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.

翻译：深度学习（DL）已在多个高风险领域实现稳健的能力评估。然而，深度学习模型的可推广性常受限于其对大量数据的需求及特定训练领域的局限性，这使得模型难以迁移至数据稀缺的新任务。因此，领域适应成为深度学习在实际场景中应用的关键要素。本文提出一种新颖的元学习模型A-VBANet，该模型能够通过单次学习实现领域无关的技能评估。我们通过在五个腹腔镜与机器人模拟器及真实腹腔镜胆囊切除术中评估外科手术技能，验证了该方法。在模拟任务中，模型在单次学习与少次学习场景下分别达到最高99.5%和99.9%的适应准确率，在腹腔镜胆囊切除术中达到89.7%。本研究首次在关键领域提出领域无关的技能评估方法，为深度学习在数据有限的多领域实际场景中的广泛应用奠定基础。