The DOI matches its Crossref record; 153 of the 157 references it cites were found in a registry or an index.
Computer-aided diagnosis in medical imaging: Historical review, current status and future potential
Open accessAbstract
Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a "second opinion" and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists' accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists.
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“Radiological imaging can be extremely complex, and it is recognized that the analysis of exams that produce hundreds of images, such as computed tomography (CT) and magnetic resonance imaging (MRI), poses challenges, even for experienced specialists(1-3).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [3]
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4 of the 157 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Doi K, Chan H-P, Giger ML. Method and system for enhancement and detection of abnormal anatomic regions in a digital image. United States Patent 4,907,156 (1990).
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Radiomics: Images Are More than Pictures, They Are Data
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421
Histopathological Image Analysis: A Review
Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034865 · Cited by 2,052
Recent advances and clinical applications of deep learning in medical image analysis
Chen, X., Wang, X., Zhang, K., Fung, K.-M. A., Thai, T., Moore, K. N., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102444 · Cited by 1,180
Overview of deep learning in medical imaging
Suzuki, K. (2017) · Radiological Physics and Technology · doi:10.1007/s12194-017-0406-5 · Cited by 1,131
Large scale deep learning for computer aided detection of mammographic lesions
Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965
Metadata
- Authors
- Doi, K.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Doi, Kunio
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2007-06
- Volume
- 31
- Issue
- 4-5
- Pages
- 198-211
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- PMC
- PMC1955762
- OpenAlex
- W2141619730
- Cited by
- 1,855
- References
- 157
- Citations by year
- 2026: 72 · 2025: 108 · 2024: 128 · 2023: 128 · 2022: 195 · 2021: 153 · 2020: 161 · 2019: 133 · 2018: 129 · 2017: 97 · 2016: 80 · 2015: 75 · 2014: 71 · 2013: 76 · 2012: 67
- FWCI
- 7.74
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Computerized Medical Imaging and Graphics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Radiological Society of North America
- University of Chicago
- Hologic
- U.S. Public Health Service
- National Cancer Institute (CA98119, CA 62625, R01CA062625, R01CA098119)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- computer-aided diagnosis, diagnostic imaging, PACS, second opinion, breast cancer detection, mammography, lung nodule detection, vertebral fracture detection, osteoporosis, intracranial aneurysm detection, bone scintigraphy, interstitial lung disease, differential diagnosis, image similarity search, history of CAD
- MeSH terms
- Diagnosis, Computer-Assisted, Diagnostic Imaging, Forecasting, Humans, Radiology Information Systems, United States, History, 20th Century
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