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The SAGES Critical View of Safety Challenge: A Global Benchmark for AI-Assisted Surgical Quality Assessment
Authors:
Deepak Alapatt,
Jennifer Eckhoff,
Zhiliang Lyu,
Yutong Ban,
Jean-Paul Mazellier,
Sarah Choksi,
Kunyi Yang,
Po-Hsing Chiang,
Noemi Zorzetti,
Samuele Cannas,
Daniel Neimark,
Omri Bar,
Amine Yamlahi,
Jakob Hennighausen,
Xiaohan Wang,
Rui Li,
Long Liang,
Yuxian Wang,
Saurabh Koju,
Binod Bhattarai,
Tim Jaspers,
Zhehua Mao,
Anjana Wijekoon,
Jun Ma,
Yinan Xu
, et al. (16 additional authors not shown)
Abstract:
Advances in artificial intelligence (AI) for surgical quality assessment promise to democratize access to expertise, with applications in training, guidance, and accreditation. This study presents the SAGES Critical View of Safety (CVS) Challenge, the first AI competition organized by a surgical society, using the CVS in laparoscopic cholecystectomy, a universally recommended yet inconsistently pe…
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Advances in artificial intelligence (AI) for surgical quality assessment promise to democratize access to expertise, with applications in training, guidance, and accreditation. This study presents the SAGES Critical View of Safety (CVS) Challenge, the first AI competition organized by a surgical society, using the CVS in laparoscopic cholecystectomy, a universally recommended yet inconsistently performed safety step, as an exemplar of surgical quality assessment. A global collaboration across 54 institutions in 24 countries engaged hundreds of clinicians and engineers to curate 1,000 videos annotated by 20 surgical experts according to a consensus-validated protocol. The challenge addressed key barriers to real-world deployment in surgery, including achieving high performance, capturing uncertainty in subjective assessment, and ensuring robustness to clinical variability. To enable this scale of effort, we developed EndoGlacier, a framework for managing large, heterogeneous surgical video and multi-annotator workflows. Thirteen international teams participated, achieving up to a 17% relative gain in assessment performance, over 80% reduction in calibration error, and a 17% relative improvement in robustness over the state-of-the-art. Analysis of results highlighted methodological trends linked to model performance, providing guidance for future research toward robust, clinically deployable AI for surgical quality assessment.
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Submitted 28 January, 2026; v1 submitted 21 September, 2025;
originally announced September 2025.
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Holistic Surgical Phase Recognition with Hierarchical Input Dependent State Space Models
Authors:
Haoyang Wu,
Tsun-Hsuan Wang,
Mathias Lechner,
Ramin Hasani,
Jennifer A. Eckhoff,
Paul Pak,
Ozanan R. Meireles,
Guy Rosman,
Yutong Ban,
Daniela Rus
Abstract:
Surgical workflow analysis is essential in robot-assisted surgeries, yet the long duration of such procedures poses significant challenges for comprehensive video analysis. Recent approaches have predominantly relied on transformer models; however, their quadratic attention mechanism restricts efficient processing of lengthy surgical videos. In this paper, we propose a novel hierarchical input-dep…
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Surgical workflow analysis is essential in robot-assisted surgeries, yet the long duration of such procedures poses significant challenges for comprehensive video analysis. Recent approaches have predominantly relied on transformer models; however, their quadratic attention mechanism restricts efficient processing of lengthy surgical videos. In this paper, we propose a novel hierarchical input-dependent state space model that leverages the linear scaling property of state space models to enable decision making on full-length videos while capturing both local and global dynamics. Our framework incorporates a temporally consistent visual feature extractor, which appends a state space model head to a visual feature extractor to propagate temporal information. The proposed model consists of two key modules: a local-aggregation state space model block that effectively captures intricate local dynamics, and a global-relation state space model block that models temporal dependencies across the entire video. The model is trained using a hybrid discrete-continuous supervision strategy, where both signals of discrete phase labels and continuous phase progresses are propagated through the network. Experiments have shown that our method outperforms the current state-of-the-art methods by a large margin (+2.8% on Cholec80, +4.3% on MICCAI2016, and +12.9% on Heichole datasets). Code will be publicly available after paper acceptance.
