Pathology Image Annotation for Personalized Cancer Treatment Dosing

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Determining the correct dosage and type of treatment for diseases, particularly in cases where multiple drugs are administered simultaneously, is challenging due to variations in disease severity and potential adverse effects on patients, such as in radiotherapy for head and neck cancer or chemotherapy and endocrine therapy for ER+ breast cancer.

Innovation Solution

A computer-implemented method using machine learning to process electronic medical images, incorporating metadata about previous treatments, to assess treatment effectiveness and recommend dosages or regimen adjustments based on digital pathology images.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If higher dosage of treatment is administered to ensure disease cure, then treatment effectiveness is improved, but adverse effects on patient increase

Engineering Contradiction:

Improvetreatment effectivenessVSAvoidadverse effects

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system dynamically adjusts treatment dosage parameters based on analyzed pathology features and treatment response predictions, optimizing the balance between effectiveness and adverse effects for each patient

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system incorporates feedback from treatment response analysis and pathology changes to continuously optimize dosage recommendations, ensuring effective treatment while minimizing harmful effects

Inventive Principle:

Principle #23Feedback

2Reliability

If multiple drugs are administered simultaneously to treat cancer, then treatment effectiveness is improved, but determining the right level of each drug becomes more complex

Engineering Contradiction:

Improvetreatment effectivenessVSAvoidtreatment regimen complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the complex multi-drug treatment problem into individual drug component analyses, evaluating each drug's contribution and interactions separately while providing integrated dosage recommendations

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The AI system acts as an intermediary that processes complex interactions between multiple drugs and pathology features, translating this complexity into simplified, actionable dosage recommendations for clinicians

Inventive Principle:

Principle #24Intermediary (Mediator)

3Object-affected harmful factors

If treatment dosage is reduced to minimize adverse effects, then patient safety is improved, but disease cure effectiveness may be compromised

Engineering Contradiction:

Improveadverse effectsVSAvoiddisease cure effectiveness

Core Design Contradiction:

Object-affected harmful factorsVSReliability

Solution Approach 1:

The system optimizes dosage parameters to find the minimum effective dose that achieves disease control while staying below thresholds for significant adverse effects, personalized to each patient's pathology and response profile

Inventive Principle:

Principle #35Parameter changes

4Reliability

If personalized treatment dosages are determined using AI analysis, then treatment effectiveness is improved, but system complexity and computational requirements increase

Engineering Contradiction:

Improvetreatment effectivenessVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system replaces complex manual clinical decision-making processes with AI-based automated analysis of pathology images and treatment response, reducing the burden on clinicians while improving personalization

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250364120A1Systems and methods to process electronic images for determining treatment

Publication Date: 2025.11.27 PAIGE AI INC

AI summary

A computer-implemented method for predicting a clinical outcome of a patient, the method comprising: obtaining a pathology image associated with the patient; processing the pathology image including: determining a salient region of the pathology image; and segmenting the pathology image into a plurality of tiles; providing the processed pathology image as an input to a machine learning model configured to annotate the pathology image; obtaining, as an output of the machine learning model, the annotated pathology image associated with the patient, wherein the annotated pathology image includes annotations for different classes of tissues including a tumor regression; and determining, based on the annotated pathology image, a score indicative of the clinical outcome of the patient.