Abstract
Background 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. The diagnosis of IS using LUS can be challenging and time-consuming, and it requires clinical expertise.
Methods In this study, multiple Convolutional Neural Network (CNN) deep learning (DL) models were trained, acting as binary classifiers, to accurately screen for IS from LUS frames by differentiating between IS-present and healthy cases. The CNN DL 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), with two clips per patient, to perform a binary classification task. Each frame within a clip was assessed to determine the presence of IS features or to confirm a healthy lung status. The dataset was split into training (70%), validation (15%), and testing (15%) sets. Following the process of fine-tuning, we successfully extracted features from pre-trained DL models. These extracted features were utilised to train multiple machine learning (ML) classifiers, hence the trained ML classifiers yielded significantly improved accuracy in IS classification. Advanced visual interpretation techniques, such as heatmaps based on Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-Agnostic explanations (LIME), were implemented to further analyse the outcomes.
Results The best-trained ML model achieved a test accuracy of 98.2%, with specificity, recall, precision, and F1-score values all above 97.9%. Our study demonstrates, for the first time, the feasibility of using a pre-trained CNN with the feature extraction and fusion technique as a diagnostic tool for IS screening on LUS frames, providing a time-efficient and practical approach to clinical decision-making.
Conclusion This study confirms the practicality of using pre-trained CNN models, with the feature extraction and fusion technique, for screening IS through LUS frames. This represents a noteworthy advancement in improving the efficiency of diagnosis. In the next steps, validation on larger datasets will assess the applicability and robustness of these CNN models in more complex clinical settings.