Sol La Bruna

Detecting glaucoma with only OCT: Implications for the clinic, research, screening, and AI development.

In section 5, the model helps account for the success of an AI deep learning model, which in turn validates our focus on the RNFL p-map.

The OCT RNFL Probability Map and Artifacts Resembling Glaucomatous Damage.

Glaucoma-like artifacts on RNFL p-maps are relatively common and can masquerade as arcuate and/or widespread/temporal damage.

A Topographic Comparison of OCT Minimum Rim Width (BMO-MRW) and Circumpapillary Retinal Nerve Fiber Layer (cRNFL) Thickness Measures in Eyes With or Suspected Glaucoma.

Although BMO-MRW and cRNFL measures agreed in the majority of hemidiscs, they still disagreed in over 25% of the EG hemidiscs.

Detection of Early Glaucomatous Damage: Performance of Summary Statistics From Optical Coherence Tomography and Perimetry.

OCT and VF metrics, single or in combinations, have only moderate sensitivity for eyes with early glaucoma.

A Model of Progression to Help Identify Macular Damage Due to Glaucoma.

Second, the patterns of progression predicted by the model suggest alternative end points for clinical trials. Finally, the model provides a heuristic for future research concerning the anatomic basis of glaucomatous damage.

The 24-2 Visual Field Guided Progression Analysis Can Miss the Progression of Glaucomatous Damage of the Macula Seen Using OCT.

In eyes with early glaucoma (i.e., 24-2 MD, > -6 dB) in this study, the 24-2 GPA missed progression seen using OCT and exhibited a relatively high rate of false positives.

The ICD-10 Glaucoma Severity Score Underestimates the Extent of Glaucomatous Optic Nerve Damage.

The ICD-10 system relies solely on damage seen on the 24-2 and as provides a 24-2 functional score rather than a "glaucoma" severity score.

A Pattern-Based OCT Metric for Glaucoma Detection.

The new metric outperformed other OCT metrics for detecting glaucomatous damage.

Progression of Early Glaucomatous Damage: Performance of Summary Statistics From Optical Coherence Tomography and Perimetry.

Although relying solely on metrics is not recommended for clinical purposes, in situations requiring very high specificity and precision, combinations of OCT-OCT metrics can be used.

Distinguishing Healthy From Glaucomatous Eyes With Optical Coherence Tomography Global Circumpapillary Retinal Nerve Fiber Thickness in the Bottom 5th Percentile.

Quantitative metrics can distinguish between most of the healthy and glaucomatous eyes with low global cpRNFL thickness. However, even the most successful metric, RMS cpRNFL, missed some glaucomatous eyes.

A Deep Learning Model Detects Glaucoma Based on an OCT Report, but Where Should the Clinician Look to Identify Glaucomatous Damage?

Although the DLM heat maps are flagging clinically meaningful elements of the report, in their existing form, they are not optimal for guiding the clinician to relevant locations of damage.

Glaucoma Detection Using Optical Coherence Tomography: Reviewing the Pitfalls of Comparison to Normative Data.

This review describes various artifacts that originate from using a normative database to compare the individual's scans.

Test of a Retinal Nerve Fiber Bundle Trajectory Model Using Eyes With Glaucomatous Optic Neuropathy.

The arcuate model performed well in predicting the borders of arcuate patterns seen on RNFL p-maps. It also predicted the associated abnormal regions of the cpRNFL thickness plots.

Understanding Patterns of Preserved Retinal Ganglion Cell Layer in Advanced Glaucoma as Seen With Optical Coherence Tomography.

All eyes with advanced glaucoma had damage to the critically important central, donut-shaped GCL region.

Improving glaucoma staging in clinical practice by combining the ICD-10 glaucoma severity classification system and optical coherence tomography.

Combining OCT and VF data provides better staging of glaucoma severity than VF data alone.