Details

Autor(en) / Beteiligte

• Huang, Xiaoqin
• Islam, Md Rafiqul
• Akter, Shanjita
• Ahmed, Fuad
• Kazami, Ehsan
• Serhan, Hashem Abu
• Abd-Alrazaq, Alaa
• Yousefi, Siamak

Titel

Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Ist Teil von

• Biomedical engineering online, 2023-12, Vol.22 (1), p.126-126, Article 126

Ort / Verlag

England: BioMed Central Ltd

Erscheinungsjahr

2023

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.