Self-Supervised Spatio-Temporal Network for Classifying Lung Tumor in EBUS Videos

Background: Endobronchial ultrasound-guided transbronchial biopsy (EBUS-TBB) is a valuable technique for diagnosing peripheral pulmonary lesions (PPLs). Although computer-aided diagnostic (CAD) systems have been explored for EBUS interpretation, most rely on manually selected 2D static frames and overlook temporal dynamics that may provide important cues for differentiating benign from malignant lesions. This study aimed to develop an artificial intelligence model that incorporates temporal modeling to analyze EBUS videos and improve lesion classification. Methods: We retrospectively collected EBUS videos from patients undergoing EBUS-TBB between November 2019 and January 2022. A dual-path 3D convolutional network (SlowFast) was employed for spatiotemporal feature extraction, and contrastive learning (SwAV) was integrated to enhance model generalizability on clinical data. Results: A total of 465 patients with corresponding EBUS videos were included. On the validation set, the SlowFast + SwAV_Frame model achieved an AUC of 0.857, accuracy of 82.26%, sensitivity of 93.18%, specificity of 55.56%, and F1-score of 88.17%, outperforming pulmonologists (accuracy 70.97%, sensitivity 77.27%, specificity 55.56%, F1-score 79.07%). On the test set, the model achieved an AUC of 0.823, accuracy of 76.92%, sensitivity of 84.85%, specificity of 63.16%, and F1-score of 82.35%. The proposed model also demonstrated superior performance compared with conventional 2D architectures. Conclusions: This study introduces the first CAD framework for real-time malignancy classification from full-length EBUS videos, which reduces reliance on manual image selection and improves diagnostic efficiency. In addition, given its higher accuracy compared with pulmonologists' assessments, the framework shows strong potential for clinical applicability.