{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T05:59:12Z","timestamp":1672379952620},"reference-count":28,"publisher":"Georg Thieme Verlag KG","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Laryngorhinootologie"],"abstract":"<jats:title>Zusammenfassung<\/jats:title><jats:p>Weltweit steigt die Inzidenz des malignen Melanoms an. Bei fr\u00fchzeitiger Erkennung ist das Melanom gut behandelbar, eine Fr\u00fcherkennung ist also lebenswichtig.<\/jats:p><jats:p>Die Hautkrebs-Fr\u00fcherkennung hat sich in den letzten Jahrzehnten bspw. durch die Einf\u00fchrung des Screenings im Jahr 2008 und die Dermatoskopie deutlich verbessert. Dennoch bleibt die visuelle Erkennung insbesondere von fr\u00fchen Melanomen eine Herausforderung, weil diese viele morphologische \u00dcberlappungen mit N\u00e4vi zeigen. Daher ist der medizinische Bedarf weiterhin hoch, die Methoden zur Hautkrebsfr\u00fcherkennung gezielt weiterzuentwickeln, um Melanome bereits in einem sehr fr\u00fchen Stadium sicher diagnostizieren zu k\u00f6nnen.<\/jats:p><jats:p>Die Routinediagnostik zur Hautkrebs-Fr\u00fcherkennung umfasst die visuelle Ganzk\u00f6rperinspektion, oft erg\u00e4nzt durch die Dermatoskopie, durch die sich die diagnostische Treffsicherheit erfahrener Haut\u00e4rzte deutlich erh\u00f6hen l\u00e4sst. Ein Verfahren, was in einigen Praxen und Kliniken zus\u00e4tzlich angeboten wird, ist die kombinierte Ganzk\u00f6rperfotografie mit der digitalen Dermatoskopie f\u00fcr die Fr\u00fcherkennung maligner Melanome, insbesondere f\u00fcr das Monitoring von Hochrisiko-Patienten.<\/jats:p><jats:p>In den letzten Jahrzenten wurden zahlreiche nicht invasive zusatzdiagnostische Verfahren zur Beurteilung verd\u00e4chtiger Pigmentmale entwickelt, die das Potenzial haben k\u00f6nnten, eine verbesserte und z. T. automatisierte Bewertung dieser L\u00e4sionen zu erm\u00f6glichen. In erster Linie ist hier die konfokale Lasermikroskopie zu nennen, ebenso die elektrische Impedanzspektroskopie, die Multiphotonen-Lasertomografie, die Multispektralanalyse, die Raman-Spektroskopie oder die optische Koh\u00e4renztomografie. Diese diagnostischen Verfahren fokussieren i. d. R. auf hohe Sensitivit\u00e4t, um zu vermeiden, ein malignes Melanom zu \u00fcbersehen. Dies bedingt allerdings \u00fcblicherweise eine geringere Spezifit\u00e4t, was im Screening zu unn\u00f6tigen Exzisionen vieler gutartiger L\u00e4sionen f\u00fchren kann. Auch sind einige der Verfahren zeitaufwendig und kostenintensiv,was die Anwendbarkeit im Screening ebenfalls einschr\u00e4nkt.<\/jats:p><jats:p>In naher Zukunft wird insbesondere die Nutzung von k\u00fcnstlicher Intelligenz die Diagnosefindung in vielf\u00e4ltiger Weise ver\u00e4ndern. Vielversprechend ist v. a. die Analyse der makroskopischen und dermatoskopischen Routine-Bilder durch k\u00fcnstliche Intelligenz. F\u00fcr die Klassifizierung von pigmentierten Hautl\u00e4sionen anhand makroskopischer und dermatoskopischer Bilder erzielte die k\u00fcnstliche Intelligenz v. a. in Form neuronaler Netze unter experimentellen Bedingungen in zahlreichen Studien bereits eine vergleichbare diagnostische Genauigkeit wie Dermatologen. Insbesondere bei der bin\u00e4ren Klassifikationsaufgabe Melanom\/N\u00e4vus erreichte sie hohe Genauigkeiten, doch auch in der Multiklassen-Differenzierung von verschiedenen Hauterkrankungen zeigt sie sich vergleichbar gut wie Dermatologen. Der Nachweis der grunds\u00e4tzlichen Anwendbarkeit und des Nutzens solcher Systeme in der klinischen Praxis steht jedoch noch aus. Noch zu schaffende Grundvoraussetzungen f\u00fcr die Translation solcher Diagnosesysteme in die dermatologischen Routine sind M\u00f6glichkeiten f\u00fcr die Nutzer, die Entscheidungen des Systems nachzuvollziehen, sowie eine gleichbleibend gute Leistung der Algorithmen auf Bilddaten aus fremden Kliniken und Praxen.<\/jats:p><jats:p>Derzeit zeichnet sich ab, dass computergest\u00fctzte Diagnosesysteme als Assistenzsysteme den gr\u00f6\u00dften Nutzen bringen k\u00f6nnten, denn Studien deuten darauf hin, dass eine Kombination von Mensch und Maschine die besten Ergebnisse erzielt. Diagnosesysteme basierend auf k\u00fcnstlicher Intelligenz sind in der Lage, Merkmale schnell, quantitativ, objektiv und reproduzierbar zu erfassen, und k\u00f6nnten somit die Medizin auf eine mathematische Grundlage stellen \u2013 zus\u00e4tzlich zur \u00e4rztlichen Erfahrung.