Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom

• URL: http://arxiv.org/abs/2409.17025v1
• Date: Wed, 25 Sep 2024 15:27:44 GMT
• Title: Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom
• Authors: Adrito Das, Bilal Sidiqi, Laurent Mennillo, Zhehua Mao, Mikael Brudfors, Miguel Xochicale, Danyal Z. Khan, Nicola Newall, John G. Hanrahan, Matthew J. Clarkson, Danail Stoyanov, Hani J. Marcus, Sophia Bano,
• Abstract summary: Improved surgical skill is generally associated with improved patient outcomes, but assessment is subjective and labour-intensive. A new public dataset is introduced, focusing on simulated surgery, using the nasal phase of endoscopic pituitary surgery as an exemplar. A Multilayer Perceptron achieved 87% accuracy in predicting surgical skill level (novice or expert), with the "ratio of total procedure time to instrument visible time" correlated with higher surgical skill.
• Score: 9.41936397281689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective; labour-intensive; and requires domain specific expertise. Automated data driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models in minimally invasive surgery. However, these models have been tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery. In this paper, a new public dataset is introduced, focusing on simulated surgery, using the nasal phase of endoscopic pituitary surgery as an exemplar. Simulated surgery allows for a realistic yet repeatable environment, meaning the insights gained from automated assessment can be used by novice surgeons to hone their skills on the simulator before moving to real surgery. PRINTNet (Pituitary Real-time INstrument Tracking Network) has been created as a baseline model for this automated assessment. Consisting of DeepLabV3 for classification and segmentation; StrongSORT for tracking; and the NVIDIA Holoscan SDK for real-time performance, PRINTNet achieved 71.9% Multiple Object Tracking Precision running at 22 Frames Per Second. Using this tracking output, a Multilayer Perceptron achieved 87% accuracy in predicting surgical skill level (novice or expert), with the "ratio of total procedure time to instrument visible time" correlated with higher surgical skill. This therefore demonstrates the feasibility of automated surgical skill assessment in simulated endoscopic pituitary surgery. The new publicly available dataset can be found here: https://doi.org/10.5522/04/26511049.

Related papers

• VISAGE: Video Synthesis using Action Graphs for Surgery [34.21344214645662]
  We introduce the novel task of future video generation in laparoscopic surgery. Our proposed method, VISAGE, leverages the power of action scene graphs to capture the sequential nature of laparoscopic procedures. Results of our experiments demonstrate high-fidelity video generation for laparoscopy procedures.
  arXiv  Detail & Related papers  (2024-10-23T10:28:17Z)
• PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery [46.2901962659261]
  The Pituitary Vision (VisVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space. There were 18-s from 9-teams across 6-countries, using a variety of deep learning models.
  arXiv  Detail & Related papers  (2024-09-02T11:38:06Z)
• Realistic Data Generation for 6D Pose Estimation of Surgical Instruments [4.226502078427161]
  6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs. We propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets.
  arXiv  Detail & Related papers  (2024-06-11T14:59:29Z)
• EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting [53.38166294158047]
  EndoGSLAM is an efficient approach for endoscopic surgeries, which integrates streamlined representation and differentiable Gaussianization. Experiments show that EndoGSLAM achieves a better trade-off between intraoperative availability and reconstruction quality than traditional or neural SLAM approaches.
  arXiv  Detail & Related papers  (2024-03-22T11:27:43Z)
• Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge [69.91670788430162]
  We present the results of the SurgLoc 2022 challenge. The goal was to leverage tool presence data as weak labels for machine learning models trained to detect tools. We conclude by discussing these results in the broader context of machine learning and surgical data science.
  arXiv  Detail & Related papers  (2023-05-11T21:44:39Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• Dissecting Self-Supervised Learning Methods for Surgical Computer Vision [51.370873913181605]
  Self-Supervised Learning (SSL) methods have begun to gain traction in the general computer vision community. The effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection.
  arXiv  Detail & Related papers  (2022-07-01T14:17:11Z)
• Real-time Informative Surgical Skill Assessment with Gaussian Process Learning [12.019641896240245]
  This work presents a novel Gaussian Process Learning-based automatic objective surgical skill assessment method for ESSBSs. The proposed method projects the instrument movements into the endoscope coordinate to reduce the data dimensionality. The experimental results show that the proposed method reaches 100% prediction precision for complete surgical procedures and 90% precision for real-time prediction assessment.
  arXiv  Detail & Related papers  (2021-12-05T15:35:40Z)
• Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery [58.63306322525082]
  Most applications rely on accurate real-time segmentation of high-resolution surgical images. We design a light-weight and highly-efficient deep residual architecture which is tuned to perform real-time inference of high-resolution images.
  arXiv  Detail & Related papers  (2020-07-08T21:38:29Z)
• Recurrent and Spiking Modeling of Sparse Surgical Kinematics [0.8458020117487898]
  A growing number of studies have used machine learning to analyze video and kinematic data captured from surgical robots. In this study, we explore the possibility of using only kinematic data to predict surgeons of similar skill levels. We report that it is possible to identify surgical fellows receiving near perfect scores in the simulation exercises based on their motion characteristics alone.
  arXiv  Detail & Related papers  (2020-05-12T15:41:45Z)

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.