Related Experiment Video For Artificial intelligence
Updated: Sep 17, 2025
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Diagnostic performance of artificial intelligence based on contrast-enhanced computed tomography in pancreatic ductal
Guangzhao Yan1, Xuming Chen1, Yanyan Wang2
1Department of Emergency Medicine, Emergency and Critical Care Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.
Purpose:
This meta-analysis systematically evaluated the diagnostic performance of artificial intelligence (AI) based on contrast-enhanced computed tomography (CECT) in detecting pancreatic ductal adenocarcinoma (PDAC).
Methods:
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) guidelines, a comprehensive literature search was conducted across PubMed, Embase, and Web of Science from inception to March 2025. Bivariate random-effects models pooled sensitivity, specificity, and area under the curve (AUC). Heterogeneity was quantified via I² statistics, with subgroup analyses examining sources of variability, including AI methodologies, model architectures, sample sizes, geographic distributions, control groups and tumor stages.
Results:
Nineteen studies involving 5,986 patients in internal validation cohorts and 2,069 patients in external validation cohorts were included. AI models demonstrated robust diagnostic accuracy in internal validation, with pooled sensitivity of 0.94 (95% CI 0.89-0.96), specificity of 0.93 (95% CI 0.90-0.96), and AUC of 0.98 (95% CI 0.96-0.99). External validation revealed moderately reduced sensitivity (0.84; 95% CI 0.78-0.89) and AUC (0.94; 95% CI 0.92-0.96), while specificity remained comparable (0.93; 95% CI 0.87-0.96). Substantial heterogeneity (I² > 85%) was observed, predominantly attributed to methodological variations in AI architectures and disparities in cohort sizes.
Conclusions:
AI demonstrates excellent diagnostic performance for PDAC on CECT, achieving high sensitivity and specificity across validation scenarios. However, its efficacy varies significantly with clinical context and tumor stage. Therefore, prospective multicenter trials that utilize standardized protocols and diverse cohorts, including early-stage tumors and complex benign conditions, are essential to validate the clinical utility of AI.
