCT scans through deep learning models trained on large datasets of annotated dental imaging to detect and evaluate anatomical features relevant to implant placement. The platform automatically segments bone structures, computes bone density values, measures available bone dimensions, and identifies the location of critical anatomical structures such as the inferior alveolar nerve canal and maxillary sinus floor. Based on this analysis, the system generates a comprehensive site assessment report that includes implant feasibility scoring, recommended implant dimensions, suggested placement angulation, and safety margins from vital structures. The invention provides a graphical interface for interactive 3D visualization, enabling surgeons to review AI-generated assessments, modify parameters, andr nia-riz cal plans. The Implant Al system is developed as an integrated software platform combining CBCT image processing, artificial intelligence, and clinical decision support for dental implant site assessment and planning. The system architecture consists of a CBCT data ingestion module, an AI-based image analysis engine, a surgical planning interface, and a report generation module. The CBCT data ingestion module accepts standard DICOM format tiles exported from dental CBCT units and performs preprocessing operations including image normalization, noise reduction, and volumetric reconstruction to prepare three-dimensional datasets for analysis. The AI- based image analysis engine employs convolutional neural networks (CNNs) and transformer-based deep learning architectures trained on curated datasets of annotated dental CBCT scans. The engine performs automatic segmentation of cortical and cancellous bone, detection of the inferior alveolar nerve canal, identification of the maxillary sinus boundaries, localization of adjacent teeth and roots, and computation of bone density values in Hounsfield units across the region of interest. The surgical planning interface provides a multi-planar reconstruction (MPR) viewer displaying axial, coronal, sagittal, and cross-sectional views with AI-generated annotations overlaid. The report generation module compiles all AI-generated findings into structured clinical reports including bone quality classification, site-specific measurements, and recommended implant dimensions exportable in PDF format. Built using Python-based deep learning frameworks including TensorFlow and PyTorch, with a React-based frontend and REST API backend, the system ensures scalability and reliable clinical-grade performance The present invention relates to an AI-based platform, Implant Al, for automated implant site assessment and planning using CBCT imaging. The system integrates deep learning-based image segmentation, bone analysis, and surgical planning tools to deliver a comprehensive pre-surgical assessment workflow for dental implantology. The platform comprises modules for CBCT data ingestion (DICOM format), AI-driven anatomical segmentation, bone quality computation, implant site feasibility assessment, virtual implant simulation, and clinical report generation. The Al engine processes three-dimensional bone volumes and identifies critical anatomical structures including the inferior alveolar nerve canal, maxillary sinus, cortical and cancellous bone boundaries, and adjacent dental roots. Built on Python-based deep learning frameworks (TensorFlow, PyTorch) with a React-based frontend and REST API backend, the system is designed for scalability and compatibility with standard CBCT imaging devices. Future enhancements include integration with intraoral scanning, AI-guided surgical navigation, cloud-based multi-institution data collaboration, and automated implant inventory management. We Claim 1. Lack of intelligent automation in dental implant site assessment: Dental implant planning relies heavily on manual interpretation of CBCT scans by surgeons, which introduces variability, potential errors, and time-intensive workflows, increasing the risk of implant failure, nerve damage, or sinus perforation. 2. Solution: Implant Al automates CBUT data analysis using deep learning models that segment bone structures, compute bone density,_measure bone dimensions, and identify critical anatomical structures, delivering accurate and consistent implant site assessments without manual measurement. 3. Absence of intelligent pre-surgical planning and virtual implant simulation: Existing dental planning software lacks AI-driven implant recommendations, virtual placement simulation with real-time feedback, and automated safety margin evaluation, limiting the surgeon's ability to plan optimal implant placement. 4. Solution: The platform provides an interactive surgical planning interface with multi-planar CBCT reconstruction, virtual implant placement tools, real-time bone engagement feedback, and AI-generated recommendations for implant dimensions, placement angulation, and safety margins from vital structures. 5. Lack of standardized, automated clinical documentation for implant procedures: Manual generation of pre-surgical reports is time-consuming and inconsistent, lacking structured documentation of bone quality, site measurements, and planning parameters required for clinical records and multidisciplinary communication. 6. Solution: The report generation module automatically compiles AI- generated findings, bone quality classifications, site-specific measurements, recommended implant specifications, and annotated CBCT images into standardized PDF clinical reports, ensuring consistent, complete, and readily shareable documentation for patient records and su gical planning.
Disclaimer: Curated by HT Syndication.
