One-shot skill assessment in high-stakes domains with limited data via meta learning
This addresses the challenge of deploying AI in critical domains like surgery with limited data, though it appears incremental as it builds on meta-learning for domain adaptation.
The paper tackled the problem of deep learning models requiring large datasets and being domain-specific for skill assessment in high-stakes fields by introducing A-VBANet, a meta-learning model that achieved up to 99.5% accuracy in one-shot learning on simulated tasks and 89.7% on real-life laparoscopic cholecystectomy.
Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our methodology has been tested by assessing surgical skills on five laparoscopic and robotic simulators and real-life laparoscopic cholecystectomy. Our model successfully adapted with accuracies up to 99.5% in one-shot and 99.9% in few-shot settings for simulated tasks and 89.7% for laparoscopic cholecystectomy. This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.