AI Lung Image Segmentation for Pulmonary Function Quantification
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Solution Overview
Problem
Current methods for diagnosing interstitial lung disease (ILD) using medical images face challenges in early detection due to the complexity of analyzing various data types, high costs, and increased rates of misdiagnosis, especially when relying on subjective radiological exams and pulmonary function tests (PFTs) with uncontrollable factors.
Innovation Solution
A method and apparatus using artificial neural networks to segment abnormal finding regions in medical images, applying predetermined weights to predict quantified pulmonary function indicators, such as spirometry results and diffusing capacity, by analyzing the size of these regions, and providing diagnostic assistance information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple tests (PFT, imaging, arterial blood gas) are synthesized to diagnose ILD, then diagnostic accuracy is improved, but analysis complexity and time consumption increase
Solution Approach 1:
The patent combines multiple diagnostic data sources (PFT results, imaging features, arterial blood gas values) into a unified AI-based diagnostic system. The neural network integrates these diverse inputs to generate a comprehensive diagnostic assessment, eliminating the need for separate manual analysis of each test while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a mediator between raw diagnostic tests and clinical interpretation. This intermediary automatically processes and synthesizes multiple test results, providing a unified diagnostic output that reduces the complexity burden on clinicians while preserving the benefits of multi-test synthesis.
2Measurement precision
If AI algorithms are trained to detect early-stage ILD patterns, then early detection capability is improved, but false positive detection increases
Solution Approach 1:
The patent employs parameter changes by adjusting the sensitivity and specificity thresholds of the AI algorithm based on clinical context and disease stage. The system dynamically modifies detection parameters to balance early detection capability with false positive control, allowing the same algorithm to adapt to different diagnostic scenarios and patient populations.
Solution Approach 2:
The patent implements a dynamic diagnostic system where the AI algorithm continuously adjusts its detection criteria based on input data characteristics and clinical context. Rather than using fixed thresholds, the system adapts its sensitivity dynamically, improving early detection while maintaining reliability by reducing false positives through context-aware parameter adjustment.
3Adaptability or versatility
If subjective radiological exam assessments are used for ILD diagnosis, then diagnostic flexibility is maintained, but inter-observer variability increases
Solution Approach 1:
The patent creates an objective digital copy of the radiological assessment process through AI analysis. The system replicates expert radiological evaluation by training on annotated imaging data, producing standardized measurements that can be consistently reproduced across different users and time points while preserving the diagnostic value of radiological examination.
Solution Approach 2:
The patent implements feedback mechanisms where the AI system provides quantitative measurements and confidence scores that feed back into the diagnostic process. This allows clinicians to maintain diagnostic flexibility by reviewing and adjusting AI findings while reducing inter-observer variability through objective reference standards and consistent measurement protocols.
Data Source
AI summary
A method of quantifying pulmonary function using a medical image includes acquiring or receiving a medical image including anatomical information for a lung region of a patient; segmenting at least one abnormal finding region in the lung region of the medical image using an artificial neural network; and predicting a quantification result related to pulmonary function based on a size of the at least one abnormal finding region.