Deep Learning in Medical Imaging: General Overview

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae; Seo, Joon Beom; Kim, Namkug

doi:10.3348/kjr.2017.18.4.570

Cited by 1,030 publications

(643 citation statements)

References 55 publications

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“…There is great debate about the speed with which newer deep learning methods will be implemented in clinical radiology practice 88 , with speculations for the time needed to fully automate clinical tasks ranging from a few years to decades. The development of deep learning-based automated solutions will begin with tackling the most common clinical problems where sufficient data are available.…”

Section: Ai Challenges In Medical Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Section: Ai Challenges In Medical Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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“…DCNN extracts low- to high-level features from the training images and uses them to select the most important features for solving a given task 17 . Considering the ability of DCNN to distinguish complex objects in the ImageNet challenge, we postulated that DCNN could show good performance in discriminating between tuberculous and pyogenic spondylitis.…”

Section: Introductionmentioning

confidence: 99%

Performance of the deep convolutional neural network based magnetic resonance image scoring algorithm for differentiating between tuberculous and pyogenic spondylitis

Kim

Lee

et al. 2018

Sci Rep

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The purpose of this study was to evaluate the performance of the deep convolutional neural network (DCNN) in differentiating between tuberculous and pyogenic spondylitis on magnetic resonance (MR) imaging, compared to the performance of three skilled radiologists. This clinical retrospective study used spine MR images of 80 patients with tuberculous spondylitis and 81 patients with pyogenic spondylitis that was bacteriologically and/or histologically confirmed from January 2007 to December 2016. Supervised training and validation of the DCNN classifier was performed with four-fold cross validation on a patient-level independent split. The object detection and classification model was implemented as a DCNN and was designed to calculate the deep-learning scores of individual patients to reach a conclusion. Three musculoskeletal radiologists blindly interpreted the images. The diagnostic performances of the DCNN classifier and of the three radiologists were expressed as receiver operating characteristic (ROC) curves, and the areas under the ROC curves (AUCs) were compared using a bootstrap resampling procedure. When comparing the AUC value of the DCNN classifier (0.802) with the pooled AUC value of the three readers (0.729), there was no significant difference (P = 0.079). In differentiating between tuberculous and pyogenic spondylitis using MR images, the performance of the DCNN classifier was comparable to that of three skilled radiologists.

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“…For this reason, the timeliness of research data is essential; indeed, some journals provide submitting authors with specific guidelines in this regard (1). Moreover, in radiology, data timeliness may be especially important since the field relies heavily on new technology particularly digital technology (234567), which develops and changes faster than in other disciplines. Thus, the timeliness of data in radiology journals may be an important indicator of quality or impact, and the present study analyzed and compared several representative general radiology journals in terms of the age of the data published therein.…”

Section: Introductionmentioning

confidence: 99%

Age of Data in Contemporary Research Articles Published in Representative General Radiology Journals

2018

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ObjectiveTo analyze and compare the age of data in contemporary research articles published in representative general radiology journals.Materials and MethodsWe searched for articles reporting original research studies analyzing patient data that were published in the print issues of the Korean Journal of Radiology (KJR), European Radiology (ER), and Radiology in 2017. Eligible articles were reviewed to extract data collection period (time from first patient recruitment to last patient follow-up) and age of data (time between data collection end and publication). The journals were compared in terms of the proportion of articles reporting the data collection period to the level of calendar month and regarding the age of data.ResultsThere were 50, 492, and 254 eligible articles in KJR, ER, and Radiology, respectively. Of these, 44 (88%; 95% confidence interval [CI]: 75.8–94.8%), 359 (73%; 95% CI: 68.9–76.7%), and 211 (83.1%; 95% CI: 78–87.2%) articles, respectively, provided enough details of data collection period, revealing a significant difference between ER and Radiology (p = 0.002). The age of data was significantly greater in KJR (median age: 826 days; range: 299–2843 days) than in ER (median age: 570 days; range: 56–4742 days; p < 0.001) and Radiology (median age: 618; range: 75–4271 days; p < 0.001).ConclusionKorean Journal of Radiology did not fall behind ER or Radiology in reporting of data collection period, but showed a significantly greater age of data than ER and Radiology, suggesting that KJR should take measures to improve the timeliness of its data.

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Deep Learning in Medical Imaging: General Overview

Cited by 1,030 publications

References 55 publications

Artificial intelligence in radiology

Artificial intelligence in radiology

Performance of the deep convolutional neural network based magnetic resonance image scoring algorithm for differentiating between tuberculous and pyogenic spondylitis

Age of Data in Contemporary Research Articles Published in Representative General Radiology Journals

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