期刊
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
卷 87, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2020.101818
关键词
Artificial intelligence; Glaucoma diagnosis; Machine learning; Support vector machine; Deep learning; Convolutional neural networks; Software as a medical device (SaMD)
Glaucoma is an eye disease characterized by increased intraocular pressure, optic neuropathy, and visual field defects. Early and accurate diagnosis is crucial to prevent irreversible vision loss. Current conventional diagnostic methods are prone to misdiagnosis, leading to the exploration of artificial intelligence as a more advanced approach.
Glaucoma, the group of eye diseases is characterized by increased intraocular pressure, optic neuropathy and visual field defect patterns. Early and correct diagnosis of glaucoma can prevent irreversible vision loss and glaucomatous structural damages to the eye. However, greater chances of misdiagnosis by the currently used conventional methods for diagnosis open up ways for more advanced techniques like the use of artificial intelligence (AI). Artificial intelligence coupled with optical coherence tomography imaging creates an algorithm that can be effectively used to make a model of complex data for detection as well as diagnosis of glaucoma. The present review is an attempt to provide state-of-the-art information on various AI techniques used in the diagnosis and assessment of glaucoma. The second part of the review is focused on understanding how the AI along with machine learning (ML) can be potentially used to be subjected for software as a medical device (SaMD) in precise diagnosis or early detection of disease conditions.
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