Automated Food Trap Detection Using Multi-Modal Scanning
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Solution Overview
Problem
Current methods for detecting and treating food traps in teeth are inadequate, relying on patient awareness and manual oral hygiene practices, which can lead to plaque buildup, dental issues, and gum disease.
Innovation Solution
The development of systems and methods using computing devices for automated detection and prediction of food traps through scanning technologies like 2D, 3D, near-infrared, and CBCT scanning, combined with machine learning algorithms, to provide feedback and treatment options for orthodontic and prosthodontic interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated detection systems with multiple scanning technologies are implemented, then detection precision and reliability improve, but device complexity and computing resources increase
Solution Approach 1:
The detection system is segmented into multiple independent scanning components (2D scanner, 3D scanner, near-infrared scanner, CBCT scanner), each performing a specific function. This allows the system to achieve high detection precision through multiple specialized sensors while managing complexity by dividing the overall detection task into separate, modular scanning operations.
Solution Approach 2:
The system employs multiple scanning technologies that can detect different properties of food traps (visual appearance, three-dimensional structure, subsurface characteristics, bone density). Each scanner serves multiple purposes: 2D for surface visualization, 3D for spatial mapping, near-infrared for subsurface detection, and CBCT for bone density assessment, creating a universal detection platform that handles various detection needs through a single integrated system.
2Manufacturing precision
If multiple scanning technologies and machine learning algorithms are used, then treatment planning accuracy improves, but computing resources and processing time increase
Solution Approach 1:
The system performs preliminary scanning and data collection using multiple technologies (2D, 3D, near-infrared, CBCT) to gather comprehensive information about food traps before treatment planning begins. Machine learning algorithms pre-process this data to identify patterns and predict treatment outcomes, allowing clinicians to make informed decisions without requiring intensive real-time computing during the actual treatment planning session.
Solution Approach 2:
The system creates detailed digital copies and three-dimensional models of the patient's oral cavity and bone structure based on scanning data. These virtual replicas allow for treatment simulation and planning in a digital environment, reducing the need for multiple physical examinations and minimizing the computational load during actual treatment execution by having pre-processed model data available.
3Reliability
If comprehensive scanning and analysis are performed, then detection reliability improves, but processing efficiency decreases
Solution Approach 1:
The system replaces manual visual inspection and physical examination methods with automated scanning technologies (2D, 3D, near-infrared, CBCT) and machine learning algorithms. This substitution increases detection reliability by eliminating human error and subjectivity while improving processing efficiency through automated data acquisition and analysis, as the computational processing occurs rapidly without requiring sequential manual assessment steps.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the detection and treatment of food traps, enhancing dental health outcomes by reducing computing resources, improving processing efficiency, and providing more effective dental treatment planning.
Implementation Method 1
scanning a patient's dentition with a 2D scanner, a 3D scanner, a near-infrared scanner, and a cone beam computed tomography (CBCT) scanner to generate scan data
Implementation Method 2
scanning a patient's dentition with a 2D scanner, a 3D scanner, a near-infrared scanner, and a cone beam computed tomography (CBCT) scanner to generate scan data
Data Source
AI summary
A system for treating food traps may include an intraoral scanner and a processor coupled in electronic communication with the intraoral scanner, a processor, and memory comprising instructions that when executed by the processor cause the system to carry out a method. The method may include receiving a 3D digital model of a patient's detention and analyzing the 3D digital model of the patient's detention by detecting anatomic structures in the 3D digital model of the patient's detention that correspond to food traps. The 3D digital model of the patient's detention may be displayed on a display and digital feedback may be provided on the displayed 3D digital model of the patient's detention. The feedback may identify a location of the food traps.