De Rosa Laura, L'Abbate Serena, Kusmic Claudia, Faita Francesco
Institute of Clinical Physiology, National Research Council (CNR), 56124 Pisa, Italy.
Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy.
Life (Basel). 2023 Aug 16;13(8):1759. doi: 10.3390/life13081759.
Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models.
A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines.
Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging.
DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.
在使用动物模型的临床前研究中，超声（US）成像比其他侵入性更强的方式越来越受到青睐。然而，这项技术存在一些局限性，主要与操作者的依赖性有关。为了克服当前的一些缺点，人们提出了复杂的数据处理模型，特别是基于深度学习（DL）网络的人工智能模型。本系统综述旨在概述DL算法在辅助US分析动物模型体内临床前研究中获取的图像方面的应用。
使用Scopus和PubMed数据库进行文献检索。2012年1月至2022年11月发表的、在临床前/动物实验场景中获取的US图像上开发DL模型的研究符合纳入标准。本综述按照PRISMA指南进行。
共纳入56项研究，并根据使用DL模型的解剖区域分为五组。16项研究聚焦于心血管系统，14项聚焦于腹部器官。5项研究将DL网络应用于肌肉骨骼系统图像，8项研究涉及大脑。13篇论文归为杂项类别，提出了采用DL系统的异类应用。我们的分析还强调，小鼠模型是在将DL应用于US成像的体内研究中最常用的动物。
DL技术在使用动物模型的临床前研究中获取的US图像方面显示出巨大潜力。然而，在这种情况下，这些技术仍处于早期阶段，在样本量、数据预处理和模型可解释性等方面仍有改进空间。
