Dental Image Explanation Generation With Historical Diagnosis Coding

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Solution Overview

Problem

Existing predictive data analysis solutions for dental images are inefficient and unreliable, requiring manual captioning and lacking flexibility in diagnosis code systems.

Innovation Solution

A system utilizing an encoder-decoder architecture with machine learning models to automatically generate historically dynamic explanation data objects for dental images, integrating current and historical diagnosis codes, reducing the need for manual captioning and enabling flexible diagnosis code systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual captioning is used for dental images, then reliability of diagnosis codes is maintained, but efficiency and time consumption deteriorate

Engineering Contradiction:

Improvecaptioning efficiencyVSAvoiddiagnosis code accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent replaces manual mechanical captioning processes with an automated machine learning-based system that uses encoder-decoder architecture to generate diagnosis codes from dental images, eliminating the need for manual intervention while maintaining diagnostic accuracy through trained neural networks

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service automation where the machine learning model independently processes dental images, extracts features, generates diagnosis codes, and performs predictions without requiring manual input or intervention, thereby improving efficiency while maintaining reliability through automated consistent processing

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If flexible diagnosis code systems are implemented, then adaptability improves, but system complexity increases

Engineering Contradiction:

Improvediagnosis code system flexibilityVSAvoidmachine learning framework complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The encoder-decoder architecture serves multiple functions within a single unified framework: it processes dental images, extracts diagnostic features, generates diagnosis codes, and performs predictive analysis, thereby achieving flexibility across different diagnosis code systems (CDT, ICD) without requiring separate specialized systems for each

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The machine learning framework is designed to be dynamic and adaptable, allowing the model to learn and adjust to different diagnosis code systems (such as switching between CDT and ICD codes) through training data, enabling the same architectural structure to serve multiple diagnostic purposes without increasing operational complexity

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12476005B2Machine learning techniques for generating historically dynamic explanation data objects

Publication Date: 2025.11.18 OPTUM INC

AI summary

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a historically dynamic explanation data object for a dental image data object. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform generating a historically dynamic explanation data object for a dental image data object using an encoder-decoder architecture, where the encoder machine learning framework of the encoder-decoder architecture comprises a current diagnosis identification machine learning model, a historical diagnosis identification machine learning model, a convolutional embedding machine learning model, a new diagnosis code inference machine learning model, and a feature vector combination machine learning model.