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 3 publications

(3 citation statements)

References 25 publications

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“…The authors found three groups related to the forecast risk of the IOP peak at 6:00 a.m.: low risk for eyes with IOP <19mmHg at 8:00 a.m. and curve concavity < 2.3; intermediate risk for eyes with IOP <19mmHg at 8:00 a.m. and curve concavity ≥2.3 and the best predictors of IOP 6:00 a.m. >21mmHg were IOP 8:00 a.m. ≥19mmHg and DCPo concavity ≥2.3. (29) The proposed model achieved a sensitivity of 100% and a specificity of 86%, resulting in an accuracy of 93%. This new approach to the DCPo may become a widely used tool in daily practice and the indication of a 24-hour monitoring IOP could be rationalized according to risk stratification.…”

Section: Adequate Intraocular Pressure Controlmentioning

confidence: 93%

“…(28) A work carried out at São Geraldo Hospital evaluated the maximum IOP forecast at 6:00 a.m. in patients who performed 24-hour IOP monitoring. (29) The authors used machine learning to predict the IOP spikes at 6:00 a.m. in patients suspected and having POAG. They included 98 eyes of 98 patients who underwent 24-hour DCPo (including IOP measurements at 6:00 a.m. in bed and in darkness).…”

Section: Adequate Intraocular Pressure Controlmentioning

confidence: 99%

“…This new approach to the DCPo may become a widely used tool in daily practice and the indication of a 24-hour monitoring IOP could be rationalized according to risk stratification. (29)…”

Section: Adequate Intraocular Pressure Controlmentioning

confidence: 99%

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24-hour intraocular pressure monitoring: past, present, and future

Cronemberger

2024

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Section: Adequate Intraocular Pressure Controlmentioning

confidence: 93%

Section: Adequate Intraocular Pressure Controlmentioning

confidence: 99%

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24-hour intraocular pressure monitoring: past, present, and future

Cronemberger

2024

Revista Brasileira De Oftalmologia

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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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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 3 publications

References 25 publications

24-hour intraocular pressure monitoring: past, present, and future

24-hour intraocular pressure monitoring: past, present, and future

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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