Point-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. We 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. Seven 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. , VGG11bn, 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. AI-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. PROSPERO (CRD420250615096).
Çelik et al. (Sat,) studied this question.