視覚ベース深層学習によるロボット手術の定量化
Quantification of Robotic Surgeries with Vision-Based Deep Learning
手術動画のみから手術フェーズ認識・手技分類・技能評価を同時に行う統一深層学習フレームワークRoboformerを提案し、未見の動画や術者・施設をまたぐ汎化性能を実証した。
著者: Dani Kiyasseh, Runzhuo Ma, Taseen F. Haque, Jessica Nguyen, Christian Wagner, Animashree Anandkumar, Andrew J. Hung
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such surgical activity has been shown to affect long-term patient outcomes. To better understand this relationship, whose mechanics remain unknown for the majority of surgical procedures, we hypothesize that the core elements of surgery must first be quantified in a reliable, objective, and scalable manner. We believe this is a prerequisite for the provision of surgical feedback and modulation of surgeon performance in pursuit of improved patient outcomes. To holistically quantify surgeries, we propose a unified deep learning framework, entitled Roboformer, which operates exclusively on videos recorded during surgery to independently achieve multiple tasks: surgical phase recognition (the what of surgery), gesture classification and skills assessment (the how of surgery). We validated our framework on four video-based datasets of two commonly-encountered types of steps (dissection and suturing) within minimally-invasive robotic surgeries. We demonstrated that our framework can generalize well to unseen videos, surgeons, medical centres, and surgical procedures. We also found that our framework, which naturally lends itself to explainable findings, identified relevant information when achieving a particular task. These findings are likely to instill surgeons with more confidence in our framework's behaviour, increasing the likelihood of clinical adoption, and thus paving the way for more targeted surgical feedback.
