CT Brain Age Screening Using AI-Extracted Quantitative Metrics
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Solution Overview
Problem
Current methods for detecting brain pathologies often require patients to be symptomatic, leading to delayed detection and progression of the pathology, which can cause undesirable outcomes.
Innovation Solution
A computer-implemented method using CT imaging and artificial intelligence to extract quantitative metrics such as brain volume, ventricular volume, and atherosclerotic calcifications to calculate a brain age score, enabling opportunistic screening for brain pathologies and assessing cognitive health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT exams are performed only when patients are symptomatic, then diagnostic accuracy for active pathologies is improved, but detection timing is delayed allowing pathology progression
Solution Approach 1:
The system performs preliminary screening of CT images for brain pathology indicators before clinical symptoms manifest. By analyzing quantitative metrics such as brain volume, ventricular volume, and atherosclerotic calcifications from routine CT scans, the system detects early signs of pathology and generates alerts for further evaluation, enabling intervention before symptom onset and pathology progression.
2Loss of time
If opportunistic screening using existing CT scans is implemented, then early detection of asymptomatic pathologies is enabled, but additional processing and analysis resources are required
Solution Approach 1:
The system repurposes existing CT scans performed for other clinical indications to serve dual purposes: maintaining their original diagnostic function while simultaneously serving as screening tools for brain pathology detection. By extracting quantitative metrics from these multi-purpose images, the system enables early detection without requiring separate dedicated screening procedures or additional imaging resources.
3Reliability
If quantitative metrics extraction from CT images is performed, then early pathology detection capability is improved, but data processing complexity increases
Solution Approach 1:
The system extracts specific quantitative metrics (brain volume, ventricular volume, atherosclerotic calcifications) from CT images by isolating and measuring key anatomical features. This extraction process converts complex medical images into simplified numerical data that can be directly compared against normative ranges, enabling reliable early detection while reducing processing complexity through focused measurement of critical parameters.
Data Source
AI summary
A computer-implemented method for assessing brain age comprises (i) obtaining a set of computed tomography (CT) images, the set of CT images capturing at least a portion of a brain of a patient, the set of CT images being captured for a purpose independent of assessing brain age; (ii) using the set of CT images as an input to an artificial intelligence (AI) module configured to determine a brain measurement based on CT image set input; (iii) obtaining a brain measurement output based on output of the AI module; (iv) using the brain measurement output to calculate a set of quantitative metrics associated with the brain of the patient; and (v) using the set of quantitative metrics and a chronologic age of the patient to calculate a brain age score of the brain of the patient.