Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

摘要 近年来，放射学与诊断影像学学科发展迅猛。我们观察到，影像检查量呈指数级增长、医学领域亚专科化程度不断加深，同时各类影像检查方法的准确率亦持续提升，这使得放射科医师难以做到“通晓所有检查与影像解剖区域的全部知识”。此外，影像检查已不再仅局限于定性诊断，如今还可提供疾病严重程度的量化信息，并能识别预后与治疗反应相关的生物标志物。有鉴于此，计算机辅助诊断（computer-aided diagnosis, CAD）系统应运而生，旨在辅助诊断影像学工作，并助力治疗决策流程。随着人工智能（artificial intelligence, AI）、“大数据”与机器学习（machine learning）技术的出现，这些工具在医师日常工作中的应用正快速普及，这不仅让每位患者的诊疗更具个体化特征，也推动放射学朝着多学科协作与精准医学的理念发展。本文将介绍当前可用的影像分析计算工具的主要特点、此类分析的核心原理，以及涉及的主要术语与概念，并探讨人工智能的发展对放射学与诊断影像学产生的影响。