Machine Learning Biopsy Grading via Histological Feature Extraction

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Solution Overview

Problem

Current methods for grading endomyocardial biopsy images in heart transplant patients are inaccurate and lack consistency, leading to poor clinical outcomes due to the 'black box' nature of deep learning models and limited interpretability, which complicates treatment decisions and increases the risk of graft rejection.

Innovation Solution

A machine learning pipeline that extracts specific histological features from digitized endomyocardial biopsy images, focusing on immune cell regions and interstitial fibers, to generate predictions that align with ISHLT grades and clinical trajectories, providing higher accuracy and interpretability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If deep learning models are used for grading endomyocardial biopsy images, then automation is improved, but interpretability deteriorates (black box nature)

Engineering Contradiction:

Improveautomation of biopsy gradingVSAvoidinterpretability of grading results

Core Design Contradiction:

Extent of automationVSLoss of information

Solution Approach 1:

The patent introduces pathologist-verified grading criteria and standardized evaluation frameworks as intermediaries between the deep learning model and clinical decision-making. These intermediaries translate the model's automated assessments into interpretable, clinically-meaningful metrics that maintain both automation benefits and clinical interpretability

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback loops where pathologist reviews and corrections of automated grading results are fed back into the model training process. This continuous feedback mechanism improves model interpretability by aligning automated assessments with expert clinical judgment while maintaining automation efficiency

Inventive Principle:

Principle #23Feedback

2Ease of operation

If current grading methods are used, then ease of operation is maintained, but measurement precision deteriorates (inaccuracy and inconsistency)

Engineering Contradiction:

Improvesimplicity of grading processVSAvoidaccuracy of rejection grading

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent segments the complex biopsy grading task into distinct, standardized evaluation components (e.g., lymphocytic infiltration patterns, myocyte damage assessment, fibrosis evaluation). Each segment is assigned specific scoring criteria, making the overall process both simpler to execute and more precise in its measurements

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system transforms subjective pathologist assessments into quantifiable parameters with defined measurement scales. By changing the grading from qualitative descriptors to standardized quantitative parameters, the system improves measurement precision while maintaining operational simplicity through automated calculation

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If manual pathologist review is used, then interpretability is maintained, but productivity deteriorates (time-consuming process)

Engineering Contradiction:

Improveclinical insight from biopsyVSAvoidspeed of grading and treatment decision

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The patent merges automated deep learning assessment with expert pathologist review into a unified hybrid system. The automated component handles routine grading tasks efficiently, while pathologists focus on complex cases requiring clinical insight, combining the speed of automation with the interpretability of expert review

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system applies partial automation where deep learning models perform initial grading on all cases, with pathologist review applied selectively only to cases that fall into ambiguous or high-stakes categories. This partial action approach maintains productivity by automating what can be automated while preserving interpretability where it matters most

Inventive Principle:

Principle #16Partial or excessive action

4Ease of operation

If inconsistent grading occurs, then ease of operation is maintained, but reliability deteriorates (poor clinical outcomes)

Engineering Contradiction:

Improveflexibility in grading interpretationVSAvoidconsistency of grading results

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent establishes universal grading criteria and standardized evaluation protocols that can be consistently applied across different pathologists, institutions, and time periods. These multi-functional guidelines serve multiple purposes: maintaining operational flexibility for different biopsy types while ensuring consistent, reliable results across all applications

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20230148068A1Prediction of cardiac rejection via machine learning derived features from digital endomyocardial biopsy images

Publication Date: 2023.05.11 CASE WESTERN RESERVE UNIV

AI summary

The present disclosure in some embodiments relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including obtaining one or more digitized endomyocardial biopsy (EMB) images from a patient having had a heart transplant; extracting a plurality of histological features from the one or more digitized EMB images; and applying a machine learning predictive model to operate on the plurality of histological features to generate a prediction for the patient. The prediction includes a grade or a clinical trajectory associated with the patient.