College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
Med Biol Eng Comput. 2024 Sep;62(9):2599-2627. doi: 10.1007/s11517-024-03097-w. Epub 2024 Apr 26.
In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023. To maintain rigor and credibility, stringent inclusion criteria, quality assessments, and precise data extraction protocols were consistently enforced throughout this review. Following a rigorous selection process, 104 studies were selected for review, focusing on diverse neuroimaging modalities with an emphasis on mental and neurological disorders. Among these, 19.2% addressed mental illness, and 80.7% focused on neurological disorders. It is found that the prevailing clinical tasks are disease classification (58.7%) and lesion segmentation (28.9%), whereas image reconstruction constituted 7.3%, and image regression and prediction tasks represented 9.6%. AI-driven neuroimaging analysis holds tremendous potential, transforming both research and clinical applications. Machine learning and deep learning algorithms outperform traditional methods, reshaping the field significantly.
在当代，人工智能（AI）经历了一场变革性的演变，对神经影像学数据分析产生了深远的影响。这一发展极大地提高了我们对复杂脑功能的理解。本研究探讨了在神经影像学数据中使用 AI 技术的影响，旨在提高诊断能力，并为该领域的整体进步做出贡献。我们在著名的科学数据库中进行了系统的搜索，包括 PubMed、IEEE Xplore 和 Scopus，精心整理了 2013 年至 2023 年期间关于 AI 驱动的神经影像学分析的 456 篇相关文章。为了保持严谨性和可信度，我们在整个综述过程中始终严格执行严格的纳入标准、质量评估和精确的数据提取协议。经过严格的筛选过程，我们选择了 104 项研究进行综述，重点关注各种神经影像学模式，特别是精神和神经疾病。其中，19.2%的研究针对精神疾病，80.7%的研究针对神经疾病。研究发现，目前主要的临床任务是疾病分类（58.7%）和病变分割（28.9%），而图像重建占 7.3%，图像回归和预测任务占 9.6%。AI 驱动的神经影像学分析具有巨大的潜力，改变了研究和临床应用。机器学习和深度学习算法优于传统方法，极大地改变了这一领域。
