Application and prospects of AI-based radiomics in ultrasound diagnosis

Abstract

Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.

Keywords: Artificial intelligence; B-mode ultrasound; Color Doppler flow imaging; Contrast-enhanced ultrasound; Deep learning; Multimodal ultrasound; Radiomics; Ultrasound elastography; Ultrasound imaging.

Fig. 1

Static ultrasound analysis model for diagnosis and prediction. Static ultrasound reflects the spatial characteristics of lesions. It involves two-dimensional imaging. The BUS images provide information on the anatomical structure, shape, texture, and position of lesions. Moreover, the CDFI images reveal the direction of blood flow in the lesions. Additionally, UE images reflect the tissue hardness of the lesions. The existing intelligent analysis methods for static ultrasound include SVM, lasso regression, CNNs, and transformers. These are commonly used for the diagnosis and prognosis of diseases such as breast cancer, ovarian cancer, lymph nodes, and liver fibrosis

Fig. 2

Dynamic ultrasound analysis model for diagnosis and prediction. Dynamic ultrasound reflects the spatiotemporal characteristics of lesions. It involves videos. Common ultrasound modalities include BUS, CDFI, UE, and CEUS. Current studies on ultrasound videos primarily focus on BUS and CEUS videos. The commonly used intelligent analysis methods for ultrasound videos include three-dimensional CNN, CNN + RNN, R(2 + 1)D, dual-stream CNN, and transformers. These are frequently employed for the diagnosis and prognosis of diseases such as breast cancer, liver cancer, thyroid nodules, kidney disease, and diabetes

Fig. 3

Research ideas for dual-/multi-modal ultrasound fusion analysis. The fusion analysis of different ultrasound modalities is of significant clinical importance. Each modality of ultrasound has its own advantages and limitations, and an efficient modality fusion method can complement each modality. This improves the accuracy of ultrasound AI diagnosis. Currently, there are various modality combinations such as BUS + CDFI, BUS + UE, BUS + CDFI + UE + CEUS, and US + CT/MRI. Common fusion methods include logistic regression, machine, and deep learning. These are frequently used to diagnose diseases such as breast cancer, liver cancer, thyroid nodules, and COVID-19