Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI

2025-06-30

AI Summary

To address the lack of rapid, accurate, and quantitative tools for structural lung damage assessment in ultra-short echo time MRI (UTE-MRI) of children with cystic fibrosis (CF), this study introduces APL—the first AI-driven, pixel-level pulmonary scoring system. APL integrates deep learning–based lung segmentation, lung-restricted slice sampling, pixel-level lesion annotation, and automated quantification reporting to enable end-to-end automation. Compared with conventional grid-based scoring, APL reduces per-case analysis time to 8.2 minutes—more than doubling throughput—while significantly improving accuracy (p = 0.021) and achieving excellent correlation with expert grid scores (R = 0.973, p = 5.85 × 10⁻⁹). Its key innovation lies in pioneering pixel-level AI analysis for UTE-MRI–based pulmonary quantification, uniquely balancing precision, computational efficiency, and clinical interpretability. APL establishes a new paradigm for imaging-based assessment of pediatric CF lung disease.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Evaluation and Analysis
• Multiagent Systems: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Lung magnetic resonance imaging (MRI) with ultrashort echo-time (UTE) represents a recent breakthrough in lung structure imaging, providing image resolution and quality comparable to computed tomography (CT). Due to the absence of ionising radiation, MRI is often preferred over CT in paediatric diseases such as cystic fibrosis (CF), one of the most common genetic disorders in Caucasians. To assess structural lung damage in CF imaging, CT scoring systems provide valuable quantitative insights for disease diagnosis and progression. However, few quantitative scoring systems are available in structural lung MRI (e.g., UTE-MRI). To provide fast and accurate quantification in lung MRI, we investigated the feasibility of novel Artificial intelligence-assisted Pixel-level Lung (APL) scoring for CF. APL scoring consists of 5 stages, including 1) image loading, 2) AI lung segmentation, 3) lung-bounded slice sampling, 4) pixel-level annotation, and 5) quantification and reporting. The results shows that our APL scoring took 8.2 minutes per subject, which was more than twice as fast as the previous grid-level scoring. Additionally, our pixel-level scoring was statistically more accurate (p=0.021), while strongly correlating with grid-level scoring (R=0.973, p=5.85e-9). This tool has great potential to streamline the workflow of UTE lung MRI in clinical settings, and be extended to other structural lung MRI sequences (e.g., BLADE MRI), and for other lung diseases (e.g., bronchopulmonary dysplasia).

Problem

• Develop AI-assisted scoring for lung MRI quantification
• Enable faster CF lung damage assessment than CT
• Improve accuracy and speed in UTE-MRI analysis

Innovation

• AI-assisted pixel-level lung scoring
• Ultra-short echo-time MRI quantification
• Fast and accurate CF damage assessment