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

• Published in: Progress in retinal and eye research Vol. 90; p. 101052
• Main Authors: Hood, Donald C., La Bruna, Sol, Tsamis, Emmanouil, Thakoor, Kaveri A., Rai, Anvit, Leshno, Ari, de Moraes, Carlos G.V., Cioffi, George A., Liebmann, Jeffrey M.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-09-2022
• Subjects: Glaucoma; OCT; Optical coherence tomography; Retinal ganglion cell layer; Retinal nerve fiber layer; Glaucoma; OCT; Retinal nerve fiber layer; Optical coherence tomography; Retinal ganglion cell layer
• ISSN: 1350-9462; 1873-1635
• DOI: 10.1016/j.preteyeres.2022.101052

A method for detecting glaucoma based only on optical coherence tomography (OCT) is of potential value for routine clinical decisions, for inclusion criteria for research studies and trials, for large-scale clinical screening, as well as for the development of artificial intelligence (AI) decision models. Recent work suggests that the OCT probability (p-) maps, also known as deviation maps, can play a key role in an OCT-based method. However, artifacts seen on the p-maps of healthy control eyes can resemble patterns of damage due to glaucoma. We document in section 2 that these glaucoma-like artifacts are relatively common and are probably due to normal anatomical variations in healthy eyes. We also introduce a simple anatomical artifact model based upon known anatomical variations to help distinguish these artifacts from actual glaucomatous damage. In section 3, we apply this model to an OCT-based method for detecting glaucoma that starts with an examination of the retinal nerve fiber layer (RNFL) p-map. While this method requires a judgment by the clinician, sections 4 and 5 describe automated methods that do not. In section 4, the simple model helps explain the relatively poor performance of commonly employed summary statistics, including circumpapillary RNFL thickness. 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. Finally, in section 6 we consider the implications of OCT-based methods for the clinic, research, screening, and the development of AI models.
•A method based upon only OCT can achieve excellent specificity and sensitivity.•A simple model of normal anatomical variation improves specificity.•The model helps explain the poor performance of common summary statistics.•An AI deep learning model has excellent sensitivity and specificity and validates the use of OCT.