Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines

Abstract

Background/Objective: A-lines and B-lines are key ultrasound markers that differentiate normal from abnormal lung conditions. A-lines are horizontal lines usually seen in normal aerated lungs, while B-lines are linear vertical artifacts associated with lung abnormalities such as pulmonary edema, infection, and COVID-19, where a higher number of B-lines indicates more severe pathology. This paper aimed to evaluate the effectiveness of a newly released lung ultrasound AI tool (ExoLungAI) in the detection of A-lines and quantification/detection of B-lines to help clinicians in assessing pulmonary conditions. Methods: The algorithm is evaluated on 692 lung ultrasound scans collected from 48 patients (65% males, aged: 55 ± 12.9) following their admission to an Intensive Care Unit (ICU) for COVID-19 symptoms, including respiratory failure, pneumonia, and other complications. Results: ExoLungAI achieved a sensitivity of 91% and specificity of 81% for A-line detection. For B-line detection, it attained a sensitivity of 84% and specificity of 86%. In quantifying B-lines, the algorithm achieved a weighted kappa score of 0.77 (95% CI 0.74 to 0.80) and an ICC of 0.87 (95% CI 0.85 to 0.89), showing substantial agreement between the ground truth and predicted B-line counts. Conclusions: ExoLungAI demonstrates a reliable performance in A-line detection and B-line detection/quantification. This automated tool has greater objectivity, consistency, and efficiency compared to manual methods. Many healthcare professionals including intensivists, radiologists, sonographers, medical trainers, and nurse practitioners can benefit from such a tool, as it assists the diagnostic capabilities of lung ultrasound and delivers rapid responses.

Keywords

• A-lines
• B-lines
• lung ultrasound
• machine learning
• artificial intelligence