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

Titelangaben

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. Bd. 44 (2023) Heft 4 . - S. 395-407.
ISSN 1438-8782
DOI: https://doi.org/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.

Weitere Angaben

Publikationsform: Artikel in einer Zeitschrift
Begutachteter Beitrag: Ja
Keywords: focal liver lesions; artificial intelligence; systematic review; METHODS & TECHNIQUES; ultrasound; deep learning
Institutionen der Universität: Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Fakultäten
Fakultäten > Fakultät für Mathematik, Physik und Informatik
Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik
Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie
Titel an der UBT entstanden: Nein
Themengebiete aus DDC: 500 Naturwissenschaften und Mathematik > 530 Physik
Eingestellt am: 31 Mai 2023 12:13
Letzte Änderung: 13 Dec 2023 08:01
URI: https://eref.uni-bayreuth.de/id/eprint/81077