DeepPhase: Surgical Phase Recognition in CATARACTS Videos
This work addresses the problem of standardizing and monitoring cataract surgeries for surgeons, but it is incremental as it builds on existing methods with comparable results.
The paper tackled automated surgical workflow analysis in cataract surgery videos by using deep learning to detect surgical instruments and infer procedural phases, achieving 99% accuracy for tool detection and 78% for phase recognition.
Automated surgical workflow analysis and understanding can assist surgeons to standardize procedures and enhance post-surgical assessment and indexing, as well as, interventional monitoring. Computer-assisted interventional (CAI) systems based on video can perform workflow estimation through surgical instruments' recognition while linking them to an ontology of procedural phases. In this work, we adopt a deep learning paradigm to detect surgical instruments in cataract surgery videos which in turn feed a surgical phase inference recurrent network that encodes temporal aspects of phase steps within the phase classification. Our models present comparable to state-of-the-art results for surgical tool detection and phase recognition with accuracies of 99 and 78% respectively.