Reliability of Smartphone for Diffusion-Weighted Imaging–Alberta Stroke Program Early Computed Tomography Scores in Acute Ischemic Stroke Patients: Diagnostic Test Accuracy Study
Bibliographic record
Abstract
BACKGROUND: High-quality neuroimages can be viewed using a medical app installed on a smartphone. Although interdevice agreement between smartphone and desktop PC monitor was found to be favorable for evaluating computed tomography images, there are no interdevice agreement data for diffusion-weighted imaging (DWI). OBJECTIVE: The aim of our study was to compare DWI interpretation using the Join smartphone app with that using a desktop PC monitor, in terms of interdevice and interrater agreement and elapsed interpretation time. METHODS: The ischemic change in the DWI of consecutive patients with acute stroke in the middle cerebral artery territory was graded by 2 vascular neurologists using the Join smartphone app and a desktop PC monitor. The vascular neurologists were blinded to all patient information. Each image was categorized as either Diffusion-Weighted Imaging-Alberta Stroke Program Early Computed Tomography Scores (DWI-ASPECTS) ≥7 or DWI-ASPECTS <7 according to the Japanese Society for Neuroendovascular Therapy. We analyzed interdevice agreement and interrater agreement with respect to DWI-ASPECTS. Elapsed interpretation time was compared between DWI-ASPECTS evaluated by the Join smartphone app and a desktop PC monitor. RESULTS: We analyzed the images of 111 patients (66% male; median age=69 years; median National Institutes of Health Stroke Scale score on admission=4). Interdevice agreement regarding DWI-ASPECTS between the smartphone and the desktop PC monitor was favorable (vascular neurologist 1: κ=0.777, P<.001, vascular neurologist 2: κ=0.787, P<.001). Interrater agreement was also satisfactory for the smartphone (κ=0.710, P<.001) and the desktop PC monitor (κ=0.663, P<.001). Median elapsed interpretation time was similar between the smartphone and the desktop PC monitor (vascular neurologist 1: 1.7 min vs 1.6 min; P=.64); vascular neurologist 2: 2.4 min vs 2.0 min; P=.14). CONCLUSIONS: The use of a smartphone app enables vascular neurologists to estimate DWI-ASPECTS accurately and rapidly. The Join medical smartphone app shows great promise in the management of acute stroke.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".