"Artificial intelligence in radiology"
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 Opinion 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.
As dependence on computers has increased, automated methods for the identification and processing of these predefined features — collectively known as computer-aided detection (CADe) — have long been proposed and occasionally utilized in the clinic. Radiologist-defined criteria are distilled into a pattern-recognition problem where computer vision algorithms highlight conspicuous objects within the image. However, these algorithms are often taskspecific and do not generalize across diseases and imaging modalities. Additionally, the accuracy of traditional predefined featurebased CADe systems remains questionable, with ongoing efforts to reduce false positives. It is often the case that outputs have to be assessed by radiologists to decide whether a certain automated annotation merits further investigation, thereby making it labour intensive. In examining mammograms, some studies have reported that radiologists rarely altered their diagnostic decisions after viewing results from predefined, feature-based CADe systems and that their clinical integration had no statistical significance on the radiologists’ performance. This is owing, in part, to the subhuman performance of these systems. Recent efforts have explored deep learning-based CADe to detect pulmonary nodules in CT and prostate cancer in multiparametric imaging, specifically multiparametric MRI. In detecting lesions in mammograms, early results show that utilizing convolutional neural networks (CNNs; deep learning algorithms) in CADe outperforms traditional CADe systems at low sensitivity while performing comparably at high sensitivity and shows similar performance compared with human readers. These findings hint at the utility of deep learning in developing robust, high-performance CADe systems. (p.502)
KeywordsArtificial Intelligence, Radiology, Health, Algorithms, Cancer
ThemesHealth and Work, Automation
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