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Deep learning and artificial intelligence in radiology: Current applications and future directions
Open accessAbstract
Radiological imaging diagnosis plays important roles in clinical patient management.Deep learning with convolutional neural networks (CNNs) is recently gaining wide attention for its high performance in recognizing images.If CNNs realize their promise in the context of radiology, they are anticipated to help radiologists achieve diagnostic excellence and to enhance patient healthcare.Here, we discuss very recent developments in the field, including studies published in the current PLOS Medicine Special Issue on Machine Learning in Health and Biomedicine, with comment on expectations and planning for artificial intelligence (AI) in the radiology clinic.Chest radiographs are one of the most utilized radiological modalities in the world and have been collected into a number of large datasets currently available to machine learning researchers.In this Special Issue, three groups of researchers applied deep learning to radiological imaging diagnosis using this modality.In the first, Pranav Rajpurkar and colleagues found that deep learning models detected clinically important abnormalities (e.g., edema, fibrosis, mass, pneumonia, and pneumothorax) on chest radiography, at a performance level comparable to practicing radiologists [1].In a similar study, Andrew Taylor and colleagues developed deep learning models that detected clinically significant pneumothoraces on chest radiography with excellent performance on data from the same site-with areas under the receiver operating characteristic curve (AUC) of 0.94-0.96[2].Meanwhile, Eric Oermann and colleagues investigated how well deep learning models that detected pneumonia on chest radiography generalized across different hospitals.They found that models trained on pooled data from sites with different pneumonia prevalence performed well on new pooled data from these same sites (AUC of 0.93-0.94)but significantly less well on external data (AUC 0.75-0.89);additional analyses supported the interpretation that deep learning models diagnosing pneumonia on chest radiography are able to exploit confounding information that is associated with pneumonia prevalence [3].Also in this Special Issue, Nicholas Bien and colleagues applied deep learning techniques to detect knee abnormalities on magnetic resonance (MR) imaging and found that the trained model showed near-human-level performance [4].Taking these four studies together, we can interpret that deep learning is currently able to diagnose a number of conditions using radiological data, but such diagnostic models may not be robust to a change in location.These Special Issue studies join a growing number of applications of deep learning to radiological images from various modalities that can aid with detection, diagnosis, staging, and subclassification of conditions.Cerebral aneurysms can be detected on MR angiography with
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“Such models often suffer from poor performance when transferred to other institutions with differing protocols, equipment, or patient populations.14,15”
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas
Chang, P. D., Grinband, J., Weinberg, B. D., Bardis, M. D., Khy, M., Cadena, G., Su, M., Cha, S., Filippi, C. G., Bota, D. A., et al. (2018) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5667 · Cited by 476
Deep Learning MR Imaging–based Attenuation Correction for PET/MR Imaging
Liu, F., Jang, H., Kijowski, R., Bradshaw, T., & McMillan, A. B. (2018) · Radiology · doi:10.1148/radiol.2017170700 · Cited by 412
Prevedello, L. M. S., Erdal, B. S., Ryu, J., Little, K. J., Demirer, M., Qian, S., & White, R. (2017) · Radiology · doi:10.1148/radiol.2017162664 · Cited by 248
Taylor, A. G., Mielke, C., & Mongan, J. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002697 · Cited by 212
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Deep neural network‐based computer‐assisted detection of cerebral aneurysms in MR angiography
Nakao, T., Hanaoka, S., Nomura, Y., Sato, I., Nemoto, M., Miki, S., Maeda, E., Yoshikawa, T., Hayashi, N., & Abe, O. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25842 · Cited by 194
Deep learning-based quantification of abdominal fat on magnetic resonance images
Grainger, A. T., Tustison, N. J., Qing, K., Roy, R. J., Berr, S. S., & Shi, W. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204071 · Cited by 13
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Fleming, K. A., Horton, S., Wilson, M. L., Atun, R. A., DeStigter, K. K., Flanigan, J. S., Sayed, S., Adam, P., Aguilar, B., Andronikou, S., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(21)00673-5 · Cited by 506
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Development and Validation of a Deep Learning Model for Non–Small Cell Lung Cancer Survival
She, Y., Jin, Z., Wu, J., Deng, J., Zhang, L., Su, H., Jiang, G., Liu, H., Xie, D., Cao, N., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.5842 · Cited by 276
Metadata
- Authors
- Yasaka, K., & Abe, O.
- Affiliations
- Institute of Medical Science, The University of Tokyo (JP)
- The University of Tokyo (JP)
- Corresponding author
- Yasaka, Koichiro
- Venue
- PLoS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-11-30
- Volume
- 15
- Issue
- 11
- Pages
- e1002707
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC6267951
- OpenAlex
- W2903224300
- Cited by
- 238
- References
- 13
- Citations by year
- 2026: 18 · 2025: 31 · 2024: 28 · 2023: 39 · 2022: 41 · 2021: 41 · 2020: 31 · 2019: 9
- FWCI
- 5.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- deep learning in radiology, convolutional neural networks, chest radiography, pneumonia detection, pneumothorax detection, model generalization, external validation, cerebral aneurysm detection, magnetic resonance angiography, clinical implementation of AI
- MeSH terms
- Deep Learning, Diagnosis, Computer-Assisted, Diffusion of Innovation, Humans, Predictive Value of Tests, Radiographic Image Interpretation, Computer-Assisted, Radiology, Reproducibility of Results
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