# Early Operative Difficulty Assessment in Laparoscopic Cholecystectomy via Snapshot-Centric Video Analysis

arXiv: https://arxiv.org/abs/2502.07008 · HTML: https://mlsanity.com/paper/2502.07008/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [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)
- [Dissecting Self-Supervised Learning Methods for Surgical Computer Vision](https://mlsanity.com/paper/2207.00449/) (2022)
- [CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions](https://mlsanity.com/paper/1910.09031/) (2019)
- [Latent Graph Representations for Critical View of Safety Assessment](https://mlsanity.com/paper/2212.04155/) (2022)
- [Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition](https://mlsanity.com/paper/2211.16963/) (2022)
- [Surgical Action Triplet Detection by Mixed Supervised Learning of Instrument-Tissue Interactions](https://mlsanity.com/paper/2307.09548/) (2023)
- [The Wisdom of Crowds: Temporal Progressive Attention for Early Action Prediction](https://mlsanity.com/paper/2204.13340/) (2022)
- [MOSaiC: a Web-based Platform for Collaborative Medical Video Assessment and Annotation](https://mlsanity.com/paper/2312.08593/) (2023)
- [Future-State Predicting LSTM for Early Surgery Type Recognition](https://mlsanity.com/paper/1811.11727/) (2018)

## Built on by (most cited first, 1 of 1)

- [SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy](https://mlsanity.com/paper/2603.29962/) (2026)

## Abstract (arXiv)

Purpose: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limited efforts explore LCOD using intraoperative video data. Early recognition of LCOD could allow prompt review by expert surgeons, enhance operating room (OR) planning, and improve surgical outcomes. Methods: We propose the clinical task of early LCOD assessment using limited video observations. We design SurgPrOD, a deep learning model to assess LCOD by analyzing features from global and local temporal resolutions (snapshots) of the observed LC video. Also, we propose a novel snapshot-centric attention (SCA) module, acting across snapshots, to enhance LCOD prediction. We introduce the CholeScore dataset, featuring video-level LCOD labels to validate our method. Results: We evaluate SurgPrOD on 3 LCOD assessment scales in the CholeScore dataset. On our new metric assessing early and stable correct predictions, SurgPrOD surpasses baselines by at least 0.22 points. SurgPrOD improves over baselines by at least 9 and 5 percentage points in F1 score and top1-accuracy, respectively, demonstrating its effectiveness in correct predictions. Conclusion: We propose a new task for early LCOD assessment and a novel model, SurgPrOD analyzing surgical video from global and local perspectives. Our results on the CholeScore dataset establishes a new benchmark to study LCOD using intraoperative video data.