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

医学 超声科 人工智能 鉴别诊断 内镜超声检查 放射科 普通外科 病理 内窥镜检查 计算机科学
作者
Takamichi Kuwahara,Kazuo Hara,Nobumasa Mizuno,Shin Haba,Nozomi Okuno,Yasuhiro Kuraishi,Daiki Fumihara,Takafumi Yanaidani,Sho Ishikawa,Tsukasa Yasuda,Masanori Yamada,Sachiyo Onishi,Keisaku Yamada,Tsutomu Tanaka,Masahiro Tajika,Yasumasa Niwa,Rui Yamaguchi,Yasuhiro Shimizu
出处
期刊:Endoscopy [Thieme Medical Publishers (Germany)]
卷期号:55 (02): 140-149 被引量:59
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
顶刊刺客cc完成签到,获得积分10
刚刚
Wu关注了科研通微信公众号
刚刚
量子星尘发布了新的文献求助20
刚刚
刚刚
勤奋晓啸发布了新的文献求助10
1秒前
1秒前
daoyi完成签到,获得积分10
1秒前
SUN完成签到,获得积分0
2秒前
mumu发布了新的文献求助10
2秒前
姬昌发布了新的文献求助10
3秒前
卢莹完成签到,获得积分10
3秒前
花生了什么树关注了科研通微信公众号
4秒前
冷酷的葶发布了新的文献求助10
4秒前
5秒前
5秒前
英姑应助科研通管家采纳,获得10
6秒前
bkagyin应助科研通管家采纳,获得10
6秒前
爆米花应助怕黑的擎采纳,获得10
6秒前
科研通AI5应助科研通管家采纳,获得10
6秒前
小马甲应助科研通管家采纳,获得10
6秒前
852应助科研通管家采纳,获得10
6秒前
科研通AI2S应助科研通管家采纳,获得10
6秒前
田様应助科研通管家采纳,获得10
7秒前
充电宝应助科研通管家采纳,获得10
7秒前
Orange应助科研通管家采纳,获得10
7秒前
浮游应助xutaiyu采纳,获得10
7秒前
科研通AI6应助科研通管家采纳,获得10
7秒前
LaTeXer应助科研通管家采纳,获得30
7秒前
SciGPT应助科研通管家采纳,获得10
7秒前
汉堡包应助科研通管家采纳,获得10
7秒前
天天快乐应助科研通管家采纳,获得10
7秒前
LaTeXer应助科研通管家采纳,获得30
7秒前
7秒前
yeemelo完成签到,获得积分10
7秒前
跳跃的猹完成签到,获得积分10
8秒前
8秒前
8秒前
姬昌完成签到,获得积分20
10秒前
幸福的芷蕊完成签到,获得积分10
10秒前
xin完成签到,获得积分10
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
二维材料在应力作用下的力学行为和层间耦合特性研究 600
苯丙氨酸解氨酶的祖先序列重建及其催化性能 500
Schifanoia : notizie dell'istituto di studi rinascimentali di Ferrara : 66/67, 1/2, 2024 470
Laboratory Animal Technician TRAINING MANUAL WORKBOOK 2012 edtion 400
Progress and Regression 400
A review of Order Plesiosauria, and the description of a new, opalised pliosauroid, Leptocleidus demoscyllus, from the early cretaceous of Coober Pedy, South Australia 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4839688
求助须知:如何正确求助?哪些是违规求助? 4142259
关于积分的说明 12823395
捐赠科研通 3887159
什么是DOI,文献DOI怎么找? 2137133
邀请新用户注册赠送积分活动 1157203
关于科研通互助平台的介绍 1057107