Pathogenicity Scoring Device for Genetic Mutation Analysis

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Solution Overview

Problem

The determination of pathological significance for genetic mutations in cancer genomic medicine is time-consuming, labor-intensive, and requires specialized knowledge, with AI systems struggling to provide accurate results due to mixed categories of Variants of Unknown Significance (VUS) and conflicting evidence.

Innovation Solution

A pathogenicity determination device that includes an input device for genetic mutation information, a processor to estimate scores for pathological significance and evidence strength, and an output device to display these scores, thereby reducing the burden on experts to confirm AI-determined results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If AI systems are used to determine pathological significance of genetic mutations, then productivity is improved, but measurement precision deteriorates due to mixed VUS categories and conflicting evidence

Engineering Contradiction:

Improvedetermination efficiencyVSAvoidpathological significance accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent segments the determination process into two independent scoring dimensions: a first score for pathological significance (presence/absence) and a second score for evidence strength (sufficiency). This segmentation allows the AI system to provide both productivity improvement through automation and measurement precision through dual-dimensional evaluation, resolving the contradiction by separating the determination of clinical significance from the assessment of evidence quality.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If experts manually confirm all AI-determined results, then measurement precision is improved, but loss of time increases due to burden on experts

Engineering Contradiction:

Improvedetermination accuracyVSAvoidexpert confirmation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies local quality by making the quality of expert review proportional to the need: mutations with high first scores (clear pathological significance) and high second scores (sufficient evidence) require minimal or no expert confirmation, while mutations with low scores in either dimension require more thorough expert review. This resolves the contradiction by concentrating expert time on cases where it is most needed rather than uniform review of all results.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The dual-score system provides structured feedback to experts about the confidence and reliability of AI determinations. The first score feedback indicates pathological significance likelihood, while the second score feedback indicates evidence sufficiency. This feedback mechanism allows experts to efficiently triage cases, improving time utilization while maintaining determination accuracy through targeted human review.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If rule-based scoring systems are used to classify DNA variants, then ease of operation is improved, but manufacturing precision deteriorates due to lack of clear criteria for pathological significance

Engineering Contradiction:

Improveclassification simplicityVSAvoidpathological significance determination consistency

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent transforms the determination system from qualitative rule-based classification to quantitative parameter-based scoring. Instead of simple categorical rules that lack precision, the system uses continuous score parameters (first score for pathological significance, second score for evidence strength) that can capture nuanced variations in mutation characteristics and evidence quality, thereby improving determination consistency while maintaining operational simplicity through automated calculation.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250166825A1Pathogenicity determination device, pathogenicity determination method, machine learning method, and learned model generation method

Publication Date: 2025.05.22 HITACHI LTD

AI summary

A pathogenicity determination device of the present disclosure includes: an input device that receives inputs of genetic mutation information indicating a genetic mutation, and genetic mutation-related information related to the genetic mutation information; a processor that estimates a first score related to presence or absence of a pathological significance of the genetic mutation and a second score related to strength or sufficiency of evidence related to the genetic mutation, based on the genetic mutation information and the genetic mutation-related information; and an output device that outputs the estimated first score and the estimated second score.