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The value of artificial intelligence techniques in predicting pancreatic ductal adenocarcinoma with EUS images: A meta-analysis and systematic review

1 Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan; Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai; Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
2 Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
3 Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China

Correspondence Address:
Zhaoshen Li,
Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/EUS-D-21-00131

PMID: 35313419

Conventional EUS plays an important role in identifying pancreatic cancer. However, the accuracy of EUS is strongly influenced by the operator's experience in performing EUS. Artificial intelligence (AI) is increasingly being used in various clinical diagnoses, especially in terms of image classification. This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of pancreatic cancer using EUS images. We searched the Embase, PubMed, and Cochrane Library databases to identify studies that used endoscopic ultrasound images of pancreatic cancer and AI to predict the diagnostic accuracy of pancreatic cancer. Two reviewers extracted the data independently. The risk of bias of eligible studies was assessed using a Deek funnel plot. The quality of the included studies was measured by the QUDAS-2 tool. Seven studies involving 1110 participants were included: 634 participants with pancreatic cancer and 476 participants with nonpancreatic cancer. The accuracy of the AI for the prediction of pancreatic cancer (area under the curve) was 0.95 (95% confidence interval [CI], 0.93–0.97), with a corresponding pooled sensitivity of 93% (95% CI, 0.90-0.95), specificity of 90% (95% CI, 0.8-0.95), positive likelihood ratio 9.1 (95% CI 4.4-18.6), negative likelihood ratio 0.08 (95% CI 0.06-0.11), and diagnostic odds ratio 114 (95% CI 56–236). The methodological quality in each study was found to be the source of heterogeneity in the meta-regression combined model, which was statistically significant (P = 0.01). There was no evidence of publication bias. The accuracy of AI in diagnosing pancreatic cancer appears to be reliable. Further research and investment in AI could lead to substantial improvements in screening and early diagnosis.

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