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

Optic Nerve Sheath Diameter Point-of-Care Ultrasonography Quality Criteria Checklist: An International Consensus Statement on Optic Nerve Sheath Diameter Imaging and Measurement

OBJECTIVES

To standardize optic nerve sheath diameter (ONSD) point-of-care ultrasonography (POCUS) and improve its research and clinical utility by developing the ONSD POCUS Quality Criteria Checklist (ONSD POCUS QCC).

DESIGN

Three rounds of modified Delphi consensus process and three rounds of asynchronous discussions.

SETTING

Online surveys and anonymous asynchronous discussion.

SUBJECTS

Expert panelists were identified according to their expertise in ONSD research, publication records, education, and clinical use. A total of 52 panelists participated in the Delphi process.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Three Delphi rounds and three asynchronous discussion rounds generated consensus on quality criteria (QC). This started with 29 QC in addition to other QC proposed by expert panelists. The QC items were categorized into probe selection, safety, body position, imaging, measurement, and research considerations. At the conclusion of the study, 28 QC reached consensus to include in the final ONSD POCUS QCC. These QC were then reorganized, edited, and consolidated into 23 QC that were reviewed and approved by the panelists.

CONCLUSIONS

ONSD POCUS QCC standardizes ONSD ultrasound imaging and measurement based on international consensus. This can establish ONSD ultrasound in clinical research and improve its utility in clinical practice.

Automation of ultrasonographic optic nerve sheath diameter measurement using convolutional neural networks

BACKGROUND AND PURPOSE

Ultrasonographic optic nerve sheath (ONS) diameter is a noninvasive intracranial pressure (ICP) surrogate. ICP is monitored invasively in specialized intensive care units. Noninvasive ICP monitoring is important in less specialized settings. However, noninvasive ICP monitoring using ONS diameter (ONSD) is limited by the need for experts to obtain and perform measurements. We aim to automate ONSD measurements using a deep convolutional neural network (CNN) with a novel masking technique.

METHODS

We trained a CNN to reproduce masks that mark the ONS. The edges of the mask are defined by an expert. Eight models were trained with 1000 epochs per model. The Dice-similarity-coefficient-weighted averaged outputs of the eight models yielded the final predicted mask. Eight hundred and seventy-three images were obtained from 52 transorbital cine-ultrasonography sessions, performed on 46 patients with brain injuries. Eight hundred and fourteen images from 48 scanning sessions were used for training and validation and 59 images from four sessions for testing. Bland-Altman and Pearson linear correlation analyses were used to evaluate the agreement between CNN and expert measurements.

RESULTS

Expert ONSD measurements and CNN-derived ONSD estimates had strong agreement (r = 0.7, p < .0001). The expert mean ONSD (standard deviation) is 5.27 mm (0.43) compared to CNN mean estimate of 5.46 mm (0.37). Mean difference (95% confidence interval, p value) is 0.19 mm (0.10-0.27 mm, p = .0011), and root mean square error is 0.27 mm.

CONCLUSION

A CNN can learn ONSD measurement using masking without image segmentation or landmark detection.

Effects of head-down tilt on optic nerve sheath diameter in healthy subjects

PURPOSE

Intracranial pressure increases in head-down tilt (HDT) body posture. This study evaluated the effect of HDT on the optic nerve sheath diameter (ONSD) in normal subjects.

METHODS

Twenty six healthy adults (age 28 [4.7] years) participated in seated and 6° HDT visits. For each visit, subjects presented at 11:00 h for baseline seated scans and then maintained a seated or 6° HDT posture from 12:00 to 15:00 h. Three horizontal axial and three vertical axial scans were obtained at 11:00, 12:00 and 15:00 h with a 10 MHz ultrasonography probe on the same eye, randomly chosen per subject. At each time point, horizontal and vertical ONSD (mm) were quantified by averaging three measures taken 3 mm behind the globe.

RESULTS

In the seated visit, ONSDs were similar across time (p > 0.05), with an overall mean (standard deviation) of 4.71 (0.48) horizontally and 5.08 (0.44) vertically. ONSD was larger vertically than horizontally at each time point (p < 0.001). In the HDT visit, ONSD was significantly enlarged from baseline at 12:00 and 15:00 h (p < 0.001 horizontal and p < 0.05 vertical). Mean (standard error) horizontal ONSD change from baseline was 0.37 (0.07) HDT versus 0.10 (0.05) seated at 12:00 h (p = 0.002) and 0.41 (0.09) HDT versus 0.12 (0.06) seated at 15:00 h (p = 0.002); mean vertical ONSD change was 0.14 (0.07) HDT versus -0.07 (0.04) seated at 12:00 h (p = 0.02) and 0.19 (0.06) HDT versus -0.03 (0.04) seated at 15:00 h (p = 0.01). ONSD change in HDT was similar between 12:00 and 15:00 h (p ≥ 0.30). Changes at 12:00 h correlated with those at 15:00 h for horizontal (r = 0.78, p < 0.001) and vertical ONSD (r = 0.73, p < 0.001).

