Your browser doesn't support javascript.
loading
Predicting Success in Descemet Membrane Endothelial Keratoplasty Surgery Using Machine Learning.
Karaca, Emine Esra; Bulut Ustael, Ayça; Keçeli, Ali Seydi; Kaya, Aydin; Uçan, Alaettin; Evren Kemer, Ozlem.
Afiliación
  • Karaca EE; Department of Ophthalmology, Ankara Bilkent City Hospital, University of Health Sciences, Ankara.
  • Bulut Ustael A; Department of Ophthalmology, Ankara Bilkent City Hospital, University of Health Sciences, Ankara.
  • Keçeli AS; Department of Computer Engineering, Hacettepe University, Ankara; and.
  • Kaya A; Department of Computer Engineering, Hacettepe University, Ankara; and.
  • Uçan A; Department of Research and Development, Tiga Health Informatics, Ankara.
  • Evren Kemer O; Department of Ophthalmology, Ankara Bilkent City Hospital, University of Health Sciences, Ankara.
Cornea ; 2024 Jun 20.
Article en En | MEDLINE | ID: mdl-38913970
ABSTRACT

PURPOSE:

This study aimed to predict early graft failure (GF) in patients who underwent Descemet membrane endothelial keratoplasty based on donor characteristics.

METHODS:

Several machine learning methods were trained to predict GF automatically. To predict GF, the following variables were obtained donor age, sex, systemic diseases, medications, duration of stay in the intensive care unit, death-to-preservation time (DPT), endothelial cell density of the cornea, tightness of Descemet membrane roll during surgery, anterior chamber tamponade, tamponade used for rebubbling, and preoperative best corrected visual acuity. Five classification methods were experimented with the study data set random forest, support vector machine, k-nearest neighbor, RUSBoosted tree, and neural networks. In holdout validation, 75% of the data were used in training and the remaining 25% used in testing. The predictive accuracy, sensitivity, specificity, f-score, and area under the receiver operating characteristic curve of the methods were evaluated.

RESULTS:

The highest classification accuracy achieved during the experiments was 96%. The precision, recall, and f1-score values were 0.95, 0.81, and 0.90, respectively. Feature importance was also computed using analysis of variance. The model revealed that GF risk was related to DPT and the intensive care unit duration (P < 0.05). No significant relationship was found between donor age, endothelial cell density, systemic diseases and medications, graft roll, tamponades, and GF risk.

CONCLUSIONS:

This study shows a strong relationship between increased intensive care duration, DPT, and GF. Experimental results demonstrate that machine learning methods may effectively predict GF automatically.
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cornea Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cornea Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos