4.7 Article

Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound

期刊

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2021.106382

关键词

Ultrasound; Abdomen; Computer-aided diagnosis; Machine learning; Neural network; Pattern recognition

资金

  1. Ministry of Science and Technology (MOST) of Taiwan [MOST109-2221-E-038-018, MOST110-2628-E-038-001]
  2. Higher Education Sprout Project from the Ministry of Education (MOE) in Taiwan [DP2-110-21121-01-A-13]

向作者/读者索取更多资源

A machine learning system was developed to detect and localize gallstones, and to detect cholecystitis, with acceptable discrimination and speed. The system showed good performance with acceptable judgment and speed using specific algorithms.
Background and objective: Emergency physicians (EPs) frequently deal with abdominal pain, including that is caused by either gallstones or acute cholecystitis. Easy access and low cost justify point-of-care ultrasound (POCUS) use as a first-line test to detect these diseases; yet, the detection performance of POCUS by EPs is unreliable, causing misdiagnoses with serious impacts. This study aimed to develop a machine learning system to detect and localize gallstones and to detect acute cholecystitis by ultrasound (US) still images taken by physicians or technicians for preliminary diagnoses. Methods: Abdominal US images (> 89,0 0 0) were collected from 2386 patients in a hospital database. We constructed training sets for gallstones with or without cholecystitis ( N = 10,971) and cholecystitis with or without gallstones ( N = 7348) as positives. Validation sets were also constructed for gallstones ( N = 2664) and cholecystitis ( N = 1919). We applied a single-shot multibox detector (SSD) and a feature pyramid network (FPN) to classify and localize objects using image features extracted by ResNet-50 for gallstones, and MobileNet V2 to classify cholecystitis. The deep learning models were pretrained using the COCO-2017 and ILSVRC-2012 datasets. Results: Using the validation sets, the SSD-FPN-ResNet-50 and MobileNet V2 achieved areas under the receiver operating characteristics curve of 0.92 and 0.94, respectively. The inference speeds were 21 (47.6 frames per second, fps) and 7 ms (142.9 fps). Conclusions: A machine learning system was developed to detect and localize gallstones, and to detect cholecystitis, with acceptable discrimination and speed. This is the first study to develop this system for either gallstone or cholecystitis detection with absence or presence of each one. After clinical trials, this system may be used to assist EPs, including those in remote areas, for detecting these diseases. (c) 2021 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据