: EUS is an important diagnostic tool in pancreatic lesions. Performance of single-center and/or single study artificial intelligence (AI) in the analysis of EUS-images of pancreatic lesions has been reported. The aim of this study was to quantitatively study the pooled rates of diagnostic performance of AI in EUS image analysis of pancreas using rigorous systematic review and meta-analysis methodology. Multiple databases were searched (from inception to December 2020) and studies that reported on the performance of AI in EUS analysis of pancreatic adenocarcinoma were selected. The random-effects model was used to calculate the pooled rates. In cases where multiple 2 × 2 contingency tables were provided for different thresholds, we assumed the data tables as independent from each other. Heterogeneity was assessed by I2% and 95% prediction intervals. Eleven studies were analyzed. The pooled overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 86% (95% confidence interval [82.8-88.6]), 90.4% (88.1-92.3), 84% (79.3-87.8), 90.2% (87.4-92.3) and 89.8% (86-92.7), respectively. On subgroup analysis, the corresponding pooled parameters in studies that used neural networks were 85.5% (80-89.8), 91.8% (87.8-94.6), 84.6% (73-91.7), 87.4% (82-91.3), and 91.4% (83.7-95.6)], respectively. Based on our meta-analysis, AI seems to perform well in the EUS-image analysis of pancreatic lesions.
Pooled diagnostic parameters of artificial intelligence in EUS image analysis of the pancreas: A descriptive quantitative review
Facciorusso, Antonio;
2022-01-01
Abstract
: EUS is an important diagnostic tool in pancreatic lesions. Performance of single-center and/or single study artificial intelligence (AI) in the analysis of EUS-images of pancreatic lesions has been reported. The aim of this study was to quantitatively study the pooled rates of diagnostic performance of AI in EUS image analysis of pancreas using rigorous systematic review and meta-analysis methodology. Multiple databases were searched (from inception to December 2020) and studies that reported on the performance of AI in EUS analysis of pancreatic adenocarcinoma were selected. The random-effects model was used to calculate the pooled rates. In cases where multiple 2 × 2 contingency tables were provided for different thresholds, we assumed the data tables as independent from each other. Heterogeneity was assessed by I2% and 95% prediction intervals. Eleven studies were analyzed. The pooled overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 86% (95% confidence interval [82.8-88.6]), 90.4% (88.1-92.3), 84% (79.3-87.8), 90.2% (87.4-92.3) and 89.8% (86-92.7), respectively. On subgroup analysis, the corresponding pooled parameters in studies that used neural networks were 85.5% (80-89.8), 91.8% (87.8-94.6), 84.6% (73-91.7), 87.4% (82-91.3), and 91.4% (83.7-95.6)], respectively. Based on our meta-analysis, AI seems to perform well in the EUS-image analysis of pancreatic lesions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
