Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience

We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506 eyes who underwent pars plana vitrectomy for rheg...

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Detalles Bibliográficos
Autores principales: Antaki, Fares, Kahwati, Ghofril, Sebag, Julia, Coussa, Razek Georges, Fanous, Anthony, Duval, Renaud, Sebag, Mikael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658348/
https://www.ncbi.nlm.nih.gov/pubmed/33177614
http://dx.doi.org/10.1038/s41598-020-76665-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658348/
https://www.ncbi.nlm.nih.gov/pubmed/33177614
http://dx.doi.org/10.1038/s41598-020-76665-3

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