Artificial Intelligence Unveils the Unseen: Mapping Novel Lung Patterns in Bronchiectasis via Texture Analysis

Abstract

Background and Objectives: Thin-section CT (TSCT) is currently the most sensitive imaging modality for detecting bronchiectasis. However, conventional TSCT or HRCT may overlook subtle lung involvement such as alveolar and interstitial changes. Artificial Intelligence (AI)-based analysis offers the potential to identify novel information on lung parenchymal involvement that is not easily detectable with traditional imaging techniques. This study aimed to assess lung involvement in patients with bronchiectasis using the Bronchiectasis Radiologically Indexed CT Score (BRICS) and AI-based quantitative lung texture analysis software (IMBIO, Version 2.2.0). Methods: A cross-sectional study was conducted on 45 subjects diagnosed with bronchiectasis. The BRICS severity score was used to classify the severity of bronchiectasis into four categories: Mild, Moderate, Severe, and tractional bronchiectasis. Lung texture mapping using the IMBIO AI software tool was performed to identify abnormal lung textures, specifically focusing on detecting alveolar and interstitial involvement. Results: Based on the Bronchiectasis Radiologically Indexed CT Score (BRICS), the severity of bronchiectasis was classified as Mild in 4 (8.9%) participants, Moderate in 14 (31.1%), Severe in 11 (24.4%), and tractional in 16 (35.6%). AI-based lung texture analysis using IMBIO identified significant alveolar and interstitial abnormalities, offering insights beyond conventional HRCT findings. This study revealed trends in lung hyperlucency, ground-glass opacity, reticular changes, and honeycombing across severity levels, with advanced disease stages showing more pronounced structural and vascular alterations. Elevated pulmonary vascular volume (PVV) was noted in cases with higher BRICSs, suggesting increased vascular remodeling in severe and tractional types. Conclusions: AI-based lung texture analysis provides valuable insights into lung parenchymal involvement in bronchiectasis that may not be detectable through conventional HRCT. Identifying significant alveolar and interstitial abnormalities underscores the potential impact of AI on improving the understanding of disease pathology and disease progression, and guiding future therapeutic strategies.

Keywords: IMBIO; alveolar; bronchiectasis; lung texture analysis.

Figure 1

Detection of parenchymal pathologies by AI and radiologist.

Figure 2

Correlation between various parenchymal pathologies detected by Artificial Intelligence and BRICS.

Figure 3

Lung texture analysis report: The Dicom representative image of each category of pattern. These were chosen based on the predominance of lung parenchymal patterns including hyperlucent, ground glass [GG], reticular, and honeycombing. Imbio LTA provided their regional distribution within three different lung zones [upper, middle, lower] in each lung. The platform provided an image in which each lung pattern was colored differently, allowing the evaluation of disease extent, composition, and location at a glance. Specific images were chosen because of the predominance of one of the patterns in each of them; at the same time, they had other patterns, also showing that all pathologies could coexist in a single case of bronchiectasis, yet have a predominance of a single pattern that may have a bearing on their clinical symptoms.