Automation of Ultrasonographic Optic Nerve Sheath Diameter Measurement: A Scoping Review

Abstract

Intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care in managing severe brain injury. However, current invasive ICP monitoring methods carry significant risks, including infection and intracranial hemorrhage, and are contraindicated in certain clinical situations. Additionally, these methods are not universally available. Optic nerve sheath diameter (ONSD) measurement presents a promising noninvasive alternative for ICP monitoring, though its clinical adoption has been limited due to its operator dependence and inconsistencies in imaging acquisition and measurement techniques. Automating both ONSD image acquisition and measurement could enhance accuracy and reliability, thereby improving its utility as a noninvasive ICP estimation tool. A range of image analysis and machine learning (ML) techniques have been applied to address these challenges. In this paper, we provide a narrative review of the current literature on ONSD automation, examining the strengths and limitations of classical image analysis and ML models in improving ONSD-based ICP assessment.