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Bisher liefert das Corvis\u00aeST (Corneal Visualization Scheimpflug Technology, Oculus, Wetzlar, Deutschland) einen Index-CBI, der quasi bin\u00e4r Keratokonus klassifiziert, aber kein Staging zul\u00e4sst. Ziel der Studie ist es, anhand von Messgr\u00f6\u00dfen des Corvis\u00aeST ein Vorhersagemodell zu entwerfen, das den Topographic Keratoconus Classification Index (TKC) der Pentacam high resolution (HR, Oculus) nachbildet.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Patienten und Methoden<\/jats:title>\n                <jats:p>Es wurden 60\u00a0Messungen an Normalprobanden (TKC\u00a00) und 379\u00a0Augen mit Keratokonus (TKC\u00a01 bis 4) in die Studie mit einbezogen. Nach der Messung mit der Pentacam HR (Zielgr\u00f6\u00dfe TKC) wurde eine Untersuchung mit dem Corvis\u00aeST durchgef\u00fchrt, aus der 6\u00a0Messparameter extrahiert wurden, die in den Corvis Biomechanical Index CBI eingehen (ARTh, SP-A1, DA-Ratio 1\u202fmm, DA-Ratio 2\u202fmm, A1 velocity, max. Deformation Amplitude). Neben dem TKC als Zielgr\u00f6\u00dfe wurde der binarisierte TKC (1: TKC\u00a01 bis 4, 0: TKC\u00a00) modelliert. Als G\u00fctema\u00df wurde die Genauigkeit des Modells als Anteil der korrekten Klassifizierungen herangezogen. Fehlklassifizierungen wurden in der Modellierung so bestraft, dass die Abweichung des modellierten TKC-Wertes vom gemessenen Wert bewertet wurde.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Ergebnisse<\/jats:title>\n                <jats:p>Es wurden 24 verschiedene Modelle des \u00fcberwachten maschinellen Lernens aus 6\u00a0Familien getestet. F\u00fcr die Modellierung des TKC in Stufen von 0\u20134 zeigte das Modell, basierend auf einer Support Vector Machine (SVM) mit linearem Kernel, die beste Performance mit einem Anteil an richtigen Klassifizierungen von 65,1\u202f%. F\u00fcr den binarisierten Wert des TKC zeigte ein Decision Tree mit grober Aufl\u00f6sung die beste Performance mit einem Anteil an richtigen Klassifizierungen von 95,2\u202f%, direkt gefolgt von der SVM mit linearem oder quadratischem Kernel und dem Nearest Neighborhood Classifier mit kubischem Kernel (jeweils 94,5\u202f%).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Schlussfolgerungen<\/jats:title>\n                <jats:p>In der Arbeit soll das Prinzip des \u00fcberwachten Maschinenlernens in der Anwendung auf die modellierte Klassifizierung von Messbefunden gezeigt werden. So wurden Messdaten des Corvis\u00aeST dazu verwendet, die Einteilung in den Schweregrad eines Keratokonus mittels Pentacam (TKC) mit einer ganzen Reihe von Algorithmen des maschinellen Lernens nachzubilden.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00347-020-01231-1","type":"journal-article","created":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T10:02:51Z","timestamp":1600941771000},"page":"697-706","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Keratokonusdetektion und Ableitung des Auspr\u00e4gungsgrades aus den Parametern des Corvis\u00aeST","Keratoconus detection and classification from parameters of the Corvis\u00aeST"],"prefix":"10.1007","volume":"118","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9175-6177","authenticated-orcid":false,"given":"Achim","family":"Langenbucher","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Larissa","family":"H\u00e4fner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timo","family":"Eppig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Berthold","family":"Seitz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N\u00f3ra","family":"Szentm\u00e1ry","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elias","family":"Flockerzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,24]]},"reference":[{"issue":"4","key":"1231_CR1","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1089\/big.2018.0175","volume":"7","author":"AHA Alfeilat","year":"2019","unstructured":"Alfeilat\u00a0AHA, Hassanat\u00a0ABA, Lasassmeh\u00a0O, Tarawneh\u00a0AS, Alhasanat\u00a0MB, Salman\u00a0HSE, Prasath\u00a0VBS (2019) Effects of distance measure choice on K\u2011nearest neighbour classifier performance: A review. 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