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Endo-Praxis 2020; 36(01): 30-43
DOI: 10.1055/a-0983-8972
Originalarbeit
© Georg Thieme Verlag KG Stuttgart · New York

Künstliche Intelligenz in der Endoskopie: Neuronale Netze und maschinelles Sehen – Techniken und Perspektiven

Rüdiger Schmitz
1   Department for Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf
2   Institute of Anatomy and Experimental Morphology, University Hospital Hamburg-Eppendorf
3   DAISYlab, Forschungszentrum Medizintechnik Hamburg, Hamburg, Germany
,
René Werner
3   DAISYlab, Forschungszentrum Medizintechnik Hamburg, Hamburg, Germany
4   Institute of Computational Neuroscience, University Hospital Hamburg-Eppendorf
,
Thomas Rösch
1   Department for Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf
› Institutsangaben
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Publikationsverlauf

Publikationsdatum:
11. Februar 2020 (online)

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Zusammenfassung

Künstliche neuronale Netze als Methoden der künstlichen Intelligenz (KI) können der Endoskopie neue Möglichkeiten eröffnen, etwa im Sinne einer automatischen Polypenerkennung oder der präzisen Vorhersage des histopathologischen Befunds einer Läsion anhand ihres endoskopischen Bildes. Während erste Versuche tatsächlich ein weitreichendes Potenzial erahnen lassen, leiten sich öffentliche und medial transportierte Erwartungen häufig mehr von einer abstrakten Faszination als von der detaillierten Funktionsweise der Methoden ab. Dieser Artikel soll anhand einer selektiven Literaturübersicht ein intuitives Verständnis der Methoden vermitteln und helfen, die Lücke zwischen Funktion und Faszination zu schließen, um Potenzial und Grenzen dieser Techniken im Bereich der Endoskopie realistisch abschätzen zu können.

Mit ihrem Erfolg bei der maschinellen Klassifikation von Bildern haben insbesondere „tiefe neuronale Netze“ der KI nach jahrzehntelanger Forschung zu rasant anwachsendem Interesse verholfen. Wir umreißen kurz die diesbezüglichen Entwicklungen und die Gründe für ihre Bedeutung weit über die Informatik hinaus. Durch den Vergleich von maschinellem und menschlichem Sehen wird ein Verständnis der detaillierten Funktionsweise dieser Methoden und ihrer Erfolge bei Seh-Aufgaben vermittelt. Darauf aufbauend analysieren wir die Funktionsweise jüngst demonstrierter Anwendungen in Hinblick auf methodische Perspektiven und Grenzen, die Aussagekraft bisher erbrachter Leistungsnachweise und die Notwendigkeit weiterer Tests. Zudem geben wir einen Eindruck von weiteren, konkret absehbaren Einzelanwendungen und besprechen, welchen Charakter diese dem Einsatz der künstlichen Intelligenz in der Endoskopie insgesamt geben könnten.

Schlüsselwörter

Endoskopie - künstliche Intelligenz - deep learning - tiefe neuronale Netze - computer-aided detection - computer-aided diagnosis - maschinelles Sehen
 

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