Mariottoni Eduardo B

Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.

A segmentation-free DL algorithm performed better than conventional RNFL thickness parameters for diagnosing glaucomatous damage on OCT scans, especially in early disease.

Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs.

An M2M DL algorithm performed as well as, if not better than, human graders at detecting eyes with repeatable glaucomatous visual field loss.

Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning.

A deep learning model was able to obtain objective and quantitative estimates of RNFL thickness that correlated well with SD OCT measurements and potentially could be used to monitor for glaucomatous changes over time.

Rates of Glaucomatous Structural and Functional Change From a Large Clinical Population: The Duke Glaucoma Registry Study.

Although most patients under routine care had slow rates of progression, a substantial proportion had rates that could potentially result in major losses if sustained over time.

Artificial Intelligence Mapping of Structure to Function in Glaucoma.

A CNN was capable of predicting SAP sensitivity thresholds from SDOCT RNFL thickness measurements and generate an SF map from simulated defects.

Blood Pressure and Glaucomatous Progression in a Large Clinical Population.

When adjusted for IOP, lower MAP and DAP during follow-up were significantly associated with faster rates of RNFL loss, suggesting that levels of systemic BP may be a significant factor in glaucoma progression.

Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

Longitudinal changes in a deep learning algorithm's predictions of RNFL thickness measurements based on fundus photographs can be used to predict risk of glaucoma conversion in eyes suspected of having the disease.

Impact of Intraocular Pressure Control on Rates of Retinal Nerve Fiber Layer Loss in a Large Clinical Population.

Intraocular pressure was significantly associated with rates of progressive RNFL loss in a large clinical population.

Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT.

A DL model was able to assess the probability of glaucomatous structural progression from SD-OCT RNFL thickness measurements.

Comparison of Short- And Long-Term Variability in Standard Perimetry and Spectral Domain Optical Coherence Tomography in Glaucoma.

Long-term variability was higher than short-term variability on SD-OCT and SAP.

Comparing the Rule of 5 to Trend-based Analysis for Detecting Glaucoma Progression on OCT.

Trend-based analysis was superior to the simple rule of 5 for identifying progression in glaucoma eyes and should be preferred as a method for longitudinal assessment of global SD-OCT RNFL change over time.

The Relationship Between Asymmetries of Corneal Properties and Rates of Visual Field Progression in Glaucoma Patients.

CH asymmetry between eyes was associated with asymmetry on rates of visual field change, providing further support for the role of CH as a risk factor for glaucoma progression.