Automated Brock Score Calculation for Lung Nodule Detection
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Solution Overview
Problem
Manual detection and analysis of lung nodules in CT scans are time-consuming and prone to errors, with small nodules often going unnoticed, necessitating an automated and accurate method for early cancer detection.
Innovation Solution
A system and method using deep learning models to detect and analyze CT scan images, involving resampling, nodule segmentation, and characteristic determination to automatically calculate a brock score in real-time, incorporating demographic data for lung cancer risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual detection and analysis of lung nodules is performed, then the health practitioner can identify nodules and their characteristics, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces the manual mechanical analysis system with an automated deep learning-based image processing system. The deep learning model automatically detects nodules, segments them, and extracts characteristics from CT scan images, eliminating the need for manual mechanical analysis by health practitioners while improving both accuracy and efficiency
2Measurement precision
If manual analysis is performed, then nodules can be identified, but small nodules often go unnoticed
Solution Approach 1:
The patent applies segmentation by dividing the CT scan image into multiple slices and further segmenting the region of interest around each detected nodule. This multi-level segmentation approach allows the deep learning model to focus on specific areas, improving the detection precision of small nodules while maintaining high reliability through automated analysis of the entire image set
3Productivity
If automated deep learning models are used, then detection accuracy and efficiency are improved, but the system complexity increases
Solution Approach 1:
The complex automated system is segmented into distinct functional modules: a deep learning model for nodule detection, an image processing module for region of interest identification, and a characteristic extraction module. This segmentation of the system makes the complex automated process more manageable and interpretable while maintaining high detection efficiency
Data Source
AI summary
Disclosed is a system and a method for determining a brock score. A CT scan image may be resampled into a plurality of slices using a bilinear interpolation. A nodule may be detected on one or more of the plurality of slices. A region of interest associated with the nodule may be identified using an image processing technique. Further, a nodule segmentation may be performed to remove an area surrounding the region of interest. Subsequently, a plurality of characteristics associated with the nodule may be identified automatically using a deep learning model. Finally, a brock score for the patient may be determined based on the plurality of characteristics and demographic data of the patient.