AI Orthodontic Diagnosis Server with Probability Scoring
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Solution Overview
Problem
Current systems for automated orthodontic diagnosis and treatment lack effective artificial intelligence capabilities, failing to make accurate and efficient decisions based on interpreted patient data.
Innovation Solution
A centralized server system that receives patient data, including photographs, study models, and radiographs, uses a database with scientific literature and dynamic treatment results to analyze and diagnose orthodontic conditions, assigning probability values to diagnoses and proposing treatment approaches using AI algorithms and decision trees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used to capture and interpret patient data, then productivity is improved, but reliability deteriorates due to lack of effective AI decision-making capabilities
Solution Approach 1:
The patent introduces an AI engine as an intermediary component between the automated data capture system and the orthodontist. This AI engine processes interpreted patient data, identifies orthodontic conditions, and generates treatment recommendations, thereby bridging the gap between automated data collection and reliable clinical decision-making.
Solution Approach 2:
The patent replaces manual mechanical analysis methods with AI-based computational analysis. The AI engine uses machine learning algorithms to analyze patient data, substitute the traditional mechanical and manual interpretation processes, and provide automated diagnosis and treatment planning capabilities.
2Reliability
If AI capabilities are added to automated systems, then reliability is improved, but device complexity increases
Solution Approach 1:
The AI engine is designed as a universal platform that can handle multiple orthodontic conditions and treatment scenarios. It processes various types of patient data (photographs, scans, models) and provides comprehensive diagnosis and treatment recommendations, reducing the need for multiple specialized systems.
Solution Approach 2:
The system incorporates self-learning capabilities where the AI engine continuously improves its diagnostic accuracy by learning from treated cases and feedback. The system automatically updates its knowledge base and refines its algorithms, reducing the need for manual configuration and maintenance complexity.
3Measurement precision
If comprehensive patient data is collected and analyzed, then measurement precision is improved, but loss of time increases due to extensive data processing
Solution Approach 1:
The system performs preliminary processing and pre-analysis of patient data as it is being collected. The AI engine begins identifying patterns and potential conditions during data ingestion, preparing preliminary diagnoses before the complete dataset is available, thereby reducing the final analysis time.
Solution Approach 2:
The AI analysis process operates continuously throughout the data collection and treatment planning workflow. Rather than performing batch processing, the system continuously analyzes incoming data streams, providing real-time or near-real-time diagnostic feedback without interrupting the clinical workflow.
Data Source
AI summary
Methods and systems for diagnosing and identifying a treatment for an orthodontic condition can include a server configured to receive patient data through a website. Methods and systems can include the use of a database that includes or has access to information derived from textbooks and scientific literature and dynamic results derived from ongoing and completed patient treatments. Methods and systems can include the operation of at least one computer program within the server, which can be capable of analyzing patient data and identifying at least one diagnosis of an orthodontic condition. Methods and systems can include assigning a probability value to at least one diagnosis, and the probability value can represent a likelihood that a diagnosis is accurate. Methods and systems can include instructing a computer program to identify at least one treatment approach, a corrective appliance, or a combination thereof for the at least one diagnosis.