<\/jats:p>","DOI":"10.1055\/a-1949-3639","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T23:58:06Z","timestamp":1672358286000},"source":"Crossref","is-referenced-by-count":0,"title":["K\u00fcnstliche Intelligenz auf dem Vormarsch \u2013 Hohe Vorhersage-Genauigkeit bei der Fr\u00fcherkennung pigmentierter Melanome"],"prefix":"10.1055","author":[{"given":"Tanja","family":"Jutzi","sequence":"additional","affiliation":[{"name":"Arbeitsgruppe Digitale Biomarker f\u00fcr die Onkologie, Deutsches Krebsforschungszentrum, Heidelberg, Deutschland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eva I.","family":"Krieghoff-Henning","sequence":"additional","affiliation":[{"name":"Arbeitsgruppe Digitale Biomarker f\u00fcr die Onkologie, Deutsches Krebsforschungszentrum, Heidelberg, Deutschland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Titus J.","family":"Brinker","sequence":"additional","affiliation":[{"name":"Arbeitsgruppe Digitale Biomarker f\u00fcr die Onkologie, Deutsches Krebsforschungszentrum, Heidelberg, Deutschland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref2","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1111\/j.1365-2133.2008.08713.x","article-title":"Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting","volume":"159","author":"ME Vestergaard","year":"2008","journal-title":"Br J Dermatol"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1093\/annonc\/mdy166","article-title":"Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists","volume":"29","author":"HA Haenssle","year":"2018","journal-title":"Annals of Oncology"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"Cd011902","DOI":"10.1002\/14651858.CD011902.pub2","article-title":"Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults","volume":"12","author":"J Dinnes","year":"2018","journal-title":"Cochrane Database Syst Rev"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.3390\/ijerph18041726","article-title":"The Value of Total Body Photography for the Early Detection of Melanoma: A Systematic Review","volume":"18","author":"A Hornung","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1016\/j.jaad.2016.07.022","article-title":"Paired comparison of the sensitivity and specificity of multispectral digital skin lesion analysis and reflectance confocal microscopy in the detection of melanoma in vivo: A cross-sectional study","volume":"75","author":"E Song","year":"2016","journal-title":"J Am Acad Dermatol"},{"key":"ref7","volume-title":"S1-Leitlinie 013\u2013076: Konfokale Lasermikroskopie in der Dermatologie","author":"JMU Welzel","year":"2011"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.19553","article-title":"Applications and future directions for optical coherence tomography in dermatology","author":"B Wan","year":"2020","journal-title":"British Journal of Dermatology"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1039\/c3pp25344a","article-title":"Diagnosis of malignant melanoma and basal cell carcinoma by in vivo NIR-FT Raman spectroscopy is independent of skin pigmentation. Photochem Photobiol Sci Erratum in: Photochem Photobiol Sci. 2014 Sep; 13(9): 1360","volume":"12","author":"PA Philipsen","year":"2013"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.1109\/tmi.2019.2962614","article-title":"Optical Biopsy of Melanoma and Basal Cell Carcinoma Progression by Noncontact Photoacoustic and Optical Coherence Tomography: In Vivo Multi-Parametric Characterizing Tumor Microenvironment","volume":"39","author":"W Zhou","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1752","DOI":"10.1038\/jid.2008.439","article-title":"Sensitivity and specificity of multiphoton laser tomography for in vivo and ex vivo diagnosis of malignant melanoma","volume":"129","author":"E Dimitrow","year":"2009","journal-title":"J Invest Dermatol"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1111\/ddg.12399","article-title":"Melanoma early detection and automatic diagnosis of pigmented lesions","author":"W Stolz","year":"2014","journal-title":"J Dtsch Dermatol Ges"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1111\/srt.12008","article-title":"Electrical impedance spectroscopy as a potential adjunct diagnostic tool for cutaneous melanoma","volume":"19","author":"P Mohr","year":"2013","journal-title":"Skin Res Technol"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.compbiomed.2018.11.010","article-title":"Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation","volume":"104","author":"P