Application of a Deep Learning System in Pterygium Grading and Further Prediction of Recurrence with Slit Lamp Photographs

Background: The aim of this study was to evaluate the efficacy of a deep learning system in pterygium grading and recurrence prediction. Methods: This was a single center, retrospective study. Slit-lamp photographs, from patients with or without pterygium, were collected to develop an algorithm. Dem...

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Detalles Bibliográficos
Autores principales: Hung, Kuo-Hsuan, Lin, Chihung, Roan, Jinsheng, Kuo, Chang-Fu, Hsiao, Ching-Hsi, Tan, Hsin-Yuan, Chen, Hung-Chi, Ma, David Hui-Kang, Yeh, Lung-Kun, Lee, Oscar Kuang-Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029774/
https://www.ncbi.nlm.nih.gov/pubmed/35453936
http://dx.doi.org/10.3390/diagnostics12040888

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029774/
https://www.ncbi.nlm.nih.gov/pubmed/35453936
http://dx.doi.org/10.3390/diagnostics12040888

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