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Submitted 26 June, 2025;
originally announced June 2025.
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Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery
Authors:
Lianhao Yin,
Yutong Ban,
Jennifer Eckhoff,
Ozanan Meireles,
Daniela Rus,
Guy Rosman
Abstract:
Understanding and anticipating intraoperative events and actions is critical for intraoperative assistance and decision-making during minimally invasive surgery. Automated prediction of events, actions, and the following consequences is addressed through various computational approaches with the objective of augmenting surgeons' perception and decision-making capabilities. We propose a predictive…
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Understanding and anticipating intraoperative events and actions is critical for intraoperative assistance and decision-making during minimally invasive surgery. Automated prediction of events, actions, and the following consequences is addressed through various computational approaches with the objective of augmenting surgeons' perception and decision-making capabilities. We propose a predictive neural network that is capable of understanding and predicting critical interactive aspects of surgical workflow from intra-abdominal video, while flexibly leveraging surgical knowledge graphs. The approach incorporates a hypergraph-transformer (HGT) structure that encodes expert knowledge into the network design and predicts the hidden embedding of the graph. We verify our approach on established surgical datasets and applications, including the detection and prediction of action triplets, and the achievement of the Critical View of Safety (CVS). Moreover, we address specific, safety-related tasks, such as predicting the clipping of cystic duct or artery without prior achievement of the CVS. Our results demonstrate the superiority of our approach compared to unstructured alternatives.
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Submitted 10 March, 2025; v1 submitted 2 February, 2024;
originally announced February 2024.
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Concept Graph Neural Networks for Surgical Video Understanding
Authors:
Yutong Ban,
Jennifer A. Eckhoff,
Thomas M. Ward,
Daniel A. Hashimoto,
Ozanan R. Meireles,
Daniela Rus,
Guy Rosman
Abstract:
We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see.
This ability is crucial in application domains which entail reasoning about multiple entities and concepts, such as AI-augmented surgery. In this paper, we propose a novel way of integrating conceptual knowledge into temporal analysis tasks via temporal concept graph networks. In th…
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We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see.
This ability is crucial in application domains which entail reasoning about multiple entities and concepts, such as AI-augmented surgery. In this paper, we propose a novel way of integrating conceptual knowledge into temporal analysis tasks via temporal concept graph networks. In the proposed networks, a global knowledge graph is incorporated into the temporal analysis of surgical instances, learning the meaning of concepts and relations as they apply to the data. We demonstrate our results in surgical video data for tasks such as verification of critical view of safety, as well as estimation of Parkland grading scale. The results show that our method improves the recognition and detection of complex benchmarks as well as enables other analytic applications of interest.
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Submitted 25 April, 2023; v1 submitted 27 February, 2022;
originally announced February 2022.
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SUPR-GAN: SUrgical PRediction GAN for Event Anticipation in Laparoscopic and Robotic Surgery
Authors:
Yutong Ban,
Guy Rosman,
Jennifer A. Eckhoff,
Thomas M. Ward,
Daniel A. Hashimoto,
Taisei Kondo,
Hidekazu Iwaki,
Ozanan R. Meireles,
Daniela Rus
Abstract:
Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation. In this work, we move beyond mere identification of past surgical phases, into the prediction of future surgical steps and specification of the transitions between them. We use a novel Generative Adv…
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Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation. In this work, we move beyond mere identification of past surgical phases, into the prediction of future surgical steps and specification of the transitions between them. We use a novel Generative Adversarial Network (GAN) formulation to sample future surgical phases trajectories conditioned on past video frames from laparoscopic cholecystectomy (LC) videos and compare it to state-of-the-art approaches for surgical video analysis and alternative prediction methods. We demonstrate the GAN formulation's effectiveness through inferring and predicting the progress of LC videos. We quantify the horizon-accuracy trade-off and explored average performance, as well as the performance on the more challenging, and clinically relevant transitions between phases. Furthermore, we conduct a survey, asking 16 surgeons of different specialties and educational levels to qualitatively evaluate predicted surgery phases.
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Submitted 9 March, 2022; v1 submitted 10 May, 2021;
originally announced May 2021.