Automatic surgical phase recognition-based skill assessment in laparoscopic distal gastrectomy using multicenter videos.

BACKGROUND: Gastric surgery involves numerous surgical phases; however, its steps can be clearly defined. Deep learning-based surgical phase recognition can promote stylization of gastric surgery with applications in automatic surgical skill assessment. This study aimed to develop a deep learning-based surgical phase-recognition model using multicenter videos of laparoscopic distal gastrectomy, and examine the feasibility of automatic surgical skill assessment using the developed model. METHODS: Surgical videos from 20 hospitals were used. Laparoscopic distal gastrectomy was defined and annotated into nine phases and a deep learning-based image classification model was developed for phase recognition. We examined whether the developed model's output, including the number of frames in each phase and the adequacy of the surgical field development during the phase of supra-pancreatic lymphadenectomy, correlated with the manually assigned skill assessment score. RESULTS: The overall accuracy of phase recognition was 88.8%. Regarding surgical skill assessment based on the number of frames during the phases of lymphadenectomy of the left greater curvature and reconstruction, the number of frames in the high-score group were significantly less than those in the low-score group (829 vs. 1,152, P < 0.01; 1,208 vs. 1,586, P = 0.01, respectively). The output score of the adequacy of the surgical field development, which is the developed model's output, was significantly higher in the high-score group than that in the low-score group (0.975 vs. 0.970, P = 0.04). CONCLUSION: The developed model had high accuracy in phase-recognition tasks and has the potential for application in automatic surgical skill assessment systems.

Anatomical classification of pharyngeal and laryngeal endoscopic images using artificial intelligence.

BACKGROUND: The entire pharynx should be observed endoscopically to avoid missing pharyngeal lesions. An artificial intelligence (AI) model recognizing anatomical locations can help identify blind spots. We developed and evaluated an AI model classifying pharyngeal and laryngeal endoscopic locations. METHODS: The AI model was trained using 5382 endoscopic images, categorized into 15 anatomical locations, and evaluated using an independent dataset of 1110 images. The main outcomes were model accuracy, precision, recall, and F1-score. Moreover, we investigated focused regions in the input images contributing to the model predictions using gradient-weighted class activation mapping (Grad-CAM) and Guided Grad-CAM. RESULTS: Our AI model correctly classified pharyngeal and laryngeal images into 15 anatomical locations, with an accuracy of 93.3%. The weighted averages of precision, recall, and F1-score were 0.934, 0.933, and 0.933, respectively. CONCLUSION: Our AI model has an excellent performance determining pharyngeal and laryngeal anatomical locations, helping endoscopists notify of blind spots.