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Machine learning and radiology

Vasiliki Foufi, Sébastien Lanteri, Christophe Gaudet-Blavignac, Pascal Remy, Xavier Montet, Christian Lovis
The aim of this work is to develop and validate an automatic annotation tool for the detection and bone localization of scaphoid fractures in radiology reports. To achieve this goal, a rule-based method using a Natural Language Processing (NLP) tool was applied. Finite state automata were constructed to detect, classify and annotate reports. An evaluation of the method on a manually annotated dataset has shown 96,8% of total match.
2018: Studies in Health Technology and Informatics
Subhamoy Mandal, Aaron B Greenblatt, Jingzhi An
Artificial intelligence (AI) and machine learning (ML) have influenced medicine in myriad ways, and medical imaging is at the forefront of technological transformation. Recent advances in AI/ML fields have made an impact on imaging and image analysis across the board, from microscopy to radiology. AI has been an active field of research since the 1950s; however, for most of this period, algorithms achieved subhuman performance and were not broadly adopted in medicine. Recent enhancements for computational hardware is enabling researchers to revisit old AI algorithms and experiment with new mathematical ideas...
September 2018: IEEE Pulse
A E Flanders, J E Jordan
The American Society of Neuroradiology has teamed up with the American College of Radiology and the Radiological Society of North America to create a catalog of neuroradiology common data elements that addresses specific clinical use cases. Fundamentally, a common data element is a question, concept, measurement, or feature with a set of controlled responses. This could be a measurement, subjective assessment, or ordinal value. Common data elements can be both machine- and human-generated. Rather than redesigning neuroradiology reporting, the goal is to establish the minimum number of "essential" concepts that should be in a report to address a clinical question...
September 20, 2018: AJNR. American Journal of Neuroradiology
Joel Aissa, Benedikt Michael Schaarschmidt, Janina Below, Oliver Th Bethge, Judith Böven, Lino Morris Sawicki, Norman-Philipp Hoff, Patric Kröpil, Gerald Antoch, Johannes Boos
PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma. MATERIALS AND METHODS: We retrospectively included consecutive malignant melanoma patients with a chest CT between 01/2015 and 01/2016. Machine learning based CAD software was used to reconstruct additional vessel-suppressed axial images. Three radiologists independently reviewed a maximum of 15 lung nodules per patient...
September 11, 2018: Clinical Imaging
Xiao-Ning Shao, Ying-Jie Sun, Kun-Tao Xiao, Yong Zhang, Wen-Bo Zhang, Zhi-Feng Kou, Jing-Liang Cheng
The diagnosis of dilated cardiomyopathy (DCM) remains a challenge in clinical radiology. This study aimed to investigate whether texture analysis (TA) parameters on magnetic resonance T1 mapping can be helpful for the diagnosis of DCM.A total of 50 DCM cases were retrospectively screened and 24 healthy controls were prospectively recruited between March 2015 and July 2017. T1 maps were acquired using the Modified Look-Locker Inversion Recovery (MOLLI) sequence at a 3.0 T MR scanner. The endocardium and epicardium were drawn on the short-axis slices of the T1 maps by an experienced radiologist...
September 2018: Medicine (Baltimore)
James A Nichols, Hsien W Herbert Chan, Matthew A B Baker
Machine learning (ML) is a form of artificial intelligence which is placed to transform the twenty-first century. Rapid, recent progress in its underlying architecture and algorithms and growth in the size of datasets have led to increasing computer competence across a range of fields. These include driving a vehicle, language translation, chatbots and beyond human performance at complex board games such as Go. Here, we review the fundamentals and algorithms behind machine learning and highlight specific approaches to learning and optimisation...
September 4, 2018: Biophysical Reviews
Julia E Goldberg, Andrew B Rosenkrantz
OBJECTIVE: To use Twitter to characterize public perspectives regarding artificial intelligence (AI) and radiology. METHODS AND MATERIALS: Twitter was searched for all tweets containing the terms "artificial intelligence" and "radiology" from November 2016 to October 2017. Users posting the tweets, tweet content, and linked websites were categorized. RESULTS: Six hundred and five tweets were identified. These were from 407 unique users (most commonly industry-related individuals [22...
July 23, 2018: Current Problems in Diagnostic Radiology
Roger D Dias, Avni Gupta, Steven J Yule
PURPOSE: To identify the different machine learning (ML) techniques that have been applied to automate physician competence assessment and evaluate how these techniques can be used to assess different competence domains in several medical specialties. METHOD: In May 2017, MEDLINE, EMBASE, PsycINFO, Web of Science, ACM Digital Library, IEEE Xplore Digital Library, PROSPERO, and Cochrane Database of Systematic Reviews were searched for articles published from inception to April 30, 2017...
