Literature DB >> 18514362

Computer-aided diagnosis: the emerging of three CAD systems induced by Japanese health care needs.

Hiroshi Fujita1, Yoshikazu Uchiyama, Toshiaki Nakagawa, Daisuke Fukuoka, Yuji Hatanaka, Takeshi Hara, Gobert N Lee, Yoshinori Hayashi, Yuji Ikedo, Xin Gao, Xiangrong Zhou.   

Abstract

The aim of this paper is to describe three emerging computer-aided diagnosis (CAD) systems induced by Japanese health care needs. CAD has been developing fast in the last two decades. The idea of using a computer to help in medical image diagnosis is not new. Some pioneer studies are dated back to the 1960s. In 1998, the first U.S. FDA (Food and Drug Administration) approved commercial CAD system, a film-digitized mammography system, was launched by R2 Technologies, Inc. The success was quickly repeated by a number of companies. The approval of Medicare CAD reimbursement in the U.S. in 2001 further boosted the industry. Today, CAD has its significance in the economy of the medical industry. FDA approved CAD products in the field of breast imaging (mammography, ultrasonography and breast MRI) and chest imaging (radiography and CT) can be seen. In Japan, as part of the "Knowledge Cluster Initiative" of the government, three computer-aided diagnosis (CAD) projects are hosted at the Gifu University since 2004. These projects are regarding the development of CAD systems for the early detection of (1) cerebrovascular diseases using brain MRI and MRA images by detecting lacunar infarcts, unruptured aneurysms, and arterial occlusions; (2) ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy using retinal fundus images; and (3) breast cancers using ultrasound 3-D volumetric whole breast data by detecting the breast masses. The projects are entering their final development stage. Preliminary results are presented in this paper. Clinical examinations will be started soon, and commercialized CAD systems for the above subjects will appear by the completion of this project.

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Year:  2008        PMID: 18514362     DOI: 10.1016/j.cmpb.2008.04.003

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  12 in total

1.  A "loop" shape descriptor and its application to automated segmentation of airways from CT scans.

Authors:  Jiantao Pu; Chenwang Jin; Nan Yu; Yongqiang Qian; Xiaohua Wang; Xin Meng; Youmin Guo
Journal:  Med Phys       Date:  2015-06       Impact factor: 4.071

2.  A Probabilistic Model to Support Radiologists' Classification Decisions in Mammography Practice.

Authors:  Jiaming Zeng; Francisco Gimenez; Elizabeth S Burnside; Daniel L Rubin; Ross Shachter
Journal:  Med Decis Making       Date:  2019-02-28       Impact factor: 2.583

3.  Multi class disorder detection of magnetic resonance brain images using composite features and neural network.

Authors:  Vandana V Kale; Satish T Hamde; Raghunath S Holambe
Journal:  Biomed Eng Lett       Date:  2019-03-04

Review 4.  Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review.

Authors:  Oliver Faust; Rajendra Acharya U; E Y K Ng; Kwan-Hoong Ng; Jasjit S Suri
Journal:  J Med Syst       Date:  2010-04-06       Impact factor: 4.460

5.  A differential geometric approach to automated segmentation of human airway tree.

Authors:  Jiantao Pu; Carl Fuhrman; Walter F Good; Frank C Sciurba; David Gur
Journal:  IEEE Trans Med Imaging       Date:  2010-09-16       Impact factor: 10.048

Review 6.  Endoscopic ultrasonography for surveillance of individuals at high risk for pancreatic cancer.

Authors:  Gabriele Lami; Maria Rosa Biagini; Andrea Galli
Journal:  World J Gastrointest Endosc       Date:  2014-07-16

7.  Construction of classifier based on MPCA and QSA and its application on classification of pancreatic diseases.

Authors:  Huiyan Jiang; Di Zhao; Tianjiao Feng; Shiyang Liao; Yenwei Chen
Journal:  Comput Math Methods Med       Date:  2013-05-22       Impact factor: 2.238

8.  Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: a diagnostic test.

Authors:  Maoling Zhu; Can Xu; Jianguo Yu; Yijun Wu; Chunguang Li; Minmin Zhang; Zhendong Jin; Zhaoshen Li
Journal:  PLoS One       Date:  2013-05-21       Impact factor: 3.240

9.  A novel multiinstance learning approach for liver cancer recognition on abdominal CT images based on CPSO-SVM and IO.

Authors:  Huiyan Jiang; Ruiping Zheng; Dehui Yi; Di Zhao
Journal:  Comput Math Methods Med       Date:  2013-12-04       Impact factor: 2.238

Review 10.  A survey on computer aided diagnosis for ocular diseases.

Authors:  Zhuo Zhang; Ruchir Srivastava; Huiying Liu; Xiangyu Chen; Lixin Duan; Damon Wing Kee Wong; Chee Keong Kwoh; Tien Yin Wong; Jiang Liu
Journal:  BMC Med Inform Decis Mak       Date:  2014-08-31       Impact factor: 2.796

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