Tschandl","year":"2019","journal-title":"Comput Biol Med"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1007\/s10916-018-1088-1","article-title":"Medical image analysis using convolutional neural networks: a review","volume":"42","author":"SM Anwar","year":"2018","journal-title":"Journal of Medical Systems"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"A Esteva","year":"2017","journal-title":"Nature"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1111\/bjd.16924","article-title":"Deep-learning-based, computeraided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis","volume":"180","author":"Y Fujisawa","year":"2019","journal-title":"Br J Dermatol"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.3390\/biom10081123","article-title":"The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning","volume":"10","author":"S Jinnai","year":"2020","journal-title":"Biomolecules"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"e1003381","DOI":"10.1371\/journal.pmed.1003381","article-title":"Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study","volume":"17","author":"SS Han","year":"2020","journal-title":"PLoS Med"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ejca.2019.04.001","article-title":"Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task","volume":"113","author":"TJ Brinker","year":"2019","journal-title":"European Journal of Cancer"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ejca.2019.05.023","article-title":"Deep neural networks are superior to dermatologists in melanoma image classification","volume":"119","author":"TJ Brinker","year":"2019","journal-title":"European Journal of Cancer"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.jaad.2017.08.016","article-title":"Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images","volume":"78","author":"MA Marchetti","year":"2018","journal-title":"J Am Acad Dermatol"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ejca.2019.06.013","article-title":"Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks","volume":"119","author":"RC Maron","year":"2019","journal-title":"European Journal of Cancer"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1111\/bjd.18875","article-title":"Attitudes towards artificial intelligence within dermatology: an international online survey","volume":"183","author":"S Polesie","year":"2020","journal-title":"Br J Dermatol"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1001\/jamadermatol.2019.5014","article-title":"Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening: A Qualitative Study","volume":"156","author":"CA Nelson","year":"2020","journal-title":"JAMA Dermatol"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"233","DOI":"10.3389\/fmed.2020.00233","article-title":"Artificial Intelligence in Skin Cancer Diagnostics: The Patients\u02bc Perspective","volume":"7","author":"TB Jutzi","year":"2020","journal-title":"Front Med (Lausanne)"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"e22909","DOI":"10.2196\/22909","article-title":"Digital Natives\u2019 Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Stud","volume":"9","author":"S Haggenm\u00fcller","year":"2021","journal-title":"JMIR mHealth and uHealth"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.ejca.2019.07.019","article-title":"Superior skin cancer classification by the combination of human and artificial intelligence","volume":"120","author":"A Hekler","year":"2019","journal-title":"Eur J Cancer"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1038\/s41591-020-0942-0","article-title":"Human-computer collaboration for skin cancer recognition","volume":"26","author":"P Tschandl","year":"2020","journal-title":"Nature Medicine"}],"container-title":["Laryngo-Rhino-Otologie"],"original-title":[],"language":"de","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-1949-3639.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T23:58:17Z","timestamp":1672358297000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-1949-3639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,29]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1055\/a-1949-3639","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["0935-8943","1438-8685"],"issn-type":[{"value":"0935-8943","type":"print"},{"value":"1438-8685","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,29]]}}}