Use of machine learning to predict the risk of early morning intraocular pressure peaks in glaucoma patients and suspects

Brandão-de-Resende, Camilo; Cronemberger, Sebastião; Veloso, Artur William Caldeira Abreu; Mérula, Rafael Vidal; Freitas, Carolina Saliba de; Borges, Erica A; Diniz-Filho, Alberto

doi:10.5935/0004-2749.20210101

Cited by 2 publications

References 25 publications

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

Huang,

Islam,

Akter

et al. 2023

BioMed Eng OnLine

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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.

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

Huang,

Islam,

Akter

et al. 2023

BioMed Eng OnLine

View full text Add to dashboard Cite

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Predicting 24-hour Intraocular Pressure Fluctuations using Machine Learning: A Retrospective Analysis

Chen,

Lei,

Liao

et al. 2023

Preprint

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24-hour intraocular pressure fluctuation is at a risk of glaucomatous progression. We thus aimed to develop a machine learning-based predictive model for 24-hour intraocular pressure (IOP) fluctuation. It was a retrospective analysis of electronic medical records from 978 eyes, focusing on 24-hour IOP monitoring data collected at Shanghai Fifth People's Hospital between January 2014 and July 2023. The dataset included various parameters such as blood pressure and corneal thickness. We divided the data into training (80%) and validation (20%) sets. Employing algorithms such as Support Vector Machine and Random Forest, our model predicted whether fluctuations in 24-hour intraocular pressure exceeded 8 mmHg. The Random Forest algorithm demonstrated the best performance in validation. The model achieved accuracy, specificity, precision, sensitivity, f1-score, and AUC area of 0.899, 0.965, 0.884, 0.718, 0.793, and 0.89. We eventually established a predictive model, demonstrating the potential to rectify intraocular pressure measurements using the Random Forest algorithm.

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Use of machine learning to predict the risk of early morning intraocular pressure peaks in glaucoma patients and suspects

Cited by 2 publications

References 25 publications

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

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

Predicting 24-hour Intraocular Pressure Fluctuations using Machine Learning: A Retrospective Analysis

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