SynthesisBMC emergency medicine2026
Diagnostic accuracy of AI-assisted point-of-care ultrasound for abdominal free fluid detection in FAST trauma assessment: a systematic review and meta-analysis.
Synthesis in BMC emergency medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
STUDY
objectivePoint-of-care ultrasound (PoCUS) is widely used in trauma care through the Focused Assessment with Sonography for Trauma (FAST) protocol, but its accuracy is highly operator-dependent. Artificial intelligence (AI) may reduce variability and improve reliability. This systematic review and meta-analysis evaluated the diagnostic accuracy of AI-assisted PoCUS for detecting free fluid in trauma patients.
methodsWe searched PubMed, Scopus, and Web of Science up to April 2025, following PRISMA-DTA guidelines (PROSPERO ID: CRD420250615096). Eligible studies assessed AI-assisted PoCUS using the FAST or FAST-equivalent views and provided sufficient data for diagnostic accuracy analysis. Because one eligible study evaluated an ascites cohort rather than trauma patients, we included it in the broader abdominal free-fluid analysis but excluded it from the trauma-only sensitivity analysis. Pooled estimates were calculated with a bivariate random-effects model. Study quality was assessed using QUADAS-AI tool.
resultsSeven retrospective studies (n = 2,332 patients, >34,000 images/videos) were included. The pooled analyses were based on the diagnostic units reported by the original studies, which varied across image-, frame-, video/clip-, FAST-view-, and examination-level data. In the trauma-only analysis, pooled sensitivity for detecting abdominal free fluid was 91.1% (95% CI: 77.9-96.8%) and specificity was 97.5% (95% CI: 95.3-98.7%), with negligible heterogeneity (I² < 1.0) and an AUC of 0.98. In the broader abdominal free-fluid analysis, which included one non-trauma ascites cohort, pooled sensitivity was 91.4% (95% CI: 81.6-96.3%) and specificity was 96.8% (95% CI: 86.5-99.3%), with an AUC of 0.97. CNN-based models showed similar performance (sensitivity 92.2%, specificity 95.4%, AUC 0.97). Narrative review highlighted substantial variability across models: high-performing frameworks such as YOLOv3 and ResNet50-V2 demonstrated sensitivities of 0.90-0.99, whereas others (e.g., VGG11_bn, MaskRCNN) were markedly less accurate. Evidence for pericardial effusion detection was limited to a single retrospective study and should be interpreted cautiously. No dedicated pelvic-view diagnostic accuracy data were available.
conclusionAI-assisted PoCUS shows promising retrospective diagnostic performance for detecting abdominal free fluid during FAST assessment in trauma settings. The strongest evidence currently supports abdominal applications, whereas data for the cardiac/pericardial component of the FAST examination remain insufficient. Prospective multicenter studies with real-time workflow integration are needed before routine clinical implementation can be recommended.
trial registrationPROSPERO (CRD420250615096).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.