Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

PURPOSE: To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomograph...

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Detalles Bibliográficos
Autores principales: Shi, Ce, Wang, Mengyi, Zhu, Tiantian, Zhang, Ying, Ye, Yufeng, Jiang, Jun, Chen, Sisi, Lu, Fan, Shen, Meixiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507244/
https://www.ncbi.nlm.nih.gov/pubmed/32974414
http://dx.doi.org/10.1186/s40662-020-00213-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507244/
https://www.ncbi.nlm.nih.gov/pubmed/32974414
http://dx.doi.org/10.1186/s40662-020-00213-3

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