CONCLUSION

The ONSD increased when body posture transitioned from seated to HDT position without any further change at the end of the 3 h in HDT.

Development of a Deep Learning-Based System for Optic Nerve Characterization in Transorbital Ultrasound Images on a Multicenter Data Set

OBJECTIVE

Characterization of the optic nerve through measurement of optic nerve diameter (OND) and optic nerve sheath diameter (ONSD) using transorbital sonography (TOS) has proven to be a useful tool for the evaluation of intracranial pressure (ICP) and multiple neurological conditions. We describe a deep learning-based system for automatic characterization of the optic nerve from B-mode TOS images by automatic measurement of the OND and ONSD. In addition, we determine how the signal-to-noise ratio in two different areas of the image influences system performance.

METHODS

A UNet was trained as the segmentation model. The training was performed on a multidevice, multicenter data set of 464 TOS images from 110 subjects. Fivefold cross-validation was performed, and the training process was repeated eight times. The final prediction was made as an ensemble of the predictions of the eight single models. Automatic OND and ONSD measurements were compared with the manual measurements taken by an expert with a graphical user interface that mimics a clinical setting.

RESULTS

A Dice score of 0.719 ± 0.139 was obtained on the whole data set merging the test folds. Pearson's correlation was 0.69 for both OND and ONSD parameters. The signal-to-noise ratio was found to influence segmentation performance, but no clear correlation with diameter measurement performance was determined.

CONCLUSION

The developed system has a good correlation with manual measurements, proving that it is feasible to create a model capable of automatically analyzing TOS images from multiple devices. The promising results encourage further definition of a standard protocol for the automatization of the OND and ONSD measurement process using deep learning-based methods. The image data and the manual measurements used in this work will be available at 10.17632/kw8gvp8m8x.1.

A Narrative Review of Point of Care Ultrasound Assessment of the Optic Nerve in Emergency Medicine

Point of care ultrasound (POCUS) of the optic nerve is easy to learn and has great diagnostic potential. Within emergency medicine, research has primarily focused on its use for the assessment of increased intracranial pressure, but many other applications exist, though the literature is heterogeneous and largely observational. This narrative review describes the principles of POCUS of the optic nerve including anatomy and scanning technique, as well as a summary of its best studied clinical applications of relevance in emergency medicine: increased intracranial pressure, idiopathic intracranial hypertension, optic neuritis, acute mountain sickness, and pediatric intracranial pressure assessment. In many of these applications, sonographic optic nerve sheath diameter (ONSD) has moderately high sensitivity and specificity, but the supporting studies are heterogeneous. Further studies should focus on standardization of the measurement of ONSD, establishment of consistent diagnostic thresholds for elevated intracranial pressure, and automation of ONSD measurement.

Observer Variability as a Determinant of Measurement Error of Ultrasonographic Measurements of the Optic Nerve Sheath Diameter: A Systematic Review

BACKGROUND

Ultrasonographic measurements of the diameter of the sheath of the optic nerve can be used to assess intracranial pressure indirectly. These measurements come with measurement error.

OBJECTIVE

Our aim was to estimate observer's measurement error as a determinant of ultrasonographic measurement variability of the optic nerve sheath diameter.

METHODS

A systematic search of the literature was conducted in Embase, Medline, Web of Science, the Cochrane Central Register of Trials, and the first 200 articles of Google Scholar up to April 19, 2021. Inclusion criteria were the following: healthy adults, B-mode ultrasonography, and measurements 3 mm behind the retina. Studies were excluded if standard error of measurement could not be calculated. Nine studies featuring 389 participants (median 40; range 15-100) and 22 observers (median 2; range 1-4) were included. Standard error of measurement and minimal detectable differences were calculated to quantify observer variability. Quality and risk of bias were assessed with the Guidelines for Reporting Reliability and Agreement Studies.

RESULTS

The standard error of measurement of the intra- and interobserver variability had a range of 0.10-0.41 mm and 0.14-0.42 mm, respectively. Minimal detectable difference of a single observer was 0.28-1.1 mm. Minimal detectable difference of multiple observers (range 2-4) was 0.40-1.1 mm. Quality assessment showed room for methodological improvement of included studies.

CONCLUSIONS

The standard errors of measurement and minimal detectable differences of ultrasonographic measurements of the optic nerve sheath diameter found in this review with healthy participants indicate caution should be urged when interpreting results acquired with this measurement method in clinical context.