Arthritis care & research · 2026
Artificial Intelligence-Aided Lung Ultrasound Detection of Interstitial Lung Disease in Systemic Sclerosis and Inflammatory Myopathy.
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Fairchild Robert M, Deluna Mariani D, Fazli Mojtaba, Mar Diane A, Chung Melody, Davuluri Srijana, Kawano Yumeko, Guo Henry, Baker Matthew C, Fiorentino David, Tamang Suzanne, Chung Lorinda
Аннотация
OBJECTIVE: Lung ultrasound (LUS) is a sensitive, low-cost, and radiation-free modality for interstitial lung disease (ILD) detection. We previously developed and validated LUS interpretation criteria in systemic sclerosis (SSc) and idiopathic inflammatory myopathy (IIM) showing excellent diagnostic performance and correlations with ILD severity. In this study, we applied deep learning to evaluate whether convolutional neural networks (CNNs) can accurately detect ILD and its severity on LUS.
METHODS: Patients with SSc or IIM ± ILD and paired LUS and chest computed tomography (CT) were included. LUS images were labeled using CT results and human LUS-ILD 2024 (LUS-ILD-24) interpretation. Three pretrained CNN architectures (InceptionV3, ResNet-50, VGG-16) were fine-tuned via transfer learning, and a de novo lightweight architecture (LUS-Net) was developed. Model performance for ILD detection was assessed at image and patient levels using area under the curve (AUC), sensitivity, specificity, and agreement with expert interpretation. CNN outputs were correlated with pulmonary function tests (PFTs) and CT-based Computer-Aided Lung Informatics for Pathology Evaluation and Rating indices. Gradient-weighted Class Activation Mapping (Grad-CAM) visualized regions driving predictions.
RESULTS: A total of 140 patients representing 3,920 LUS images were included and split into development (74) and independent test sets (66). VGG-16 achieved the best patient-level performance (AUC 0.972, sensitivity 97.4%, specificity 92.6%) showing strong correlations with PFTs and CT severity. Grad-CAM highlighted pleural features as the primary regions influencing model predictions. CNN performance matched or exceeded LUS-ILD-24 interpretation.
CONCLUSION: Deep learning applied to LUS enables accurate ILD detection in connective tissue disease and can enhance expert interpretation. Explainable artificial intelligence suggests pleural features, even when B-lines are infrequent, are sufficient for reliable ILD recognition.
METHODS: Patients with SSc or IIM ± ILD and paired LUS and chest computed tomography (CT) were included. LUS images were labeled using CT results and human LUS-ILD 2024 (LUS-ILD-24) interpretation. Three pretrained CNN architectures (InceptionV3, ResNet-50, VGG-16) were fine-tuned via transfer learning, and a de novo lightweight architecture (LUS-Net) was developed. Model performance for ILD detection was assessed at image and patient levels using area under the curve (AUC), sensitivity, specificity, and agreement with expert interpretation. CNN outputs were correlated with pulmonary function tests (PFTs) and CT-based Computer-Aided Lung Informatics for Pathology Evaluation and Rating indices. Gradient-weighted Class Activation Mapping (Grad-CAM) visualized regions driving predictions.
RESULTS: A total of 140 patients representing 3,920 LUS images were included and split into development (74) and independent test sets (66). VGG-16 achieved the best patient-level performance (AUC 0.972, sensitivity 97.4%, specificity 92.6%) showing strong correlations with PFTs and CT severity. Grad-CAM highlighted pleural features as the primary regions influencing model predictions. CNN performance matched or exceeded LUS-ILD-24 interpretation.
CONCLUSION: Deep learning applied to LUS enables accurate ILD detection in connective tissue disease and can enhance expert interpretation. Explainable artificial intelligence suggests pleural features, even when B-lines are infrequent, are sufficient for reliable ILD recognition.