Senders Joeky T, Zaki Mark M, Karhade Aditya V, Chang Bliss, Gormley William B, Broekman Marike L, Smith Timothy R, Arnaout Omar
Department of Neurosurgery, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
Acta Neurochir (Wien). 2018 Jan;160(1):29-38. doi: 10.1007/s00701-017-3385-8. Epub 2017 Nov 13.
Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML's potential to assist and improve neurosurgical care.
A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment.
Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction.
ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.
机器学习（ML）是人工智能的一个分支，它使计算机能够从大型复杂数据集中学习，而无需进行明确编程。尽管机器学习已经以各种形式广泛出现在我们的日常生活中，但其巨大潜力尚未进入主流医学研究和日常临床护理领域。神经外科中使用的复杂诊断和治疗方式提供了大量非常适合机器学习模型的数据。本系统评价探讨了机器学习在辅助和改善神经外科护理方面的潜力。
在PubMed和Embase数据库中进行系统文献检索，以识别截至2017年1月1日的所有潜在相关研究。纳入所有评估机器学习模型辅助神经外科治疗的研究。
在识别出的6402条引文中，经过后续标题/摘要和全文筛选后，选择了221项研究。在这些研究中，机器学习被用于辅助癫痫、脑肿瘤、脊柱病变、神经血管疾病、帕金森病、创伤性脑损伤和脑积水患者的手术治疗。在多个范例中，机器学习被发现是术前规划、术中指导、神经生理监测和神经外科手术结果预测的宝贵工具。
机器学习已开始通过提高围手术期决策的效率和精度来寻找旨在改善神经外科护理的应用。在临床神经外科护理中实施之前，对特定机器学习模型进行全面验证至关重要。为了弥合研究与临床护理之间的差距，在开发这些技术的同时应考虑实际和伦理问题。
