Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

Abstract

Background and aim: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: artificial intelligence; deep learning; in vivo animal model; preclinical model; review; ultrasound imaging.

Figure 1

Flow diagram of systematic identification, screening, and inclusion of articles on the use of DL techniques applied to US imaging in preclinical in vivo models. DL: Deep learning; US: Ultrasound.

Figure 2

Temporal distribution of the 36 original articles and the 20 conference proceedings based on the year of publication.

Figure 3

Pie chart representing the partition of publications according to the number of publications, and their relative percentage in parenthesis, based on the anatomical district in which DL models were used on US imaging analysis.