Convolutional automatic identification of B-lines and interstitial syndrome in lung ultrasound images using pre-trained neural networks with feature fusion.

ABSTRACT:

Introduction

Interstitial/alveolar syndrome (IS) is a condition detectable on lung ultrasound (LUS) that indicates underlying pulmonary or cardiac diseases associated with significant morbidity and increased mortality rates. However, diagnosing IS using LUS can be challenging and time-consuming, and it requires clinical expertise.

Methods

In this study, multiple convolutional neural network (CNN) models were trained as binary classifiers to accurately screen for IS in LUS frames by distinguishing between IS-present and healthy cases. The CNN models were initially pre-trained using a generic image dataset to learn general visual features (ImageNet) and then fine-tuned on our specific dataset of 108 LUS clips from 54 patients (27 healthy and 27 with IS, two clips per patient)

SUBMITTER: Moafa K 

PROVIDER: S-EPMC12862092 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature