Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure

Abstract

Abstract Background Ultrasound can detect fluid in the alveolar and interstitial spaces of the lung using the presence of artifacts known as B-lines. The aim of this study was to determine whether a deep learning algorithm generated B-line severity score correlated with pulmonary congestion and disease severity based on clinical assessment (as identified by composite congestion score and Rothman index) and to evaluate changes in the score with treatment. Patients suspected of congestive heart failure underwent daily ultrasonography. Eight lung zones (right and left anterior/lateral and superior/inferior) were scanned using a tablet ultrasound system with a phased-array probe. Mixed effects modeling explored the association between average B-line score and the composite congestion score, and average B-line score and Rothman index, respectively. Covariates tested included patient and exam level data (sex, age, presence of selected comorbidities, baseline sodium and hemoglobin, creatinine, vital signs, oxygen delivery amount and delivery method, diuretic dose). Results Analysis included 110 unique subjects (3379 clips). B-line severity score was significantly associated with the composite congestion score, with a coefficient of 0.7 (95% CI 0.1–1.2 p = 0.02), but was not significantly associated with the Rothman index. Conclusions Use of this technology may allow clinicians with limited ultrasound experience to determine an objective measure of B-line burden.

Keywords

• Point-of-care ultrasound
• Lung ultrasound
• B-lines
• Heart failure
• Artificial intelligence