Abstract:Bruch's membrane opening(BMO)is the true anatomical border of the optic nerve head and provides higher anatomical accuracy, structural stability and measurement repeatability than traditional assessments based on the clinically visible optic disc margin. Optical coherence tomography(OCT)serves as a key technical tool for quantitative assessment of BMO-associated structural parameters. With the development of spectral-domain OCT(SD-OCT)and swept-source OCT(SS-OCT), parameters such as BMO-minimum rim width(BMO-MRW), BMO-minimum rim area(BMO-MRA), BMO area, offset, tilt, and torsion have emerged as important imaging biomarkers for evaluating optic nerve head morphology and for assisting in the diagnosis, staging, and longitudinal monitoring of glaucoma, myopia, and pathological myopia. Traditionally, the acquisition of these parameters has relied heavily on manual segmentation, which is limited by low efficiency, strong subjectivity, and considerable inter-observer variability. In recent years, artificial intelligence has been increasingly applied to OCT image segmentation, quality control, cross-device calibration, and automated quantitative analysis, thereby improving efficiency, objectivity, and reproducibility, particularly in eyes with complex optic disc morphology. This review summarizes recent advances in the BMO-related terminology standardization, imaging and quantitative methodologies, quality control,artificial intelligence-assisted analysis, as well as clinical evidence of its application in glaucoma, myopia and pathological myopia. Particular emphasis is placed on their potential value in the differential diagnosis and follow-up evaluation of highly myopic eyes with glaucoma, aiming to provide a reference for the standardized clinical application and future translational use of BMO-related parameters.
