Deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy: A review

Relevance: Glaucoma is a group of diseases characterized by progressive, bilateral yet asymmetric optic neuropathy, which results in permanent vision loss when is not treated promptly; It is asymptomatic in the early stages; thus, unfortunately, the diagnosis is discovered when the compromise is already severe, and the condition is advanced. Because of this, it is crucial to conduct early screening using technologies that are accessible to the population. Artificial intelligence (AI), particularly deep learning (DL), plays an essential role in this issue. DL may be an efficient approach for glaucoma screenings with the proper training. Objective: Describe the development of AI and DL over time and their use and significance in glaucoma screening. Methods: A literature search was conducted in PUBMED/MEDLINE, EMBASE, and manuscript references in English and Spanish between January 2014 to July 2022 on the role and evolution of AI and DL over the years and the usefulness of deep learning for glaucoma diagnosis. Of the 1914 abstracts reviewed, 105 articles were selected that contained information on the history of AI in medicine and the applicability of this tool for the early diagnosis of glaucoma. Findings and conclusions: We can demonstrate that deep learning can outperform glaucoma specialists in diagnosing the condition through fundus imaging data; DL is an exciting tool in the screening and early diagnosis of glaucoma.