Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review. (April 2022)

• Record Type: Journal Article
• Authors: Goyal, Hemant; Sherazi, Syed Ali Amir; Gupta, Shweta; Perisetti, Abhilash; Achebe, Ikechukwu; Ali, Aman; Tharian, Benjamin; Thosani, Nirav; Sharma, Neil R.
• Is Part Of: Therapeutic advances in gastroenterology. Volume 15(2022)
• Publication Date: 2022-04
• Subjects: artificial intelligence -- artificial neural network -- convolutional neural network -- endoscopic ultrasonography -- pancreatic cancer -- support vector machine; Gastroenterology -- Periodicals; Digestive organs -- Diseases -- Treatment -- Periodicals; Gastrointestinal system -- Diseases -- Treatment -- Periodicals; Liver -- Diseases -- Treatment -- Periodicals; Pharmacology -- Periodicals; Gastroenterology -- Periodicals; Gastrointestinal Diseases -- therapy -- Periodicals; Liver Diseases -- therapy -- Periodicals; Pharmacology -- Periodicals; Gastroentérologie -- Périodiques; Appareil digestif -- Maladies -- Traitement -- Périodiques; Tractus gastro-intestinal -- Maladies -- Traitement -- Périodiques; Hépatologie -- Périodiques; Foie -- Maladies -- Périodiques; Pharmacologie -- Périodiques; 616.3005
• DOI: 10.1177/17562848221093873
• Languages: English
• ISSNs: 1756-283X

Abstract

Background: Pancreatic cancer (PC) is a highly fatal malignancy with a global overall 5-year survival of under 10%. Screening of PC is not recommended outside of clinical trials. Endoscopic ultrasonography (EUS) is a very sensitive test to identify PC but lacks specificity and is operator-dependent, especially in the presence of chronic pancreatitis (CP). Artificial Intelligence (AI) is a growing field with a wide range of applications to augment the currently available modalities. This study was undertaken to study the effectiveness of AI with EUS in the diagnosis of PC. Methods: Studies from MEDLINE and EMBASE databases reporting the AI performance applied to EUS imaging for recognizing PC. Data were analyzed using descriptive statistics. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess the quality of the included studies. Results: A total of 11 articles reported the role of EUS in the diagnosis of PC. The overall accuracy, sensitivity, and specificity of AI in recognizing PC were 80–97.5%, 83–100%, and 50–99%, respectively, with corresponding positive predictive value (PPV) and negative predictive value (NPV) of 75–99% and 57–100%, respectively. Types of AI studied were artificial neural networks (ANNs), convolutional neural networks (CNN), and support vector machine (SVM). Seven studies using other than basic ANN reported a sensitivity and specificity of 88–96% and 83–94% to differentiate PC from CP. Two studies using SVM reported a 94–96% sensitivity, 93%–99% specificity, and 94–98% accuracy to diagnose PC from CP. The reported sensitivity and specificity of detection of malignant from benign Intraductal Papillary Mucinous Neoplasms (IPMNs) was 96% and 92%, respectively. Conclusion: AI reported a high sensitivity with high specificity and accuracy to diagnose PC, differentiate PC from CP, and differentiate benign from malignant IPMN when used with EUS.