CBCT Airway Imaging Analysis with Automated 3D Mask Prediction
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Solution Overview
Problem
The high level of detail in CBCT scans makes manual analysis challenging and resource-intensive, requiring significant human effort for consistent disease identification.
Innovation Solution
An automated system using machine learning techniques processes CBCT scans to generate 3D masks and predict 2D slices, enabling efficient extraction of features and diagnostic predictions for airway imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of CBCT scans is performed, then detailed disease identification can be achieved, but significant human effort and time are required
Solution Approach 1:
The patent replaces the mechanical human analysis process with an automated machine learning system. The system uses trained models to process CBCT scan data, generating 3D masks and diagnostic predictions automatically, thereby eliminating the need for manual human analysis while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces intermediate processing steps including 3D mask generation and 2D slice prediction as mediators between the raw CBCT data and final diagnostic conclusions. These intermediate representations enable automated analysis while preserving the detailed information needed for accurate disease identification.
2Reliability
If manual analysis of CBCT scans is performed, then consistent disease identification can be achieved, but significant human overhead is required
Solution Approach 1:
The patent implements a self-service automated analysis system that processes CBCT scans without requiring human analysts. The machine learning models independently perform segmentation, feature extraction, and diagnostic prediction, ensuring consistent results while eliminating human resource overhead.
Solution Approach 2:
The patent replaces the human analyst system with an automated computational system. This substitution ensures that the same analysis protocol is applied consistently to all scans, eliminating variability in human performance while reducing resource requirements.
3Productivity
If automated machine learning analysis is implemented, then analysis time is reduced, but processing complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct modular components: 3D mask generation, 2D slice prediction, feature extraction, and diagnostic prediction. Each module performs a specific function and can be independently optimized, managing system complexity while maintaining high throughput.
Solution Approach 2:
The patent transforms the 3D CBCT data into 2D slices through predicted 2D representations, enabling more efficient processing while preserving essential diagnostic information. This dimensional transformation simplifies the processing complexity while maintaining analytical capability.
Data Source
AI summary
A system for automated image analysis includes an imaging device configured to capture input data of a patient, a processor, and a memory coupled to the processor. The memory has instructions stored thereon, which when executed by the processor, cause the system to: capture input data from the imaging device, the input data including a three-dimensional (3D) image of a portion of a patient; receive the input data from the imaging device; generate a first 3D mask based on the input data; generate a second 3D mask based on the first 3D mask; and determine a diagnostic prediction based on the first 3D mask and the second 3D mask. Generating the second 3D mask includes predicting a plurality of two-dimensional (2D) slices based on the first 3D mask, such that each 2D slice of the plurality of 2D slices is an axial cross-section of the first 3D mask.