Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses.

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Abstract

There are several types of pancreatic masses, so it is important to distinguish between them before treatment. Artificial intelligence (AI) is a mathematical technique that automates learning and recognizing data patterns. The aim of this study was to investigate the efficacy of our AI model using endoscopic ultrasonography (EUS) images of multiple types of pancreatic masses (pancreatic ductal adenocarcinoma: PDAC, pancreatic adenosquamous carcinoma: PASC, acinar cell carcinoma: ACC, metastatic pancreatic tumor: MPT, neuroendocrine carcinoma: NEC, neuroendocrine tumor: NET, solid-pseudopapillary neoplasm; SPN, chronic pancreatitis: CP, and autoimmune pancreatitis: AIP).Patients who underwent EUS were included in this retrospective study. The included patients were divided into training, validation, and test cohorts. Using these cohorts, an AI model that can distinguish pancreatic carcinomas from nonpancreatic carcinomas was developed using a deep learning architecture and the diagnostic performance of the AI model was evaluated.From 933 patients, a total of 22,000 images were generated. The area under the curve, sensitivity, specificity, and accuracy (95% confidence interval: CI) of the AI model for the diagnosis of pancreatic carcinomas in the test cohort were 0.90 (0.84-0.97), 0.94 (0.88-0.98), 0.82 (0.68-0.92), and 0.91 (0.85-0.95), respectively. The per-category sensitivities (95% CI) of each disease were PDAC: 0.96 (0.87-0.99), PASC: 1.00 (0.05-1.00), ACC: 1.00 (0.22-1.00), MPT: 0.33 (0.01-0.91), NEC: 1.00 (0.22-1.00), NET: 0.93 (0.66-1.00), SPN: 1.00 (0.22-1.00), CP: 0.78 (0.52-0.94), and AIP: 0.73 (0.39-0.94).Our developed AI model may distinguish pancreatic carcinomas from nonpancreatic carcinomas, but external validation is needed.Thieme. All rights reserved.