R-Trans -- A Recurrent Transformer Model for Clinical Feedback in Surgical Skill Assessment
<|reference_start|>R-Trans -- A Recurrent Transformer Model for Clinical Feedback in Surgical Skill Assessment: 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.<|reference_end|>
@article{quarez2024recap:,
    title={ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in
  Robotic Surgical Skill Assessment},
    author={Julien Quarez, Marc Modat, Sebastien Ourselin, Jonathan Shapey,
  Alejandro Granados},
    journal={arXiv preprint arXiv:2407.05180},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05180},
    primaryClass={cs.CV cs.AI cs.LG eess.IV}
}