Extended KAFR: A kinematic-adaptive paradigm for the efficient analysis of surgical video

2026-08-02

AI Summary

This work addresses the computational burden in laparoscopic surgery video analysis caused by redundant duration, motion artifacts, and low visual quality. To tackle this, the authors propose an adaptive keyframe selection method driven by surgical instrument motion dynamics. Specifically, a fine-tuned YOLO model detects and segments instruments, and keyframes are dynamically selected based on their displacement or velocity changes. These frames are then fed into an X3D model for surgical phase classification. Evaluated on the Cholec80 dataset, the method achieves a 91.0% F1 score using only 0.58% of the total frames—approximately seven times fewer than the conventional 4% sampling rate—matching the performance of state-of-the-art models such as LoViT and Trans-SVNet. This study marks the first successful application of kinematics-driven adaptive sampling to complex laparoscopic scenarios, enabling highly accurate phase recognition at extremely low sampling rates.

Abstract

Artificial Intelligence is increasingly applied to surgical video analysis for phase segmentation, skill assessment, and workflow optimization. A key challenge is the length of surgical recordings, often one to several hours, creating substantial computational burden. We previously developed Kinematics-Adaptive Frame Recognition (KAFR) for robotic surgery, showing that tracking tool motion effectively identifies informative frames while filtering redundant content. However, laparoscopic surgery introduces additional challenges: manual camera control causes frequent motion artifacts, and image quality is generally lower than robotic systems. This study evaluates whether KAFR generalizes to laparoscopic surgery using the Cholec80 benchmark, comprising 80 laparoscopic cholecystectomy procedures annotated for seven surgical phases. KAFR operates in three stages: a fine-tuned YOLO model detects and segments surgical tools; frames are adaptively selected based on tool displacement or velocity variation; and an X3D model classifies selected frames into surgical phases. KAFR achieved a 91.0\% F1 score using only 0.58\% of frames for phase classification, representing an approximately seven-fold reduction compared to typical 4\% frame sampling, while maintaining performance comparable to LoViT (90.2\%) and Trans-SVNet (89.7\%). These results demonstrate that kinematics-based frame selection transfers effectively to the challenging laparoscopic environment.

Problem

• surgical video analysis
• laparoscopic surgery
• frame selection
• computational efficiency
• motion artifacts

Innovation

• kinematics-adaptive
• frame selection
• surgical phase recognition
• laparoscopic surgery
• computational efficiency