An Automated Algorithm for Optic Nerve Sheath Diameter Assessment from B-mode Ultrasound Images

Background and purpose: The optic nerve sheath diameter (ONSD) is a promising surrogate marker for the detection of raised intracranial pressure (ICP). However, inconsistencies in manual ONSD assessment are thought to affect ONSD and the corresponding ONSD cutoff values for the diagnosis of elevated ICP, hereby hampering the full potential of ONSD. In this study, we developed an image intensity-invariant algorithm to automatically estimate ONSD from B-mode ultrasound images at multiple depths.

Methods: The outcomes of the algorithm were validated against manual ONSD measurements by two human experts. Each expert analyzed the images twice (M1 and M2) in unknown order.

Results: The algorithm proved capable of segmenting the ONSD in 39 of 42 images, hereby showing mean differences of -.08 ± .45 and -.05 ± .41 mm compared to averaged ONSD values (M1 + M2/2) of Operator 1 and Operator 2, respectively, whereas the mean difference between the two experts was .03 ± .26 mm. Moreover, differences between algorithm-derived and expert-derived ONSD values were found to be much smaller than the 1 mm difference that is expected between patients with normal and elevated ICP, making it likely that our algorithm can distinguish between these patient groups.

Conclusions: Our algorithm has the potential to improve the accuracy of ONSD as a surrogate marker for elevated ICP because it has no intrinsic variability. However, future research should be performed to validate if the algorithm does indeed result in more accurate noninvasive ICP predictions.