Sotoudeh Houman, Shafaat Omid, Bernstock Joshua D, Brooks Michael David, Elsayed Galal A, Chen Jason A, Szerip Paul, Chagoya Gustavo, Gessler Florian, Sotoudeh Ehsan, Shafaat Amir, Friedman Gregory K
Department of Neuroradiology, University of Alabama, Birmingham, AL, United States.
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Front Oncol. 2019 Aug 14;9:768. doi: 10.3389/fonc.2019.00768. eCollection 2019.
Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy. Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma. Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis. Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches.
人工智能(AI)已加速了包括医学在内的多个学科的新发现。临床医学缺乏基于AI的应用,这可能是由于对AI方法缺乏认识。计算机科学家和临床医生之间未来的合作对于在该领域为患者最大限度地发挥变革性技术的益处至关重要。为了说明这一点,我们描述了基于AI在神经胶质瘤(最常见的原发性中枢神经系统(CNS)恶性肿瘤)诊断和管理方面的进展。本文针对没有计算机科学背景的临床医生,简要介绍了AI方法的基础概念及其与临床医学的相关性。我们还回顾了神经胶质瘤诊断和管理中的新型AI方法。已经开发出神经胶质瘤的新型AI方法,用于从影像学预测分级和基因组学,从组织病理学实现诊断自动化,并提供预后见解。新型AI方法在神经胶质瘤中表现出可接受的性能。有必要进行进一步研究以改进方法并确定这些新方法的全部临床效用。