Model selection and real-time skill assessment for suturing in robotic surgery

• URL: http://arxiv.org/abs/2601.12012v1
• Date: Sat, 17 Jan 2026 11:28:59 GMT
• Title: Model selection and real-time skill assessment for suturing in robotic surgery
• Abstract summary: We examine methods to achieve real-time prediction of surgical skill level in real-time based on OSATS scores.<n>For the model design, we evaluate the effectiveness of multimodal deep learning models for predicting surgical skill levels.<n>For the skill-level-based cross-validation, we separately trained models on surgeons with different skill levels.
• Score: 33.229894165318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated feedback systems have the potential to provide objective skill assessment for training and evaluation in robot-assisted surgery. In this study, we examine methods to achieve real-time prediction of surgical skill level in real-time based on Objective Structured Assessment of Technical Skills (OSATS) scores. Using data acquired from the da Vinci Surgical System, we carry out three main analyses, focusing on model design, their real-time performance, and their skill-level-based cross-validation training. For the model design, we evaluate the effectiveness of multimodal deep learning models for predicting surgical skill levels using synchronized kinematic and vision data. Our models include separate unimodal baselines and fusion architectures that integrate features from both modalities and are evaluated using mean Spearman's correlation coefficients, demonstrating that the fusion model consistently outperforms unimodal models for real-time predictions. For the real-time performance, we observe the prediction's trend over time and highlight correlation with the surgeon's gestures. For the skill-level-based cross-validation, we separately trained models on surgeons with different skill levels, which showed that high-skill demonstrations allow for better performance than those trained on low-skilled ones and generalize well to similarly skilled participants. Our findings show that multimodal learning allows more stable fine-grained evaluation of surgical performance and highlights the value of expert-level training data for model generalization.

Related papers

• Explainable AI-Powered Framework for Video-Based Skill Assessment in Cataract Surgery [5.586191108738563]
  We present the world's largest dataset of cataract surgery videos, comprising 2,000 recordings.<n>We propose an AI-powered analytical framework that employs advanced computer vision and signal-processing techniques to automatically evaluate surgical videos.<n>A significant advantage of our framework over previous methods lies precisely in its explainability of outputs, elevating it beyond merely an opaque skill classification tool.
  arXiv  Detail & Related papers  (2026-08-18T08:45:27Z)
• ExpOS: Explainable Open-Surgery Skills Assessment Using 3D Hand Reconstruction [1.9116784879310027]
  We present ExpOS, an explainable framework for data-driven assessment of open-surgery skills.<n>We trained and evaluated the method on 221 videos of medical students performing three open-surgery tasks.
  arXiv  Detail & Related papers  (2026-05-22T14:06:41Z)
• AI-Driven Evaluation of Surgical Skill via Action Recognition [4.92174988745803]
  We propose an AI-driven framework for the automated assessment of microanastomosis performance.<n>Performance is evaluated along five aspects of microanastomosis skill, including overall action execution, motion quality during procedure-critical actions, and general instrument handling.
  arXiv  Detail & Related papers  (2025-12-30T18:45:34Z)
• Analysis of Transferability Estimation Metrics for Surgical Phase Recognition [3.3285108719932555]
  Fine-tuning pre-trained models has become a cornerstone of modern machine learning, allowing practitioners to achieve high performance with limited labeled data.<n>In surgical video analysis, where expert annotations are especially time-consuming and costly, identifying the most suitable pre-trained model for a downstream task is both critical and challenging.<n>We provide the first comprehensive benchmark of three representative metrics, LogME, H-Score, and TransRate, on two diverse datasets.
  arXiv  Detail & Related papers  (2025-08-22T18:05:33Z)
• Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics [0.0]
  The study focuses on class distinction and performance evaluation, combining OpenCV with deep learning techniques.<n>The system effectively distinguishes among three classes: authorized personnel (admin), intruders, and non-human entities.
  arXiv  Detail & Related papers  (2025-03-24T19:36:47Z)
• Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment [66.6041949490137]
  We propose a method that integrates information from transcribed verbal feedback and corresponding surgical video to predict feedback effectiveness. Our findings show that both transcribed feedback and surgical video are individually predictive of trainee behavior changes. Our results demonstrate the potential of multi-modal learning to advance the automated assessment of surgical feedback.
  arXiv  Detail & Related papers  (2024-11-17T00:13:00Z)
• How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation [6.547981908229007]
  We show how architectural and framework biases combine to influence model performance.<n>Experiments show imputation performance variations of up to 20% based on preprocessing and implementation choices.<n>We identify critical gaps between current deep imputation methods and medical requirements.
  arXiv  Detail & Related papers  (2024-07-11T12:33:28Z)
• Distilled Datamodel with Reverse Gradient Matching [74.75248610868685]
  We introduce an efficient framework for assessing data impact, comprising offline training and online evaluation stages. Our proposed method achieves comparable model behavior evaluation while significantly speeding up the process compared to the direct retraining method.
  arXiv  Detail & Related papers  (2024-04-22T09:16:14Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model [74.62272538148245]
  We show that for arbitrary pairings of pretrained models, one model extracts significant data context unavailable in the other. We investigate if it is possible to transfer such "complementary" knowledge from one model to another without performance degradation.
  arXiv  Detail & Related papers  (2023-10-26T17:59:46Z)
• Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning [0.8612287536028312]
  We propose a deep learning (DL) model that can automatically and objectively provide a high-stakes summative assessment of surgical skill execution. Formative assessment is generated using heatmaps of visual features that correlate with surgical performance.
  arXiv  Detail & Related papers  (2022-03-17T20:07:48Z)
• Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications [72.52228843498193]
  In training deep learning models, a compromise often must be made between performance and trust. In this work, we integrate a new surrogate loss with self-supervised learning for computer-aided screening of COVID-19 patients.
  arXiv  Detail & Related papers  (2021-12-14T21:16:52Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)

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.