Literature DB >> 28347448

Computer-aided diagnosis: A survey with bibliometric analysis.

Ryohei Takahashi1, Yuya Kajikawa2.   

Abstract

Computer-aided diagnosis (CAD) has been a promising area of research over the last two decades. However, CAD is a very complicated subject because it involves a number of medicine and engineering-related fields. To develop a research overview of CAD, we conducted a literature survey with bibliometric analysis, which we report here. Our study determined that CAD research has been classified and categorized according to disease type and imaging modality. This classification began with the CAD of mammograms and eventually progressed to that of brain disease. Furthermore, based on our results, we discuss future directions and opportunities for CAD research. First, in contrast to the typical hypothetical approach, the data-driven approach has shown promise. Second, the normalization of the test datasets and an evaluation method is necessary when adopting an algorithm and a system. Third, we discuss opportunities for the co-evolution of CAD research and imaging instruments-for example, the CAD of bones and pancreatic cancer. Fourth, the potential of synergy with CAD and clinical decision support systems is also discussed.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Bibliometric analysis; CAD; Citation network analysis; Computer-aided diagnosis

Mesh:

Year:  2017        PMID: 28347448     DOI: 10.1016/j.ijmedinf.2017.02.004

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  16 in total

1.  Artificial intelligence in musculoskeletal oncological radiology.

Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

Review 2.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

3.  CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold.

Authors:  Ali Abbasian Ardakani; Ahmad Bitarafan-Rajabi; Afshin Mohammadi; Sepideh Hekmat; Aylin Tahmasebi; Mohammad Bagher Shiran; Ali Mohammadzadeh
Journal:  Eur Radiol       Date:  2019-01-09       Impact factor: 5.315

4.  Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment.

Authors:  G I Cassinelli Petersen; J Shatalov; T Verma; W R Brim; H Subramanian; A Brackett; R C Bahar; S Merkaj; T Zeevi; L H Staib; J Cui; A Omuro; R A Bronen; A Malhotra; M S Aboian
Journal:  AJNR Am J Neuroradiol       Date:  2022-03-31       Impact factor: 3.825

5.  Network analysis for estimating standardization trends in genomics using MEDLINE.

Authors:  Eun Bit Bae; Sejin Nam; Sungin Lee; Sun-Ju Ahn
Journal:  BMC Med Res Methodol       Date:  2022-10-07       Impact factor: 4.612

6.  Global Scientific Research Landscape on Medical Informatics From 2011 to 2020: Bibliometric Analysis.

Authors:  Xuefei He; Cheng Peng; Yingxin Xu; Ye Zhang; Zhongqing Wang
Journal:  JMIR Med Inform       Date:  2022-04-21

Review 7.  Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

Authors:  Qinghua Huang; Fan Zhang; Xuelong Li
Journal:  Biomed Res Int       Date:  2018-03-04       Impact factor: 3.411

8.  Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study.

Authors:  Liang Yongping; Zhang Juan; Ping Zhou; Zhao Yongfeng; Wengang Liu; Yifan Shi
Journal:  JMIR Med Inform       Date:  2020-05-05

9.  An emotional modulation model as signature for the identification of children developmental disorders.

Authors:  Arianna Mencattini; Francesco Mosciano; Maria Colomba Comes; Tania Di Gregorio; Grazia Raguso; Elena Daprati; Fabien Ringeval; Bjorn Schuller; Corrado Di Natale; Eugenio Martinelli
Journal:  Sci Rep       Date:  2018-09-27       Impact factor: 4.379

Review 10.  Deep learning in interstitial lung disease-how long until daily practice.

Authors:  Ana Adriana Trusculescu; Diana Manolescu; Emanuela Tudorache; Cristian Oancea
Journal:  Eur Radiol       Date:  2020-06-14       Impact factor: 5.315

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