Clinical-Evolutionary Staging System of Primary Open-Angle Glaucoma Using Optical Coherence Tomography.

Area of Science:

• Ophthalmology
• Medical Imaging
• Computational Biology

Background:

• Primary open-angle glaucoma (POAG) is a leading cause of blindness.
• Early detection and staging are critical for effective blindness prevention.

Purpose of the Study:

• To develop an objective algorithm for classifying POAG into clinical-evolutionary stages.
• To improve early detection and management of glaucoma.

Main Methods:

• Utilized spectral domain optical coherence tomography (SD-OCT) on 1001 patients (766 healthy, 235 with glaucoma/ocular hypertension).
• Measured Bruch's membrane opening-minimum rim width (BMO-MRW) and peripapillary retinal nerve fibre layer (RNFL) thicknesses.
• Applied k-means and linear discriminant analysis for disease stage classification.

Main Results:

• Defined four distinct glaucoma stages with a classification accuracy over 92% (88% including healthy eyes).
• Developed an online application for predicting glaucoma stage probability.
• Demonstrated the efficacy of SD-OCT parameters in glaucoma staging.

Conclusions:

• Proposed a novel, objective algorithm for classifying POAG using SD-OCT.
• The algorithm facilitates precise staging for improved patient management.
• This advancement contributes to enhanced glaucoma diagnosis and treatment strategies.