Dental Radiograph Annotation Using Multi-Model Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inconsistent and inaccurate readings of medical radiographs, particularly in dentistry, due to varying interpretations by healthcare providers and limitations of existing computer software in analyzing dental X-rays.
Innovation Solution
An API and machine learning system that utilizes computer vision and machine learning to automatically identify pathologies in dental radiographs, providing real-time or near-real-time annotations and feedback for improved accuracy, using trained models and user interfaces for clinician review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review by healthcare providers is used, then diagnostic judgment can be made, but reading consistency and accuracy vary due to limited experience and subjective interpretation
Solution Approach 1:
The patent replaces manual visual inspection by healthcare providers with an automated computer vision system. The machine learning model automatically detects pathologies, anatomies, restorations, and anomalies in dental radiographs, eliminating subjective human interpretation and providing consistent, objective measurements across all cases.
Solution Approach 2:
The system creates a digital copy of the radiograph and processes it through machine learning models to generate annotations. This allows the system to review and analyze the same image data multiple times without fatigue or bias, providing consistent results that replicate expert judgment across different cases.
2Extent of automation
If existing computer software is used for image management, then digital file review is enabled, but automated analysis capability is insufficient
Solution Approach 1:
The system enables self-service automated analysis where the machine learning model independently processes radiographs, detects pathologies, and generates annotations without requiring manual intervention. The system serves itself by automatically reviewing images, identifying features of interest, and providing diagnostic support.
Solution Approach 2:
The system provides feedback by generating automated annotations and confidence scores that indicate the likelihood of detected pathologies. This feedback mechanism allows healthcare providers to review and verify findings, improving overall accuracy while maintaining high automation levels.
3Adaptability or versatility
If multiple healthcare providers review the same radiograph, then different perspectives can be obtained, but disagreements and inconsistencies arise
Solution Approach 1:
The system applies homogeneous processing to all radiographs through standardized machine learning models. This ensures that the same algorithmic criteria are applied consistently across all cases, eliminating variability in interpretation that occurs when different providers review the same image with their individual experiences and biases.
Data Source
AI summary
Systems and methods are provided for automatically marking locations within a radiograph of one or more dental pathologies, anatomies, anomalies or other conditions determined by automated image analysis of the radiograph by a number of different machine learning models. Image annotation data may be generated based at least in part on obtained results associated with output of the multiple machine learning models, where the image annotation data indicates at least one location in the radiograph and an associated dental pathology, restoration, anatomy or anomaly detected at the at least one location by at least one of the machine learning models. A number of different pathologies may be identified and their locations marked within a single radiograph image.