Computer-aided diagnosis: A survey with bibliometric analysis

• Published in: International journal of medical informatics (Shannon, Ireland) Vol. 101; pp. 58 - 67
• Main Authors: Takahashi, Ryohei, Kajikawa, Yuya
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.05.2017
• Subjects: Algorithms; Bibliometric analysis; Bibliometrics; Breast Neoplasms - diagnosis; CAD; Citation network analysis; Computer-aided diagnosis; Diagnosis, Computer-Assisted - methods; Female; Humans; Internal Medicine; Mammography - methods; Other; Surveys and Questionnaires; Citation network analysis; Bibliometric analysis; Computer-aided diagnosis; CAD
• ISSN: 1386-5056; 1872-8243; 1872-8243
• DOI: 10.1016/j.ijmedinf.2017.02.004

•The development of computer-aided diagnosis(CAD) has been summarized with citation network analysis.•CAD research has been classified and categorized by disease types and imaging modalities.•CAD research initially started with mammograms CAD, has progressed to brain disease CAD.•Based on our results, we discussed future directions and opportunities of CAD research; model, data, and instruments. 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.