AI Parsing Pipeline for CBCT Anatomical Localization
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Solution Overview
Problem
Current dental imaging technologies, such as cone beam computed tomography (CBCT), face challenges including time consumption, complexity in software usage, and the need for specialized training, while existing automated systems lack effective anatomical localization and condition classification, especially for 3D teeth segmentation and panoramic imaging. Additionally, there is a gap in communication between healthcare providers and patients, which affects diagnosis understanding and adherence.
Innovation Solution
An automated parsing pipeline system and method for anatomical localization and condition classification using deep learning models to process CBCT images, generating 3D teeth segmentation masks, and a system for generating personalized medical summaries from practitioner-patient conversations, incorporating AI to enhance communication and reduce administrative burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CBCT imaging is used to view structures in the oral-maxillofacial complex in three dimensions, then diagnostic capability is improved, but time consumption and complexity for personnel to become acquainted with the imaging software and correctly using digital imaging and communications in medicine (DICOM) data increase
Solution Approach 1:
The patent applies self-service by implementing automated AI-based parsing of CBCT images to perform anatomical localization and condition classification without requiring manual intervention from dental professionals. The system automatically processes images, generates 3D segmentation masks, and provides diagnostic insights, eliminating the need for personnel to spend time learning complex software operations while maintaining high diagnostic capability through AI expertise
Solution Approach 2:
The patent replaces the mechanical system of manual image analysis and software operation with an automated AI-based system. The deep learning models automatically parse CBCT images, identify anatomical structures, and classify conditions, substituting human expertise with intelligent algorithms that can process images rapidly without the time constraints and learning curves associated with manual operation
2Extent of automation
If deep learning is applied to interpret CBCT images, then automated anatomical localization and condition classification is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex CBCT image parsing task into distinct functional modules: image input processing, anatomical structure localization, condition classification, and result generation. Each module is handled by specialized deep learning components, making the overall complex system manageable through modular architecture while achieving high automation in anatomical localization and diagnostic analysis
3Ease of manufacture
If existing annotation tools are used for full mouth set of x-rays or panoramic radiographs, then image overlaying and centralized storage is improved, but the ability to localize and enumerate teeth using neural networks and sort images into a full mount table with neural network-mediated classification is lost
Solution Approach 1:
The patent applies universality by creating a multi-functional system that combines image overlaying, centralized storage, neural network-based tooth localization, enumeration, and classification capabilities into a single integrated platform. The system can handle various image types (CBCT, panoramic, full mouth series) and performs multiple diagnostic functions simultaneously, eliminating the need for separate tools and maintaining both storage efficiency and automated intelligence
4Reliability
If clear and effective communication of diagnosis to patient is implemented, then patient understanding and treatment adherence are improved, but communication time and complexity increase
Solution Approach 1:
The patent applies feedback by implementing AI-based analysis of practitioner-patient conversations that provides automated parsing, transcription, and generation of personalized medical summaries. The system processes communication data, identifies key diagnostic information, and creates structured summaries that can be efficiently communicated to patients, providing feedback loops that improve understanding and adherence without requiring proportional increases in communication time
Data Source
AI summary
The invention relates to a method to generate a personalized medical summary (PMS) from a practitioner-patient conversation capturing a conversation between a practitioner and a patient, transcribing the conversation between the practitioner and the patient and generating the PMS based on the transcribed conversation.