Diagnostic performance of real-time artificial intelligence using deep learning analysis of endoscopic ultrasound videos for gallbladder polypoid lesions.
Authors
Affiliations (7)
Affiliations (7)
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Translational-Clinical Medicine, Gachon University, Incheon, Republic of Korea.
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
- Gachon Biomedical & Convergence Institute, Gachon University Gil Medical Center, Incheon, Republic of Korea.
- Medical Devices R&D Center, Gachon University Gil Medical Center, Incheon, Republic of Korea. [email protected].
- Department of Biomedical Engineering, Pre-medical Course, College of Medicine, Gachon University, Incheon, Republic of Korea. [email protected].
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea. [email protected].
Abstract
Endoscopic ultrasound (EUS) is accurate for diagnosing gallbladder (GB) polyps but is limited by subjective interpretation and operator expertise. Although artificial intelligence (AI) has been applied to still EUS images of GB polyps, its application to EUS videos, which provide richer diagnostic data, remains unexplored. This study evaluated the diagnostic performance of AI models in analyzing EUS videos for GB polyp assessment. EUS videos of patients with histologically confirmed GB polyps were divided into training and validation cohorts. Segmentation models (Attention U-Net, Residual U-Net, and deep understanding convolutional kernel [DUCK] net) identified polyp regions, followed by classification into neoplastic and non-neoplastic polyps using classification models (EfficientNet-B2, ResNet101, and vision transformer). The training cohort included 17 (11 patients) and 79 (39 patients) videos with neoplastic and non-neoplastic polyps, respectively, and the validation cohort included 11 (6 patients) and 25 (11 patients) videos, respectively. Attention U-Net (0.998) and DUCK Net (0.995) achieved the highest training cohort segmentation accuracy. EfficientNet-B2 showed the highest classification performance (accuracy 0.957, recall 0.954, F1-score 0.939, AUC 0.991) and maintained strong performance on the validation dataset (accuracy 0.879, recall 0.968, F1-score 0.917, AUC 0.861). AI demonstrated high accuracy in EUS video-based GB polyp analysis, warranting further prospective validation.