Study Overview
Ethics
This study has regulatory and ethical approvals for waiver of consent. All data were deidentified and delinked from patient records. The shift-T function on this PACS system removes all text, which removes all identifiers.
Design
Cohort study comparing PACS read CT images to smartphone captured and processed images.
Setting
CT scans from the trauma registry at a Level 1 trauma center in Austin, Texas.
Image Cohort Selection
We selected 8 standard of care baseline computed tomography (CT) scans from traumatic intracranial hemorrhage patients in the trauma registry to represent mild, moderate and severe subdural hematomas and multicompartmental hemorrhage, and mild and moderate isolated intraparenchymal contusions. Subdural severity degree was divided by 0-5 mm width, 5-10 mm width and >10 mm width. Multicompartmental and intraparenchymal hemorrhage was defined as total hematoma volume of all bleeding sites of 1-10 cc, 10-30 cc and >30 cc. All volumes were calculated by the simplified ABC/2 method 3,4. The axial CT images were acquired from the skull base to the vertex without contrast. Dose lowering techniques included adjusting the mA and/or kV per standard of care.
Variables
The primary outcome variables were hematoma volume measured by ABC/2, Marshal Scale, midline shift measurement, image quality by contrast-to-noise ratio (CNR) and time to capture. A radiologist and an imaging scientist applied ABC/2 method, calculated the Marshall scale and midline shift on the video images and on the PACS in a randomized order. We calculate the intraclass correlation coefficient (ICC). We measured image quality by calculating contrast-to-noise ratio (CNR). We report summary statistics on time to capture in the smartphone group without a comparator.
Data Source
Trauma registry standard of care CT scans of traumatic intracranial hemorrhage.
Bias
We selected the scans to represent different severity of disease, which is known to affect ability to measure and is thus a better test of a new capture method, but this could be seen as a selection bias. The raters were unblinded to the study purpose which could introduce bias.
Data Collection
Data collectors included a convenience sample of emergency department (ED) staff present on a Saturday morning, including 3 attending emergency physicians, 2 emergency resident physicians, 1 emergency nurse, 1 emergency technician, 2 social workers and a clerk. A central ED radiology reading station was used to access the Picture Archive and Communication System (PACS) via Synapse version 4.4 (Fujifilm Healthcare, Lexington, MA). The images were in DICOM format with text (and thus identifiers) hidden. Each data collector used a smartphone to record the screen while using the arrow button to scroll through axial slices of the non-contrast CT of each selected scan. The smartphones used were 3 iPhone 12s, 2 iPhone 11s, 1 iPhone 10, 1 iPhone 9, a Samsung Galaxy S7, a Samsung Galaxy S9 and an Android OnePlus. Data collection methods are outlined in Figure 1.
Image Data Management
Videos were saved in mp4 or mov format at native resolution and emailed via a secure healthcare email platform for post processing. The resulting files were evaluated for completeness, technical quality, and adherence to the protocol requirements. Video files were converted to tagged image file (tif) format at a rate of 30 frames per second using Python version 3.7.2. Image analyses were performed using ImageJ 5 a Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation. All CT images were randomly presented to avoid observer recall bias.
Image Data Collection
Image read data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at Dell Medical School at The University of Texas Austin 6,7. The embedded scalebar in each deidentified image was used to calibrate the scale and obtain data values that related to spatial resolution including subdural hematoma thickness, midline shift, and hematoma volume (via the ABC/2 method) 3,4. To quantitatively assess lesion conspicuity, image contrast to noise was calculated via
[1]
where SI indicates the mean signal intensity within either the lesion or surrounding tissue and σ is the standard deviation of the image noise.
The Marshall score for diffuse head injury was recorded for each data set 8. The ratings included: 1, Normal for age; 2, High/mixed density mass less than 25cc, midline shift less than 5mm, basilar cisterns preserved; 3, Basilar cisterns effaced & High/mixed density mass less than 25cc, midline shift less than 5mm, basilar cisterns preserved; 4, Midline shift greater than 5mm; 5, Evacuated mass lesion: High/mixed density mass >25cc which was surgically evacuated; 6, Non-evacuated mass lesion: High/mixed density mass >25cc not surgically treated.
Image Raters
The image raters were one imaging scientist and one radiologist working independently and reviewing the images in a randomized order, but we could not blind them as the PACS images were read on PACS and the smartphone images were read on ImageJ.
Primary Outcomes
The primary outcomes were hematoma volume measured by ABC/2, Marshal Scale, midline shift measurement, image quality by contrast-to-noise ratio (CNR) and time to capture.
Statistics
All statistical analyses were completed in RStudio version 1.4.1106 (PBC, Boston, MA) using R version 4.1.0 (https://www.R-project.org/). Interrater reliability was evaluated using the intraclass correlation coefficient (ICC). Specifically, the ICC1 was calculated to reflect the amount of total variance of the measurements for each lesion type in relation to the total variance using the Pearson correlation with an = 0.05. To further assess the level of agreement between image analysis on the PACS vs. smartphones for quantitative measures (volume, SDH thickness, and midline shift), Bland Altman analyses were performed with acceptable limits defined a priori to ensure the calculated critical difference was greater than the difference between each smartphone measurement and PACS.
To detect differences across the multiple phones and users in capturing the quantitative measures and ordinal Marshall and Likert scores for each lesion type, the non-parametric Friedman λ2 test was used with post-hoc Kendall W to determine effect size. Significance for all analyses was set at ∝=0.05.
