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

Title data

Vetter, Marcel ; Waldner, Maximilian J. ; Zundler, Sebastian ; Klett, Daniel ; Bocklitz, Thomas ; Neurath, Markus F. ; Adler, Werner ; Jesper, Daniel:
Artificial intelligence for the classification of focal liver lesions in ultrasound : a systematic review.
In: Ultraschall in der Medizin = European Journal of Ultrasound. Vol. 44 (2023) Issue 4 . - pp. 395-407.
ISSN 1438-8782
DOI: https://doi.org/10.1055/a-2066-9372

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: focal liver lesions; artificial intelligence; systematic review; METHODS & TECHNIQUES; ultrasound; deep learning
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie
Result of work at the UBT: No
DDC Subjects: 500 Science > 530 Physics
Date Deposited: 31 May 2023 12:13
Last Modified: 13 Dec 2023 08:01
URI: https://eref.uni-bayreuth.de/id/eprint/81077