CATH Lab Image Analysis for Lesion Treatment Planning

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Solution Overview

Problem

Existing medical procedures lack efficient systems for real-time data analysis and remote consultation during cardiac catheterization, leading to suboptimal treatment planning and increased intervention delays.

Innovation Solution

A system utilizing computer vision and machine learning models to analyze imaging data for lesion characterization and treatment planning, enabling remote clinician consultation and secure data sharing, while protecting patient privacy and facilitating clinician education.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If imaging data is shared with remote clinicians for consultation, then treatment planning accuracy is improved, but patient data security and privacy protection become compromised

Engineering Contradiction:

Improvetreatment planning accuracyVSAvoiddata security risk

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system creates a de-identified copy of the imaging data that separates patient identifiers from clinical information. This copy can be shared with remote clinicians for treatment planning while the original identifiable data remains secure in the local system, thus enabling accurate remote consultation without compromising patient privacy

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system introduces an intermediary de-identification layer between the local imaging data and remote clinicians. This intermediary process removes or obscures identifiable information while preserving clinical utility, allowing secure data sharing for treatment planning purposes

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If real-time data analysis is performed during cardiac catheterization, then treatment planning accuracy is improved, but processing time and computational resources increase

Engineering Contradiction:

Improvetreatment planning accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary automated analysis of imaging data during the diagnostic procedure to pre-identify lesion characteristics and generate treatment planning recommendations before the therapeutic procedure begins. This preliminary action reduces the time needed for treatment planning by having analysis results ready in advance

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual clinician analysis of imaging data with automated computer vision and machine learning algorithms. This substitution significantly reduces processing time while maintaining or improving analysis accuracy, as automated systems can process images faster and more consistently than human reviewers

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If comprehensive imaging data is collected during diagnostic procedure, then treatment planning quality is improved, but data management complexity and storage requirements increase

Engineering Contradiction:

Improvetreatment planning qualityVSAvoiddata management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system extracts and separates identifiable patient information from clinical imaging data, removing unnecessary identifiers while retaining essential diagnostic information. This extraction simplifies data management by reducing the volume of sensitive data that requires secure handling while preserving treatment planning quality

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system segments imaging data into distinct components: identifiable patient information, de-identified clinical data, and treatment planning recommendations. This segmentation allows different parts of the data to be managed according to their specific requirements, reducing overall management complexity while maintaining comprehensive treatment planning capability

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250356987A1Use of CATH lab images for treatment planning

Publication Date: 2025.11.20 MEDTRONIC VASCULAR INC

AI summary

Example systems and techniques are disclosed that may determine at least one treatment strategy for a lesion. An example system may include memory configured to store at least one computer vision model and at least one machine learning model and processing circuitry communicatively coupled to the memory. The processing circuitry may be configured to receive diagnostic imaging data of at least a portion of a vasculature of a patient generated during a cardiac diagnostic procedure. The processing circuitry may be configured to execute the at least one computer vision model to determine characteristics of a lesion in the vasculature based on the received diagnostic imaging data. The processing circuitry may be configured to execute the at least one machine learning model to determine at least one treatment strategy based on the determined characteristic of the lesion, the at least one treatment strategy including at least one treatment technique and at least one medical instrument.