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

arXiv: https://arxiv.org/abs/2608.01058 · HTML: https://mlsanity.com/paper/2608.01058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 59 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 29 of 29)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks](https://mlsanity.com/paper/2003.10751/) (2020)
- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020)
- [Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer](https://mlsanity.com/paper/2103.09712/) (2021)
- [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019)
- [Temporal Memory Relation Network for Workflow Recognition from Surgical Video](https://mlsanity.com/paper/2103.16327/) (2021)
- [OperA: Attention-Regularized Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2103.03873/) (2021)
- [LoViT: Long Video Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2305.08989/) (2023)
- [Exploiting the potential of unlabeled endoscopic video data with self-supervised learning](https://mlsanity.com/paper/1711.09726/) (2017)
- [Scaling up self-supervised learning for improved surgical foundation models](https://mlsanity.com/paper/2501.09436/) (2025)
- [Less is More: Surgical Phase Recognition from Timestamp Supervision](https://mlsanity.com/paper/2202.08199/) (2022)
- [An Empirical Study on Activity Recognition in Long Surgical Videos](https://mlsanity.com/paper/2205.02805/) (2022)
- [Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos](https://mlsanity.com/paper/2301.07053/) (2023)
- [TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition](https://mlsanity.com/paper/2307.09997/) (2023)
- [Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer](https://mlsanity.com/paper/2302.09187/) (2023)
- [Advanced Capsule Networks via Context Awareness](https://mlsanity.com/paper/1903.07497/) (2019)
- [An Improvement for Capsule Networks using Depthwise Separable Convolution](https://mlsanity.com/paper/2007.15167/) (2020)
- [Evaluating Model Performance with Hard-Swish Activation Function Adjustments](https://mlsanity.com/paper/2410.06879/) (2024)
- [Offline and Online Deep Learning for Image Recognition](https://mlsanity.com/paper/1903.07479/) (2019)
- [Action Recognition for American Sign Language](https://mlsanity.com/paper/2205.12261/) (2022)
- [Friends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2401.11644/) (2024)
- [Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication](https://mlsanity.com/paper/2412.16195/) (2024)
- [Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentation](https://mlsanity.com/paper/2501.11153/) (2025)

## Abstract (arXiv)

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.