A role for artificial intelligence in molecular imaging of infection and inflammation.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Johannes Schwenck, Manfred Kneilling, Niels P Riksen, Christian la Fougère, Douwe J Mulder, Riemer J H A Slart, Erik H J G Aarntzen
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引用次数: 3

Abstract

The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers' expertise. Although molecular imaging, like [18F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.

Abstract Image

Abstract Image

Abstract Image

人工智能在感染和炎症分子成像中的作用。
隐匿性感染和低度炎症的检测在临床实践中仍然具有挑战性,很大程度上取决于读者的专业知识。虽然分子成像,如[18F]FDG PET或放射性标记的白细胞闪烁成像,提供了炎症反应的定量和可重复的全身数据,但其解释仅限于视觉分析。这往往导致诊断和治疗延迟,以及潜在应用的未开发领域。人工智能(AI)为挖掘丰富的成像数据提供了创新的方法,并已经在其他医疗领域带来了颠覆性的突破。在这里,我们讨论了基于人工智能的工具如何提高感染和炎症分子成像的检测灵敏度,以及人工智能如何将数据分析从当前应用推向预测结果和长期风险评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European Journal of Hybrid Imaging
European Journal of Hybrid Imaging Computer Science-Computer Science (miscellaneous)
CiteScore
3.40
自引率
0.00%
发文量
29
审稿时长
17 weeks
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