R-Trans -- A Recurrent Transformer Model for Clinical Feedback in Surgical Skill Assessment
- URL: http://arxiv.org/abs/2407.05180v1
- Date: Mon, 22 Apr 2024 10:33:06 GMT
- Title: R-Trans -- A Recurrent Transformer Model for Clinical Feedback in Surgical Skill Assessment
- Authors: Julien Quarez, Matthew Elliot, Oscar Maccormac, Nawal Khan, Marc Modat, Sebastien Ourselin, Jonathan Shapey, Alejandro Granados,
- Abstract summary: We develop a recurrent transformer model that outputs the surgeon's performance throughout their training session.
These scores are averaged and aggregated to produce a GRS prediction.
We report Spearman's Correlation Coefficient ( SCC), demonstrating that our model outperforms SOTA models for all tasks.
- Score: 35.27723246803406
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In surgical skill assessment, Objective Structured Assessments of Technical Skills (OSATS scores) and the Global Rating Scale (GRS) are established tools for evaluating the performance of surgeons during training. These metrics, coupled with feedback on their performance, enable surgeons to improve and achieve standards of practice. Recent studies on the open-source dataset JIGSAW, which contains both GRS and OSATS labels, have focused on regressing GRS scores from kinematic signals, video data, or a combination of both. In this paper, we argue that regressing the GRS score, a unitless value, by itself is too restrictive, and variations throughout the surgical trial do not hold significant clinical meaning. To address this gap, we developed a recurrent transformer model that outputs the surgeon's performance throughout their training session by relating the model's hidden states to five OSATS scores derived from kinematic signals. These scores are averaged and aggregated to produce a GRS prediction, enabling assessment of the model's performance against the state-of-the-art (SOTA). We report Spearman's Correlation Coefficient (SCC), demonstrating that our model outperforms SOTA models for all tasks, except for Suturing under the leave-one-subject-out (LOSO) scheme (SCC 0.68-0.89), while achieving comparable performance for suturing and across tasks under the leave-one-user-out (LOUO) scheme (SCC 0.45-0.68) and beating SOTA for Needle Passing (0.69). We argue that relating final OSATS scores to short instances throughout a surgeon's procedure is more clinically meaningful than a single GRS score. This approach also allows us to translate quantitative predictions into qualitative feedback, which is crucial for any automated surgical skill assessment pipeline. A senior surgeon validated our model's behaviour and agreed with the semi-supervised predictions 77 \% (p = 0.006) of the time.
Related papers
- ZEAL: Surgical Skill Assessment with Zero-shot Tool Inference Using Unified Foundation Model [0.07143413923310668]
This study introduces ZEAL (surgical skill assessment with Zero-shot surgical tool segmentation with a unifiEd foundAtion modeL)
ZEAL predicts segmentation masks, capturing essential features of both instruments and surroundings.
It produces a surgical skill score, offering an objective measure of proficiency.
arXiv Detail & Related papers (2024-07-03T01:20:56Z) - Fairness Evolution in Continual Learning for Medical Imaging [47.52603262576663]
We study the behavior of Continual Learning (CL) strategies in medical imaging regarding classification performance.
We evaluate the Replay, Learning without Forgetting (LwF), LwF, and Pseudo-Label strategies.
LwF and Pseudo-Label exhibit optimal classification performance, but when including fairness metrics in the evaluation, it is clear that Pseudo-Label is less biased.
arXiv Detail & Related papers (2024-04-10T09:48:52Z) - Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward
Comprehensive Benchmarks [60.82579717007963]
We introduce an enhanced evaluation framework designed to more accurately gauge the effectiveness, consistency, and overall capability of Graph Contrastive Learning (GCL) methods.
arXiv Detail & Related papers (2024-02-24T01:47:56Z) - Semi-supervised ViT knowledge distillation network with style transfer
normalization for colorectal liver metastases survival prediction [1.283897253352624]
We propose an end-to-end approach for automated prognosis prediction using histology slides stained with H&E and HPS.
We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline.
We exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer (ViT) in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction.
arXiv Detail & Related papers (2023-11-17T03:32:11Z) - Rethinking Semi-Supervised Medical Image Segmentation: A
Variance-Reduction Perspective [51.70661197256033]
We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation.
We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks.
We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
arXiv Detail & Related papers (2023-02-03T13:50:25Z) - Assessment of Treatment Effect Estimators for Heavy-Tailed Data [70.72363097550483]
A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of ground truth (or validation set) to test their performance.
We provide a novel cross-validation-like methodology to address this challenge.
We evaluate our methodology across 709 RCTs implemented in the Amazon supply chain.
arXiv Detail & Related papers (2021-12-14T17:53:01Z) - Towards Unified Surgical Skill Assessment [18.601526803020885]
We propose a unified multi-path framework for automatic surgical skill assessment.
We conduct experiments on the JIGSAWS dataset of simulated surgical tasks, and a new clinical dataset of real laparoscopic surgeries.
arXiv Detail & Related papers (2021-06-02T09:06:43Z) - Bootstrapping Your Own Positive Sample: Contrastive Learning With
Electronic Health Record Data [62.29031007761901]
This paper proposes a novel contrastive regularized clinical classification model.
We introduce two unique positive sampling strategies specifically tailored for EHR data.
Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
arXiv Detail & Related papers (2021-04-07T06:02:04Z) - Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of
Operating Field [18.643159726513133]
Surgical skill assessment is studied in this paper on a real clinical dataset.
The clearness of operating field (COF) is identified as a good proxy for overall surgical skills.
An objective and automated framework is proposed to predict surgical skills through the proxy of COF.
In experiments, the proposed method achieves 0.55 Spearman's correlation with the ground truth of overall technical skill.
arXiv Detail & Related papers (2020-08-27T07:12:16Z) - Temporal Segmentation of Surgical Sub-tasks through Deep Learning with
Multiple Data Sources [14.677001578868872]
We propose a unified surgical state estimation model based on the actions performed or events occurred as the task progresses.
We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) and a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging.
Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models.
arXiv Detail & Related papers (2020-02-07T17:49:08Z)
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.