Systematic review and meta-analysis of AI-assisted imaging for abdominal infections shows enhanced diagnostic accuracy
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Key Takeaway
Consider AI-assisted imaging for abdominal infections; meta-analysis shows enhanced accuracy for appendicitis and pneumoperitoneum.
This systematic review and meta-analysis evaluates the diagnostic accuracy of AI-assisted imaging modalities for abdominal infections, including appendicitis, pneumoperitoneum, and cholecystitis. The authors synthesized data from eleven included studies to assess sensitivity, specificity, likelihood ratios, and diagnostic odds ratios. No specific population or setting details were reported in the source material.
Key findings demonstrate enhanced diagnostic accuracy for abdominal infections overall, with a sensitivity of 0.891 (95% CI: 0.824-0.944) and a specificity of 0.860 (95% CI: 0.784-0.922). For AI-aided CT specifically, sensitivity was 0.902 (95% CI: 0.850-0.948), while ultrasound sensitivity was 0.864 (95% CI: 0.792-0.922). The area under the curve (AUC) for pneumoperitoneum was 0.985, and the AUC for appendicitis was 0.947.
The authors highlight that future research should prioritize multicenter studies to validate AI models' generalizability and ensure consistent performance across diverse healthcare settings. No adverse events or safety data were reported. While the practice relevance suggests these modalities significantly enhance diagnostic accuracy, the evidence relies on the included studies without explicit reporting of absolute numbers or follow-up duration.
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View Original Abstract ↓
INTRODUCTION: Artificial intelligence (AI)-based imaging modalities are next-generation diagnostic devices for abdominal infections that promise to provide enhanced diagnostic speed and accuracy. This systematic review and meta-analysis critically analyze the diagnostic accuracy of AI-assisted imaging modalities, including for appendicitis, pneumoperitoneum, and cholecystitis, to present a balanced estimate of their clinical utility.
METHODS: A systematic literature search was undertaken in PubMed, Scopus, and Cochrane databases to search for studies that assessed the diagnostic accuracy of AI-based imaging modalities for abdominal infections. Eleven studies were included based on the inclusion criteria, and data were pooled for analysis. Diagnostic performance was measured by estimating sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), and area under the curve (AUC) using a random-effects model. Subgroup analyses were done to investigate the effect of infection type on diagnostic accuracy.
RESULTS: AI-assisted imaging demonstrated an overall sensitivity of 0.891 (95% CI: 0.824-0.944) and specificity of 0.860 (95% CI: 0.784-0.922) for diagnosing abdominal infections. Subgroup analysis revealed that AI-aided computed tomography (CT) exhibited a sensitivity of 0.902 (95% CI: 0.850-0.948), while ultrasound (US) showed a sensitivity of 0.864 (95% CI: 0.792-0.922). The highest AUCs were observed for pneumoperitoneum (0.985) and appendicitis (0.947), underscoring AI's robust diagnostic capabilities across multiple pathologies.
CONCLUSION: AI-imaging modalities significantly enhance diagnostic accuracy for abdominal infections, particularly for appendicitis and pneumoperitoneum. This meta-analysis underscores AI's clinical potential, though future research should prioritize multicenter studies to validate AI models' generalizability and ensure consistent performance across diverse healthcare settings.
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