Künstliche Intelligenz zur Entwicklung von Screening-Parametern im Bereich der kornealen Biomechanik

Zusammenfassung

Machine Learning stellt insbesondere dann eine sinnvolle Alternative dar, wenn eine Datenanalyse mit wissensbasierten analytischen Methoden sehr aufwendig und schwierig ist. In solchen Fällen bietet sich auch eine Kombination aus analytischen Methoden und empirischen Methoden mittels künstlicher Intelligenz (KI) an. Die Entwicklung verschiedener Auswertefunktionen des Corvis ST ist hierfür ein konkretes Beispiel. In diesem Beitrag wird die Entwicklung dreier Screening-Parameter mittels KI beschrieben. Der Artikel zeigt, wie diese Entwicklungen im Bereich der Erkennung von klinischem und subklinischem Keratokonus sowie Glaukom-Screening klinisch hilfreich sind.

Abstract

Machine learning and artificial intelligence are mostly important if data analysis by knowledge-based analytical methods is difficult and complex. In such cases, combined analytical and empirical approaches based on AI are also meaningful. The development and validation of several clinical parameters for the Corvis ST are a concrete example of this approach. In this article, the development of three screening parameters is described. It is shown how these developments lead to clinical solutions that can be beneficial for detecting clinical and subclinical keratoconus as well as for glaucoma screening.

Schlüsselwörter

Glaukom - refraktive Chirurgie - Kornea

Key words

cornea - glaucoma - refractive surgery

• Literatur

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