CTO Treatment Plan Adjustment Using Intra-Operative Imaging

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Solution Overview

Problem

Existing treatment plans for chronic total occlusions (CTOs) in blood vessels are challenging to adjust during interventional procedures due to the need for real-time assessment of occlusion characteristics that can only be detected during the procedure, often relying on trial and error.

Innovation Solution

A system and method that utilize pre-operative imaging data to create a treatment plan and adjust it based on intra-operative imaging and sensor data, including characteristics like proximal cap morphology, occlusion length, vessel quality, and collateral circulation, using machine-learning models to predict necessary adjustments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a treatment plan is created using only pre-operative imaging data, then the planning process is simpler and faster, but the accuracy and reliability of the treatment plan is insufficient because critical occlusion characteristics can only be detected during the procedure

Engineering Contradiction:

Improveassessment accuracy of occlusion characteristicsVSAvoidprocedure time for plan adjustment

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary extraction and assessment of occlusion characteristics from pre-operative imaging data before the treatment procedure begins. This preliminary action prepares the treatment plan framework in advance, so that when the procedure starts, only incremental adjustments are needed based on intra-operative findings, rather than creating the entire plan from scratch during the procedure.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors and compares intra-operative imaging data against the pre-established treatment plan, automatically detecting deviations in occlusion characteristics. This feedback mechanism triggers real-time plan adjustments only when necessary, minimizing procedure time while maintaining high accuracy through data-driven decision-making.

Inventive Principle:

Principle #23Feedback

2Reliability

If treatment plan adjustments are made during the procedure based on intra-operative findings, then the reliability and success rate of the treatment improves, but the complexity of the procedure increases and more time is required

Engineering Contradiction:

Improvetreatment success rateVSAvoidsystem complexity for real-time adjustment

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system replaces manual clinical judgment and trial-and-error adjustments with an automated machine learning model that processes intra-operative imaging data and recommends plan modifications. This substitution of mechanical/cognitive processes with an automated intelligent system reduces procedural complexity while maintaining high reliability through consistent, data-driven decision-making.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables the treatment plan to adjust itself automatically based on intra-operative findings without requiring extensive manual intervention. The machine learning model autonomously analyzes imaging data, compares it with pre-operative baselines, and generates adjustment recommendations, allowing the clinical team to focus on execution rather than complex planning decisions during the procedure.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If manual trial and error methods are used to assess occlusion characteristics during the procedure, then the system is simpler to implement, but the time required for assessment increases and accuracy decreases

Engineering Contradiction:

Improveocclusion characteristic detection accuracyVSAvoidlevel of automated analysis

Core Design Contradiction:

Measurement precisionVSExtent of automation

Solution Approach 1:

The system replaces manual visual assessment and trial-and-error techniques with automated machine learning-based image analysis. The algorithm processes intra-operative imaging data to precisely extract occlusion characteristics such as cap morphology, occlusion length, and vessel quality, achieving superior measurement precision through computational methods rather than human observation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentEP4685809A1Systems and methods for adjustment of a treatment plan using treatment progress characteristics from imaging data

Publication Date: 2026.01.28 KONINKLIJKE PHILIPS NV

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AI summary

A system is provided for determining a treatment plan for a medical procedure. The system includes a processor configured to generate the treatment plan for the medical procedure. The processor is also configured to obtain images acquired during performance of the medical procedure according to the treatment plan, record progression of the medical procedure based on the images, and extract treatment progress characteristics from the images. The treatment plan may be for treatment of an occlusion and the treatment progress characteristics may include at least one of: (i) occlusion proximal cap morphology, (ii) occlusion length, course, and composition, (iii) quality of a vessel distal to the occlusion, and (iv) collateral circulation. The processor is configured to determine (i) whether to adjust the treatment plan and/or (ii) a treatment selection to use to adjust the treatment plan based on the treatment progress characteristics and the progression of the medical procedure.