Artificial intelligence for the classification of focal liver lesions in ultrasound - a systematic review.

Vetter M, Waldner M, Zundler S, Klett D, Bocklitz T, Neurath M, Adler W, Jesper D (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Ultraschall in der Medizin Georg Thieme Verlag

Book Volume: 44

Pages Range: 395-407

Journal Issue: 4

DOI: 10.1055/a-2066-9372

Abstract

Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction between benign and malignant liver lesions. Artificial intelligence describes applications that try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the benign or malignant nature and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion criteria. Studies showed good diagnostic performance for both the classification as benign or malignant and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited.

Authors with CRIS profile

Marcel Vetter Department of Medicine 1 – Gastroenterology, Pneumology and Endocrinology Maximilian Waldner Lehrstuhl für Innere Medizin I Sebastian Zundler Department of Medicine 1 – Gastroenterology, Pneumology and Endocrinology Daniel Klett Department of Medicine 1 – Gastroenterology, Pneumology and Endocrinology Markus Neurath Lehrstuhl für Innere Medizin I Werner Adler Lehrstuhl für Biometrie und Epidemiologie

Involved external institutions

Friedrich-Schiller-Universität Jena DE Germany (DE)

How to cite

APA:

Vetter, M., Waldner, M., Zundler, S., Klett, D., Bocklitz, T., Neurath, M.,... Jesper, D. (2023). Artificial intelligence for the classification of focal liver lesions in ultrasound - a systematic review. Ultraschall in der Medizin, 44(4), 395-407. https://doi.org/10.1055/a-2066-9372

MLA:

Vetter, Marcel, et al. "Artificial intelligence for the classification of focal liver lesions in ultrasound - a systematic review." Ultraschall in der Medizin 44.4 (2023): 395-407.

BibTeX: Download