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Letting Data Speak, AI Act!

Case Study

3D Dental Scan Matching System for Prosthodontic Applications

A leading dental technology company specializing in prosthodontic applications, providing digital solutions for dental professionals to design and manufacture custom dental prosthetics using 3D scanning technology.

About the Client

A leading dental technology company specializing in prosthodontic applications, providing digital solutions for dental professionals to design and manufacture custom dental prosthetics using 3D scanning technology.

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Challenge

The client faced significant challenges in managing and utilizing their growing library of 3D dental scans. Without an efficient system to retrieve similar previous cases, dental professionals had to design each new prosthetic from scratch, leading to inconsistencies in design approach and inefficient use of their historical design data.

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Key Results

  • Significantly reduced prosthetic design time, enabling dental professionals to deliver custom restorations more efficiently.

  • Substantially improved design consistency through immediate access to anatomically similar previous cases.

  • Achieved near-instantaneous matching of complex 3D structures with optimized vector-based similarity search.

  • Decreased computational costs by approximately 90% compared to alternative deep learning approaches (DGCNN implementation).

Solution

JashDS developed a comprehensive dental scan matching system leveraging cloud infrastructure, advanced 3D processing algorithms, and vector-based similarity search. The solution was designed to be fully automated, scalable, and accessible through standardized APIs.


Key components of the solution included:


  • AWS-Based Architecture: The system implemented an AWS infrastructure utilizing EC2 for computational processing, S3 buckets for secure storage of dental scans, and ChromaDB for high-performance vector storage and retrieval.


  • Automated STL File Processing Pipeline: A robust pipeline was created to process dental scan files with several critical steps:

    • Input validation to ensure system reliability and consistency

    • Orientation correction to automatically standardize arbitrarily aligned scans

    • Slice generation at specified heights to capture features at different levels of dental structure

    • Feature extraction using PCA-based orientation checks and generating 512D vectors

    • Database operations storing vector embeddings in ChromaDB with relevant metadata


  • Cron-Driven Automation: An intelligent system was implemented with configuration initialization, file discovery, secure processing, vector generation, and state synchronization to ensure continuous updates as new scans were uploaded.


  • Dual API Implementation: The solution delivered two primary APIs:

    • Query S3 Files API allowing searches against the entire database of processed scans

    • Query Local Files API enabling users to upload and match new files against the database


The system's design prioritized cost efficiency, technical advantages through dental-specific geometric features, operational simplicity, and business benefits including lower total cost of ownership compared to deep learning alternatives.


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Technologies Used

  • AWS EC2

  • AWS S3

  • ChromaDB

  • REST API

  • Python

  • Trimesh

  • NumPy

  • STL/PLY file processing

  • Principal Component Analysis (PCA)

  • k-Nearest Neighbors (k-NN) search

  • Vector embeddings

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