{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T19:57:14Z","timestamp":1792180634820,"version":"4.4.0"},"reference-count":64,"publisher":"Georg Thieme Verlag KG","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Ultraschall Med"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The\n      diagnosis of frequent benign lesions can be determined reliably based on the characteristic\n      B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal\n      liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS)\n      increases diagnostic accuracy for the distinction between benign and malignant liver lesions.\n      Artificial intelligence describes applications that try to emulate human intelligence, at\n      least in subfields such as the classification of images. Since ultrasound is considered to be\n      a particularly examiner-dependent technique, the application of artificial intelligence could\n      be an interesting approach for an objective and accurate diagnosis. In this systematic review\n      we analyzed how artificial intelligence can be used to classify the benign or malignant nature\n      and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search\n      on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion\n      criteria. Studies showed good diagnostic performance for both the classification as benign or\n      malignant and the differentiation of individual tumor entities. The results could be improved\n      by inclusion of clinical parameters and were comparable to those of experienced investigators\n      in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in\n      the studies and a lack of independent validation cohorts, the transfer of the results into\n      clinical practice is limited.<\/jats:p>","DOI":"10.1055\/a-2066-9372","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T19:17:11Z","timestamp":1680290231000},"page":"395-407","source":"Crossref","is-referenced-by-count":12,"title":["Artificial intelligence for the classification of focal liver lesions in ultrasound \u2013 a systematic review"],"prefix":"10.1055","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9619-6647","authenticated-orcid":false,"given":"Marcel","family":"Vetter","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian J","family":"Waldner","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Zundler","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Klett","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Bocklitz","sequence":"additional","affiliation":[{"name":"Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-Universitat Jena, Jena, Germany"},{"name":"Leibniz-Institute of Photonic Technology, Friedrich Schiller University Jena, Jena, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus F","family":"Neurath","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Werner","family":"Adler","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Jesper","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology 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