Novel Algorithm for Automated Optic Nerve Sheath Diameter Measurement Using a Clustering Approach
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Published on: September 4, 2021
Venkatakrishna Rajajee1,2,3, Reza Soroushmehr4,3, Craig A Williamson1,2,3
1Department of Neurological Surgery, University of Michigan, Ann Arbor, MI 48109-5338, USA.
Introduction:
Using ultrasound to measure optic nerve sheath diameter (ONSD) has been shown to be a useful modality to detect elevated intracranial pressure. However, manual assessment of ONSD by a human operator is cumbersome and prone to human errors. We aimed to develop and test an automated algorithm for ONSD measurement using ultrasound images and compare it to measurements performed by physicians.
Materials And Methods:
Patients were recruited from the Neurological Intensive Care Unit. Ultrasound images of the optic nerve sheath from both eyes were obtained using an ultrasound unit with an ocular preset. Images were processed by two attending physicians to calculate ONSD manually. The images were processed as well using a novel computerized algorithm that automatically analyzes ultrasound images and calculates ONSD. Algorithm-measured ONSD was compared with manually measured ONSD using multiple statistical measures.
Results:
Forty-four patients with an average/Standard Deviation (SD) intracranial pressure of 14 (9.7) mmHg were recruited and tested (with a range between 1 and 57 mmHg). A t-test showed no statistical difference between the ONSD from left and right eyes (P > 0.05). Furthermore, a paired t-test showed no significant difference between the manually and algorithm-measured ONSD with a mean difference (SD) of 0.012 (0.046) cm (P > 0.05) and percentage error of difference of 6.43% (P = 0.15). Agreement between the two operators was highly correlated (interclass correlation coefficient = 0.8, P = 0.26). Bland-Altman analysis revealed mean difference (SD) of 0.012 (0.046) (P = 0.303) and limits of agreement between -0.1 and 0.08. Receiver Operator Curve analysis yielded an area under the curve of 0.965 (P < 0.0001) with high sensitivity and specificity.
Conclusion:
The automated image-analysis algorithm calculates ONSD reliably and with high precision when compared to measurements obtained by expert physicians. The algorithm may have a role in computer-aided decision support systems in acute brain injury.
