Artificial Intelligence - Based Ultrasound Elastography for Disease Evaluation - a Narrative Review

Overview

Journal Front Oncol

Specialty Oncology

Date 2023 Jun 19

PMID 37333814

Authors

Xian-Ya Zhang

Qi Wei

Ge-Ge Wu

Qi Tang

Xiao-Fang Pan

Gong-Quan Chen

Di Zhang

Christoph F Dietrich

Xin-Wu Cui

Affiliations

Soon will be listed here.

Abstract

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.

Citing Articles

Applications of Artificial Intelligence in Gastrointestinal Endoscopic Ultrasound: Current Developments, Limitations and Future Directions.

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[Imaging in chronic inflammatory bowel disease].

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Applications of artificial intelligence in musculoskeletal ultrasound: narrative review.

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References

Kohli M, Prevedello L, Filice R, Geis J . Implementing Machine Learning in Radiology Practice and Research. AJR Am J Roentgenol. 2017; 208(4):754-760. DOI: 10.2214/AJR.16.17224. View

Zhang Q, Xiao Y, Chen S, Wang C, Zheng H . Quantification of elastic heterogeneity using contourlet-based texture analysis in shear-wave elastography for breast tumor classification. Ultrasound Med Biol. 2014; 41(2):588-600. DOI: 10.1016/j.ultrasmedbio.2014.09.003. View

Ara S, Alam F, Rahman M, Akhter S, Awwal R, Hasan K . Bimodal Multiparameter-Based Approach for Benign-Malignant Classification of Breast Tumors. Ultrasound Med Biol. 2015; 41(7):2022-38. DOI: 10.1016/j.ultrasmedbio.2015.01.023. View

Stoean R, Stoean C, Lupsor M, Stefanescu H, Badea R . Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C. Artif Intell Med. 2010; 51(1):53-65. DOI: 10.1016/j.artmed.2010.06.002. View

Bravo A, Sheth S, Chopra S . Liver biopsy. N Engl J Med. 2001; 344(7):495-500. DOI: 10.1056/NEJM200102153440706. View

Giger M . Machine Learning in Medical Imaging. J Am Coll Radiol. 2018; 15(3 Pt B):512-520. DOI: 10.1016/j.jacr.2017.12.028. View

Li Y, Liu Y, Zhang M, Zhang G, Wang Z, Luo J . Radiomics With Attribute Bagging for Breast Tumor Classification Using Multimodal Ultrasound Images. J Ultrasound Med. 2019; 39(2):361-371. DOI: 10.1002/jum.15115. View

Yu Y, Xiao Y, Cheng J, Chiu B . Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images. Comput Biol Med. 2017; 93:31-46. DOI: 10.1016/j.compbiomed.2017.12.006. View

Gillies R, Schabath M . Radiomics Improves Cancer Screening and Early Detection. Cancer Epidemiol Biomarkers Prev. 2020; 29(12):2556-2567. DOI: 10.1158/1055-9965.EPI-20-0075. View

10.

Jiang M, Li C, Luo X, Chuan Z, Chen R, Tang S . Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer. Eur Radiol. 2021; 32(4):2313-2325. DOI: 10.1007/s00330-021-08330-w. View

11.

Qin P, Wu K, Hu Y, Zeng J, Chai X . Diagnosis of Benign and Malignant Thyroid Nodules Using Combined Conventional Ultrasound and Ultrasound Elasticity Imaging. IEEE J Biomed Health Inform. 2019; 24(4):1028-1036. DOI: 10.1109/JBHI.2019.2950994. View

12.

Khan A, Shah J, Bhat M . CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed. 2020; 196:105581. PMC: 7274128. DOI: 10.1016/j.cmpb.2020.105581. View

13.

Ahmed M, Purushotham A, Douek M . Novel techniques for sentinel lymph node biopsy in breast cancer: a systematic review. Lancet Oncol. 2014; 15(8):e351-62. DOI: 10.1016/S1470-2045(13)70590-4. View

14.

Zhang Q, Suo J, Chang W, Shi J, Chen M . Dual-modal computer-assisted evaluation of axillary lymph node metastasis in breast cancer patients on both real-time elastography and B-mode ultrasound. Eur J Radiol. 2017; 95:66-74. DOI: 10.1016/j.ejrad.2017.07.027. View

15.

Gao J, Jiang Q, Zhou B, Chen D . Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview. Math Biosci Eng. 2019; 16(6):6536-6561. DOI: 10.3934/mbe.2019326. View

16.

Guo Y, Duan X, Wang C, Guo H . Segmentation and recognition of breast ultrasound images based on an expanded U-Net. PLoS One. 2021; 16(6):e0253202. PMC: 8205136. DOI: 10.1371/journal.pone.0253202. View

17.

Chen L, Liang J, Wu H, Wang Z, Li S, Li W . Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics. Life Sci. 2018; 208:55-63. DOI: 10.1016/j.lfs.2018.07.007. View

18.

Liao W, He P, Hao J, Wang X, Yang R, An D . Automatic Identification of Breast Ultrasound Image Based on Supervised Block-Based Region Segmentation Algorithm and Features Combination Migration Deep Learning Model. IEEE J Biomed Health Inform. 2019; 24(4):984-993. DOI: 10.1109/JBHI.2019.2960821. View

19.

Zhou B, Bartholmai B, Kalra S, Zhang X . Predicting lung mass density of patients with interstitial lung disease and healthy subjects using deep neural network and lung ultrasound surface wave elastography. J Mech Behav Biomed Mater. 2020; 104:103682. PMC: 8845488. DOI: 10.1016/j.jmbbm.2020.103682. View

20.

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View