AI unleashed: A meta-analysis transforming radiological insights in diagnosing abdominal infections.
Authors
Affiliations (6)
Affiliations (6)
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Via del Vespro 129, Palermo, 90127, Italy. Electronic address: [email protected].
- Ahepa University Hospital Thessaloniki, St. Kiriakidi 1, Thessaloniki, 54636, Greece. Electronic address: [email protected].
- Department of Radiology, Hôpital de Morges, Chem. du Crêt 2, Morges, 1110, Switzerland. Electronic address: [email protected].
- Department of Radiology and Nuclear Medicine, Tϋbingen University Hospital, Otfried-Müller-Straße 14, Tübingen, 72076, Germany. Electronic address: [email protected].
- Institut für Radiologie und Nuklearmedizin, Helios Klinikum Pforzheim GmbH, Kanzlerstraße 2-6, Pforzheim, 75175, Germany. Electronic address: [email protected].
- Institut für Radiologie und Nuklearmedizin, Helios Klinikum Pforzheim GmbH, Kanzlerstraße 2-6, Pforzheim, 75175, Germany. Electronic address: [email protected].
Abstract
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. 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. 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. 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.