The operating room (OR) is a dynamic and complex environment consisting of a
multidisciplinary team working together in a high take environment to provide
safe and efficient patient care. Additionally, surgeons are frequently exposed
to multiple psycho-organisational stressors that may cause negative
repercussions on their immediate technical performance and long-term health.
Many factors can therefore contribute to increasing the Cognitive Workload
(CWL) such as temporal pressures, unfamiliar anatomy or distractions in the OR.
In this paper, a cascade of two machine learning approaches is suggested for
the multimodal recognition of CWL in four different surgical task conditions.
Firstly, a model based on the concept of transfer learning is used to identify
if a surgeon is experiencing any CWL. Secondly, a Convolutional Neural Network
(CNN) uses this information to identify different degrees of CWL associated to
each surgical task. The suggested multimodal approach considers adjacent
signals from electroencephalogram (EEG), functional near-infrared spectroscopy
(fNIRS) and eye pupil diameter. The concatenation of signals allows complex
correlations in terms of time (temporal) and channel location (spatial). Data
collection was performed by a Multi-sensing AI Environment for Surgical Task &
Role Optimisation platform (MAESTRO) developed at the Hamlyn Centre, Imperial
College London. To compare the performance of the proposed methodology, a
number of state-of-art machine learning techniques have been implemented. The
tests show that the proposed model has a precision of 93%.

本文提出了一种联合三个信号测量 Cognitive Workload 的方法，该方法基于机器学习和神经网络技术，可以识别手术过程中医生的脑力负荷情况，并精确地将其分类，比较结果表明有 93％的准确度。