AI Classifier for Lung Nodule Assessment
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Solution Overview
Problem
Current methods for detecting lung cancer through pulmonary nodules in lung scans are plagued by high rates of misdiagnosis, with benign and malignant nodules often being indistinguishable, leading to unnecessary treatments and missed early-stage cancers due to inaccurate risk scoring.
Innovation Solution
A method utilizing an AI classifier that processes lung scan and blood panel data to identify specific image and blood markers, trained on large datasets to provide a more accurate cancer risk score and patient management recommendations, incorporating both imaging and laboratory diagnostic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based risk scoring models (Mayo/Swensen, Brock, VA models) are used to assess lung nodule malignancy, then the assessment process can be performed with available imaging data, but the accuracy is poor with high rates of false positive and false negative diagnoses
Solution Approach 1:
The patent combines multiple data sources including imaging data (CT scans), blood marker data (proteomic, genomic, metabolomic markers), and patient clinical data into a unified AI-based risk assessment model. This integration of heterogeneous data types enables more accurate differentiation between benign and malignant nodules by leveraging complementary information from each data source, directly addressing the low accuracy of image-based models alone.
Solution Approach 2:
The patent introduces an AI classifier as an intermediary system that processes and integrates multiple data types (imaging, blood markers, clinical parameters) to generate risk scores. This intermediary layer synthesizes information from diverse sources in a way that human clinicians cannot, enabling more accurate malignancy assessment while reducing misdiagnosis rates.
2Measurement precision
If blood marker information is combined with image marker information to improve risk scoring, then the accuracy may improve, but the complexity of evaluation increases and misdiagnosis rates remain unacceptably high
Solution Approach 1:
The patent employs an AI classifier that automatically processes, integrates, and interprets multiple data types without requiring manual clinical synthesis. The system self-manages the complexity of evaluating numerous blood markers, imaging features, and patient parameters simultaneously, generating risk scores through automated pattern recognition and weighting algorithms, thereby reducing the burden on clinicians while maintaining high accuracy.
3Ease of operation
If conventional manual techniques are used to evaluate lung nodules, then the evaluation process is simpler, but the ability to detect subtle patterns and relationships is limited
Solution Approach 1:
The patent replaces manual clinical evaluation with an automated AI classifier that uses machine learning algorithms to detect subtle patterns and relationships in medical data. The system substitutes human cognitive processing with computational methods that can simultaneously analyze numerous features from imaging, blood markers, and patient history, identifying patterns invisible to human observers while maintaining ease of use through automated decision support.
Data Source
AI summary
One or more example embodiments describes a method of performing lung nodule assessment, which method comprises the steps of obtaining a lung scan for a patient from an imaging modality; obtaining a blood panel for that patient from a blood analysis modality; and processing the lung scan and the blood panel in a classifier, which classifier is trained to assess a lung nodule based on the lung scan and the blood panel. The invention further describes a method of training such a classifier, and a lung nodule assessment arrangement.