Spontaneous venous pulsations (SVP) are a common finding in healthy people. The absence of SVP is associated with rapid progression in glaucoma and increased intracranial pressure. Traditionally, SVP has been documented qualitatively by clinicians during biomicroscopy. Nowadays numerous imaging devices recording the fundus exist. Hence, video data for objectification of SVP is readily available. Still, these clinical datasets are afflicted with various quality issues and artifacts. In this machine vision based study, we explore methods to overcome challenges in identifying SVP in fundus videos of varying quality and provide a detailed protocol thereof. Hereby, we aim to lower the burden of access of implementing machine vision in clinical video datasets and quantification of SVP.
Keywords: Glaucoma; Intracranial pressure; Machine vision; Neuro-ophthalmology; Spontaneous venous pulsations.
© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.