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

• URL: http://arxiv.org/abs/2502.07008v1
• Date: Mon, 10 Feb 2025 20:14:01 GMT
• Title: Early Operative Difficulty Assessment in Laparoscopic Cholecystectomy via Snapshot-Centric Video Analysis
• Authors: Saurav Sharma, Maria Vannucci, Leonardo Pestana Legori, Mario Scaglia, Giovanni Guglielmo Laracca, Didier Mutter, Sergio Alfieri, Pietro Mascagni, Nicolas Padoy,
• Abstract summary: We propose the clinical task of early LCOD assessment using limited video observations.<n>We design SurgPrOD, a deep learning model to assess LCOD by analyzing features from global and local temporal resolutions.<n>We introduce the CholeScore dataset, featuring video-level LCOD labels to validate our method.
• Score: 3.104121871683839
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 recog- nition 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.

Related papers

• NeuroABench: A Multimodal Evaluation Benchmark for Neurosurgical Anatomy Identification [56.133469598652624]
  Multimodal Large Language Models (MLLMs) have shown significant potential in surgical video understanding.<n>Neurosurgical Anatomy Benchmark (NeuroABench) is first multimodal benchmark explicitly created to evaluate anatomical understanding in the neurosurgical domain.<n>NeuroABench consists of 9 hours of annotated neurosurgical videos covering 89 distinct procedures.
  arXiv  Detail & Related papers  (2025-12-07T17:00:25Z)
• Learning from Single Timestamps: Complexity Estimation in Laparoscopic Cholecystectomy [8.637329291879162]
  We introduce STC-Net, a novel framework for SingleTimestamp-based Complexity estimation in Laparoscopic Cholecystectomy (LC) videos.<n>It operates directly on full videos under weak temporal supervision.<n>It achieves an accuracy of 62.11% and an F1-score of 61.42%, outperforming non-localized baselines.
  arXiv  Detail & Related papers  (2025-11-06T16:39:55Z)
• PolypSeg-GradCAM: Towards Explainable Computer-Aided Gastrointestinal Disease Detection Using U-Net Based Segmentation and Grad-CAM Visualization on the Kvasir Dataset [7.02937797539818]
  Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide.<n>Deep learning methods have demonstrated strong potential for automated polyp analysis, but their limited interpretability remains a barrier to clinical adoption.<n>We present PolypSeg-GradCAM, a framework that integrates the U-Net architecture with Gradient-weighted Class Activation Mapping (Grad-CAM) for transparent polyp segmentation.
  arXiv  Detail & Related papers  (2025-09-17T02:57:33Z)
• CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography [32.67225649913398]
  We introduce CM-UNet, which leverages self-supervised pre-training on unannotated datasets and transfer learning on limited annotated data.<n>Fine-tuning CM-UNet with only 18 annotated images instead of 500 resulted in a 15.2% decrease in Dice score.<n>This is one of the first studies to highlight the importance of self-supervised learning in improving coronary artery segmentation from X-ray angiography.
  arXiv  Detail & Related papers  (2025-07-22T21:27:34Z)
• SurgeryLSTM: A Time-Aware Neural Model for Accurate and Explainable Length of Stay Prediction After Spine Surgery [44.119171920037196]
  We develop and evaluate machine learning (ML) models for predicting length of stay (LOS) in elective spine surgery.<n>We compare traditional ML models with our developed model, SurgeryLSTM, a masked bidirectional long short-term memory (BiLSTM) with an attention.<n>Performance was evaluated using the coefficient of determination (R2) and key predictors were identified using explainable AI.
  arXiv  Detail & Related papers  (2025-07-15T01:18:28Z)
• AGIR: Assessing 3D Gait Impairment with Reasoning based on LLMs [0.0]
  gait impairment plays an important role in early diagnosis, disease monitoring, and treatment evaluation for neurodegenerative diseases. Recent deep learning-based approaches have consistently improved classification accuracies, but they often lack interpretability. We introduce AGIR, a novel pipeline consisting of a pre-trained VQ-VAE motion tokenizer and a Large Language Model (LLM) fine-tuned over pairs of motion tokens.
  arXiv  Detail & Related papers  (2025-03-23T17:12:16Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references. We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey. Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• $\mathsf{CSMAE~}$:~Cataract Surgical Masked Autoencoder (MAE) based Pre-training [25.71088804562768]
  We introduce a Masked Autoencoder (MAE)-based pretraining approach, specifically developed for Cataract Surgery video analysis. Instead of randomly selecting tokens for masking they are selected based on the importance of the token token. This approach surpasses current state-of-the-art self-supervised pretraining and adapter-based learning methods by a significant margin.
  arXiv  Detail & Related papers  (2025-02-12T22:24:49Z)
• KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation [46.57880203321858]
  We propose a novel network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module. Our approach is based on two key components: Kalman filter (KF) based linear deformable convolution (LD) and cross-attention (CA) modules. The proposed method is evaluated on retinal fundus image datasets (DRIVE, CHASE_BD1, and STARE) as well as the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-10-28T16:00:42Z)
• Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images [45.29301790646322]
  Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning.
  arXiv  Detail & Related papers  (2024-07-02T19:30:25Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion [24.560417980602928]
  Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. We introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process.
  arXiv  Detail & Related papers  (2023-07-05T17:23:42Z)
• One-shot skill assessment in high-stakes domains with limited data via meta learning [0.0]
  A-VBANet is a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our model successfully adapted with accuracies up to 99.5% in one-shot and 99.9% in few-shot settings for simulated tasks and 89.7% for laparoscopic cholecystectomy.
  arXiv  Detail & Related papers  (2022-12-16T01:04:52Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• Surgical data science for safe cholecystectomy: a protocol for segmentation of hepatocystic anatomy and assessment of the critical view of safety [2.7457279076218666]
  We present a protocol, checklists, and visual examples to promote consistent annotation of hepatocystic anatomy and CVS criteria. Deep learning models for surgical video analysis could support visual tasks such as assessing the critical view of safety in laparoscopic cholecystectomy.
  arXiv  Detail & Related papers  (2021-06-21T08:27:38Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset [6.5800499500032705]
  We design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. We exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our solution outperforms single human annotators in rating accuracy and consistency.
  arXiv  Detail & Related papers  (2020-06-08T13:55:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.