scholarly journals Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century

2020 ◽  
Vol 93 (1106) ◽  
pp. 20190855 ◽  
Author(s):  
Issam El Naqa ◽  
Masoom A Haider ◽  
Maryellen L Giger ◽  
Randall K Ten Haken
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Diagnostic Imaging ◽  
Predictive Analytics ◽  
Current Status ◽  
Cost Burden ◽  
Computer Aided Detection ◽  
Daily Lives ◽  
Computer Aided ◽  
Software Platforms

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI’s inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI’s chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence

2020 ◽  
pp. 084653712094997
Author(s):  
William T. Tran ◽  
Ali Sadeghi-Naini ◽  
Fang-I Lu ◽  
Sonal Gandhi ◽  
Nicholas Meti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Screening ◽  
Diagnostic Accuracy ◽  
Breast Cancer Screening ◽  
Current Status ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Screening And Diagnosis

Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis. In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.

scholarly journals Artificial Intelligence in General

2019 ◽  
pp. 1-16
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Design ◽  
Intelligent Systems ◽  
Production Systems ◽  
Knowledge Bases ◽  
Computer Integrated Manufacturing ◽  
Current Status ◽  
Computer Aided ◽  
Control Computer ◽  
History Of

There are many kinds of uses for artificial intelligence (AI) in almost every field. AI is quite often used for control, computer aided design (CAD) and computer aided manufacturing (CAM), machine control, computer integrated manufacturing (CIM), production spot control, factory control, intelligent control, intelligent systems, deep learning, the cloud, knowledge bases, database, management, production systems, statistics, to assist sales forces, environment examination, agriculture, art, livings, daily life, etc. The present AI uses will be reexamined whether there is any matter to be considered further or not in AI research directions and their purposes behind the current status by looking at the history of AI development.

scholarly journals Su1267 DIAGNOSTIC IMAGING SYSTEM WITH VIRTUAL ENTEROSCOPY AND COMPUTER-AIDED DETECTION FOR EVALUATION OF SMALL BOWEL LESIONS OF CROHN'S DISEASE

2018 ◽  
Vol 87 (6) ◽  
pp. AB304
Author(s):  
Kazuhiro Furukawa ◽  
Ryoji Miyahara ◽  
Kohei Funasaka ◽  
Hiroki Suhara ◽  
Masanobu Matsushita ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Small Bowel ◽  
Crohn's Disease ◽  
Diagnostic Imaging ◽  
Imaging System ◽  
Computer Aided Detection ◽  
Computer Aided

scholarly journals Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

2020 ◽  
Author(s):  
Thanh Thi Nguyen
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Medical Image ◽  
Current Status ◽  
Future Research ◽  
Research Directions ◽  
Daily Lives ◽  
Related Data ◽  
Future Research Directions ◽  
The Internet Of Things

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

scholarly journals Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

2019 ◽  
Vol 52 (6) ◽  
pp. 387-396 ◽  
Author(s):  
Marcel Koenigkam Santos ◽  
José Raniery Ferreira Júnior ◽  
Danilo Tadao Wada ◽  
Ariane Priscilla Magalhães Tenório ◽  
Marcello Henrique Nogueira Barbosa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Diagnostic Imaging ◽  
Precision Medicine ◽  
Quantitative Information ◽  
Computer Aided Diagnosis ◽  
Rapid Expansion ◽  
Computer Aided ◽  
The Impact ◽  
Aided Diagnosis

Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

scholarly journals Present Limitations of Artificial Intelligence in the Emergency Setting – Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism

2021 ◽  
Author(s):  
Katharina Müller-Peltzer ◽  
Lena Kretzschmar ◽  
Giovanna Negrão de Figueiredo ◽  
Alexander Crispin ◽  
Robert Stahl ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Pulmonary Embolism ◽  
Emergency Room ◽  
False Positive ◽  
Detection Algorithm ◽  
Emergency Setting ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Noise Ratio

Purpose Since artificial intelligence is transitioning from an experimental stage to clinical implementation, the aim of our study was to evaluate the performance of a commercial, computer-aided detection algorithm of computed tomography pulmonary angiograms regarding the presence of pulmonary embolism in the emergency room. Materials and Methods This retrospective study includes all pulmonary computed tomography angiogram studies performed in a large emergency department over a period of 36 months that were analyzed by two radiologists experienced in emergency radiology to set a reference standard. Original reports and computer-aided detection results were compared regarding the detection of lobar, segmental, and subsegmental pulmonary embolism. All computer-aided detection findings were analyzed concerning the underlying pathology. False-positive findings were correlated to the contrast-to-noise ratio. Results Expert reading revealed pulmonary embolism in 182 of 1229 patients (49 % men, 10–97 years) with a total of 504 emboli. The computer-aided detection algorithm reported 3331 findings, including 258 (8 %) true-positive findings and 3073 (92 %) false-positive findings. Computer-aided detection analysis showed a sensitivity of 47 % (95 %CI: 33–61 %) on the lobar level and 50 % (95 %CI 43–56 %) on the subsegmental level. On average, there were 2.25 false-positive findings per study (median 2, range 0–25). There was no significant correlation between the number of false-positive findings and the contrast-to-noise ratio (Spearman’s Rank Correlation Coefficient = 0.09). Soft tissue (61.0 %) and pulmonary veins (24.1 %) were the most common underlying reasons for false-positive findings. Conclusion Applied to a population at a large emergency room, the tested commercial computer-aided detection algorithm faced relevant performance challenges that need to be addressed in future development projects. Key Points:  Citation Format

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