
Letting Data Speak, AI Act!
Case Study
3D Dental Scan Matching System for Prosthodontic Applications

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.

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.

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.

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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