Why the study?
It was unknown how much data is needed for an AI-based system to learn surgical context with high fidelity.
A deep learning system can accurately detect surgical phases in laparoscopic cholecystectomy videos, demonstrating high generalization across different medical centers.
Supports AI phase detection across centers; leaves open effects on operative outcomes or training.
AI is becoming ubiquitous, revolutionizing many aspects of our lives. In surgery, it is still a promise. AI has the potential to improve surgeon performance and impact patient care, from post-operative debrief to real-time decision support. But, how much data is needed by an AI-based system to learn surgical context with high fidelity? To answer this question, we leveraged a large-scale, diverse, cholecystectomy video dataset. We assessed surgical workflow recognition and report a deep learning system, that not only detects surgical phases, but does so with high accuracy and is able to generalize to new settings and unseen medical centers. Our findings provide a solid foundation for translating AI applications from research to practice, ushering in a new era of surgical intelligence.
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Bar et al. (2020) studied this question.
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