Sinus CT Opacification Scoring With CNN Volumetric Segmentation

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Solution Overview

Problem

Current methods for assessing paranasal sinus opacification, such as the Lund-Mackay system, are time-consuming, subjective, and lack sensitivity to change, making them impractical for routine clinical use and research on chronic rhinosinusitis (CRS) and unified airway diseases.

Innovation Solution

A convolutional neural network (CNN) is trained to perform automated volumetric segmentation and scoring of paranasal sinus opacification on CT scans, providing efficient and objective quantification by correlating with clinical metrics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual image segmentation is used for computerized volumetric analysis, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Improvevolumetric analysis accuracyVSAvoidanalysis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical segmentation with an automated computerized system using algorithms and image processing techniques to perform volumetric analysis, thereby eliminating the time-consuming manual process while maintaining or improving measurement precision through consistent automated calculations

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service automated segmentation where the computerized analysis system independently performs volumetric measurements without requiring manual intervention, allowing the analysis to serve itself through algorithmic processing of the imaging data

Inventive Principle:

Principle #25Self-service

2Ease of operation

If visual scoring systems are used for sinus assessment, then ease of operation is improved, but measurement precision deteriorates

Engineering Contradiction:

Improvescoring simplicityVSAvoidopacification quantification accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent replaces subjective visual scoring with an automated computerized volumetric analysis system that objectively quantifies sinus opacification, substituting human visual assessment with algorithm-based measurement to eliminate subjectivity while maintaining ease of operation through automated processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated segmentation methods are used, then productivity is improved, but manufacturing precision deteriorates

Engineering Contradiction:

Improveassessment speedVSAvoidsegmentation accuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent employs advanced computerized algorithms and image processing techniques to perform automated segmentation with high precision, replacing manual methods while maintaining accuracy through sophisticated computational approaches that can quickly and accurately delineate sinus boundaries and calculate volumes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12482124B2Systems and methods of volumetrically assessing structures of skeletal cavities

Publication Date: 2025.11.25 NAT JEWISH HEALTH

AI summary

Systems and methods of detecting a presence of opacification or pneumatization in skeletal structures of patients are disclosed. The systems and methods include receiving images, processing the images using a convolutional neural network, and generating, with the convolutional neural network, an opacification score for the image. Systems and methods include training the convolutional neural network to delineate skeletal structure pixels within a computed tomography scan image and to generate an intensity value for each skeletal structure pixel within a computed tomography scan image to determine an opacification score for the computed tomography scan image.