人工智能在临床前成像中有作用吗?

IF 4.6 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Alina Küper MD , Paul Blanc-Durand MD , Andrei Gafita MD , David Kersting MD, MSc , Wolfgang P. Fendler MD , Constantin Seibold MSc , Alexandros Moraitis MSc , Katharina Lückerath PhD , Michelle L. James PhD , Robert Seifert MD
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引用次数: 1

摘要

这篇综述概述了目前将人工智能方法整合到核医学临床前成像研究领域的机会。分子成像和治疗学不断发展的多种能力可以很好地满足对适应特定肿瘤表型的成像剂和治疗剂日益增长的需求。然而,对快速开发在诊断成像中表现出色并获得显著治疗效果的具有最小副作用的新型特异性放射性配体的需求不断增加,这需要一个具有挑战性的临床前管道:从化学、物理和生物开发的靶点识别到临床试验的进行,再加上剂量测定和各种前期、中期、,以及治疗后分期图像,以创建用于评估诊断或治疗配体的功效的翻译反馈回路。在这条管道的几乎所有领域,人工智能的使用,特别是神经网络等深度学习系统的使用,不仅可以解决上述挑战,还可以提供如果不使用人工智能就不可能实现的见解。未来,我们预计不仅核医学的临床方面将得到人工智能的支持,而且还将普遍转向人工智能辅助的计算机研究,以解决识别癌症患者靶点和开发放射性配体的日益复杂的性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Is There a Role of Artificial Intelligence in Preclinical Imaging?

This review provides an overview of the current opportunities for integrating artificial intelligence methods into the field of preclinical imaging research in nuclear medicine. The growing demand for imaging agents and therapeutics that are adapted to specific tumor phenotypes can be excellently served by the evolving multiple capabilities of molecular imaging and theranostics. However, the increasing demand for rapid development of novel, specific radioligands with minimal side effects that excel in diagnostic imaging and achieve significant therapeutic effects requires a challenging preclinical pipeline: from target identification through chemical, physical, and biological development to the conduct of clinical trials, coupled with dosimetry and various pre, interim, and post-treatment staging images to create a translational feedback loop for evaluating the efficacy of diagnostic or therapeutic ligands. In virtually all areas of this pipeline, the use of artificial intelligence and in particular deep-learning systems such as neural networks could not only address the above-mentioned challenges, but also provide insights that would not have been possible without their use. In the future, we expect that not only the clinical aspects of nuclear medicine will be supported by artificial intelligence, but that there will also be a general shift toward artificial intelligence-assisted in silico research that will address the increasingly complex nature of identifying targets for cancer patients and developing radioligands.

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来源期刊
Seminars in nuclear medicine
Seminars in nuclear medicine 医学-核医学
CiteScore
9.80
自引率
6.10%
发文量
86
审稿时长
14 days
期刊介绍: Seminars in Nuclear Medicine is the leading review journal in nuclear medicine. Each issue brings you expert reviews and commentary on a single topic as selected by the Editors. The journal contains extensive coverage of the field of nuclear medicine, including PET, SPECT, and other molecular imaging studies, and related imaging studies. Full-color illustrations are used throughout to highlight important findings. Seminars is included in PubMed/Medline, Thomson/ISI, and other major scientific indexes.
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