Department of Biomedical Software Engineering, The Catholic University of Korea, Bucheon, Republic of Korea.
CERVO Brain Research Centre, Laval University, Québec, Canada.
Curr Opin Neurol. 2024 Dec 1;37(6):614-620. doi: 10.1097/WCO.0000000000001322. Epub 2024 Oct 9.
As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients' response to novel therapeutic interventions.
Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data. They often proposed new features using conventional machine learning rather than deep learning algorithms. To better predict the outcome of patients with disorders of consciousness, recovery was most often based on the Glasgow Outcome Scale, and traditional machine learning techniques were used in most cases. Machine learning has also been employed to predict the effects of novel therapeutic interventions (e.g., zolpidem and transcranial direct current stimulation).
Artificial intelligence and machine learning can assist in clinical decision-making, including the diagnosis, prognosis, and therapy for patients with disorders of consciousness. The performance of these models can be expected to be significantly improved by the use of deep learning techniques.
目的综述：随着人工智能和机器学习技术的不断发展，它们越来越多地被用于提高对获得性脑损伤后严重意识障碍患者的科学认识和临床护理水平。我们在此综述了最近利用这些技术来降低意识障碍诊断和预后不确定性，并更好地描述患者对新型治疗干预措施反应的研究。
最新发现：大多数论文都集中在使用人工智能来更好地分析功能神经影像学和脑电图数据，从而区分无反应性觉醒综合征和最小意识状态。它们经常使用传统的机器学习算法而不是深度学习算法来提出新的特征。为了更好地预测意识障碍患者的预后，恢复通常基于格拉斯哥预后量表，并且在大多数情况下使用传统的机器学习技术。机器学习也被用于预测新型治疗干预措施（例如唑吡坦和经颅直流电刺激）的效果。
总结：人工智能和机器学习可以辅助临床决策，包括意识障碍患者的诊断、预后和治疗。通过使用深度学习技术，这些模型的性能有望得到显著提高。
