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

医学 胃肠病学 胰腺炎 腺癌 内科学 鉴别诊断 内镜超声检查 内镜逆行胰胆管造影术 胰腺 胰腺肿块 放射科 病理 内窥镜检查 癌症
作者
Takamichi Kuwahara,Kazuo Hara,Nobumasa Mizuno,Shin Haba,Nozomi Okuno,Yasuhiro Kuraishi,Daiki Fumihara,Takafumi Yanaidani,Sho Ishikawa,Masakazu Yamada,Sachiyo Oonishi,Keisaku Yamada,Tsutomu Tanaka,Masahiro Tajika,Yasumasa Niwa,Rui Yamaguchi,Yasuhiro Shimizu
出处
期刊:Endoscopy [Georg Thieme Verlag KG]
卷期号:55 (02): 140-149 被引量:12
标识
DOI:10.1055/a-1873-7920
摘要

Abstract Background There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI) is a mathematical technique that automates learning and recognition of data patterns. This study aimed to investigate the efficacy of our AI model using endoscopic ultrasonography (EUS) images of multiple types of pancreatic mass (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, and autoimmune pancreatitis [AIP]). Methods 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 noncarcinomatous pancreatic lesions was developed using a deep-learning architecture and the diagnostic performance of the AI model was evaluated. Results 22 000 images were generated from 933 patients. The area under the curve, sensitivity, specificity, and accuracy (95 %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 (0.90–0.99), 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), chronic pancreatitis 0.78 (0.52–0.94), and AIP 0.73 (0.39–0.94). Conclusions Our developed AI model can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions, but external validation is needed.
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