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Computer-aided diagnosis: A survey with bibliometric analysis.

Ryohei Takahashi1, Yuya Kajikawa1

  • 1Department of Innovation Science, School of Environment and Society, Tokyo Institute of Technology, 3-3-6 Shibaura, Minato-ku, Tokyo, Japan.

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|March 29, 2017
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Computer-aided diagnosis (CAD) research has evolved significantly, moving from mammograms to brain diseases. Future directions include data-driven approaches and integration with clinical decision support systems.

Keywords:
Bibliometric analysisCADCitation network analysisComputer-aided diagnosis

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Computer-aided diagnosis (CAD) is a complex, interdisciplinary field merging medicine and engineering.
  • Over the past two decades, CAD has shown significant promise in medical diagnostics.
  • A comprehensive overview of CAD research is needed to guide future development.

Purpose of the Study:

  • To provide a research overview of Computer-aided diagnosis (CAD).
  • To analyze the evolution and classification of CAD research through bibliometric analysis.
  • To identify future directions and opportunities in the field of CAD.

Main Methods:

  • Conducted a literature survey focusing on Computer-aided diagnosis (CAD).
  • Performed bibliometric analysis to classify and categorize CAD research.
  • Examined research trends based on disease type and imaging modality.

Main Results:

  • CAD research is classified by disease type, progressing from mammography to brain imaging.
  • The data-driven approach shows promise, contrasting with traditional hypothetical methods.
  • Normalization of datasets and evaluation methods are crucial for algorithm and system adoption.

Conclusions:

  • CAD research has evolved, with emerging opportunities in data-driven methods and imaging instrument co-evolution.
  • Synergy between CAD and clinical decision support systems presents a significant future avenue.
  • Standardization of datasets and evaluation metrics is essential for advancing CAD.
  • Further research into CAD for bone and pancreatic cancer, alongside imaging advancements, is recommended.