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

Ching-Kai Lin1,2,3,4, Chin-Wen Chen1, Hung-Chih Tu1

  • 1Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu 300, Taiwan.

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|December 30, 2025
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Summary
This summary is machine-generated.

This study introduces an AI model analyzing full-length EBUS videos for diagnosing lung lesions. The AI model shows higher accuracy than pulmonologists, improving diagnostic efficiency for peripheral pulmonary lesions.

Keywords:
3D convolutional neural networkendobronchial ultrasoundself-supervised learningvideo classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Endobronchial ultrasound-guided transbronchial biopsy (EBUS-TBB) is crucial for diagnosing peripheral pulmonary lesions (PPLs).
  • Current computer-aided diagnostic (CAD) systems for EBUS often use static 2D frames, neglecting temporal video dynamics.
  • Temporal dynamics in EBUS videos may offer vital clues for distinguishing benign from malignant lesions.

Purpose of the Study:

  • To develop an artificial intelligence (AI) model for analyzing full-length EBUS videos.
  • To incorporate temporal modeling into AI for improved classification of PPLs.
  • To enhance the diagnostic accuracy of EBUS-TBB using AI.

Main Methods:

  • Retrospective collection of EBUS videos from 465 patients (Nov 2019 - Jan 2022).
  • Utilized a dual-path 3D convolutional network (SlowFast) for spatiotemporal feature extraction.
  • Integrated contrastive learning (SwAV) to improve model generalizability on clinical EBUS data.

Main Results:

  • The SlowFast + SwAV_Frame model achieved an AUC of 0.857 on the validation set, outperforming pulmonologists (AUC not provided).
  • On the test set, the AI model achieved an AUC of 0.823, with 76.92% accuracy and 84.85% sensitivity.
  • The AI model demonstrated superior performance compared to conventional 2D architectures and human expert assessment.

Conclusions:

  • This study presents the first CAD framework for real-time malignancy classification using full-length EBUS videos.
  • The AI framework reduces reliance on manual image selection and enhances diagnostic efficiency.
  • The AI model shows significant potential for clinical application due to its high accuracy in classifying PPLs.