August 14, 2018: Academic Medicine: Journal of the Association of American Medical Colleges
Filippo Pesapane, Caterina Volonté, Marina Codari, Francesco Sardanelli
Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place...
August 15, 2018: Insights Into Imaging
Amir M Tahmasebi, Henghui Zhu, Gabriel Mankovich, Peter Prinsen, Prescott Klassen, Sam Pilato, Rob van Ommering, Pritesh Patel, Martin L Gunn, Paul Chang
In today's radiology workflow, free-text reporting is established as the most common medium to capture, store, and communicate clinical information. Radiologists routinely refer to prior radiology reports of a patient to recall critical information for new diagnosis, which is quite tedious, time consuming, and prone to human error. Automatic structuring of report content is desired to facilitate such inquiry of information. In this work, we propose an unsupervised machine learning approach to automatically structure radiology reports by detecting and normalizing anatomical phrases based on the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) ontology...
August 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Ryan Clay, Srinivasan Rajagopalan, Ronald Karwoski, Fabien Maldonado, Tobias Peikert, Brian Bartholmai
The majority of incidentally and screen-detected lung cancers are adenocarcinomas. Optimal management of these tumors is clinically challenging due to variability in tumor histopathology and behavior. Invasive adenocarcinoma (IA) is generally aggressive while adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) may be extremely indolent. Computer Aided Nodule Analysis and Risk Yield (CANARY) is a quantitative computed tomography (CT) analysis tool that allows non-invasive assessment of tumor characteristics...
June 2018: Translational Lung Cancer Research
Hamed Hassanzadeh, Anthony Nguyen, Sarvnaz Karimi, Kevin Chu
OBJECTIVE: Application of machine learning techniques for automatic and reliable classification of clinical documents have shown promising results. However, machine learning models require abundant training data specific to each target hospital and may not be able to benefit from available labeled data from each of the hospitals due to data variations. Such training data limitations have presented one of the major obstacles for maximising potential application of machine learning approaches in the healthcare domain...
September 2018: Journal of Biomedical Informatics
Garry Choy, Omid Khalilzadeh, Mark Michalski, Synho Do, Anthony E Samir, Oleg S Pianykh, J Raymond Geis, Pari V Pandharipande, James A Brink, Keith J Dreyer
Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology...
August 2018: Radiology
G Langs, S Röhrich, J Hofmanninger, F Prayer, J Pan, C Herold, H Prosch
Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become an important building block of precision medicine. Furthermore, we discuss the roles machine learning can play in clinical routine and research and predict how it might change the field of radiology...
June 19, 2018: Der Radiologe
Babak Ehteshami Bejnordi, Maeve Mullooly, Ruth M Pfeiffer, Shaoqi Fan, Pamela M Vacek, Donald L Weaver, Sally Herschorn, Louise A Brinton, Bram van Ginneken, Nico Karssemeijer, Andrew H Beck, Gretchen L Gierach, Jeroen A W M van der Laak, Mark E Sherman
The breast stromal microenvironment is a pivotal factor in breast cancer development, growth and metastases. Although pathologists often detect morphologic changes in stroma by light microscopy, visual classification of such changes is subjective and non-quantitative, limiting its diagnostic utility. To gain insights into stromal changes associated with breast cancer, we applied automated machine learning techniques to digital images of 2387 hematoxylin and eosin stained tissue sections of benign and malignant image-guided breast biopsies performed to investigate mammographic abnormalities among 882 patients, ages 40-65 years, that were enrolled in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project...
June 13, 2018: Modern Pathology: An Official Journal of the United States and Canadian Academy of Pathology, Inc
Alfonso Emilio Gerevini, Alberto Lavelli, Alessandro Maffi, Roberto Maroldi, Anne-Lyse Minard, Ivan Serina, Guido Squassina
Radiological reporting generates a large amount of free-text clinical narratives, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary to make their content effectively available to radiologists in an aggregated form. In this paper we focus on the classification of chest computed tomography reports according to a classification schema proposed for this task by radiologists of the Italian hospital ASST Spedali Civili di Brescia...
June 7, 2018: Artificial Intelligence in Medicine
Alex John London
Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the "AI winter." With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks, AI is in another cycle of halcyon days. Although medicine is particularly recalcitrant to change, applications of AI in health care have professionals in fields like radiology worried about the future of their careers and have the public tittering about the prospect of soulless machines making life-and-death decisions...
May 2018: Hastings Center Report
Johannes Uhlig, Annemarie Uhlig, Meike Kunze, Tim Beissbarth, Uwe Fischer, Joachim Lotz, Susanne Wienbeck
OBJECTIVE: The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. SUBJECTS AND METHODS: Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation...
August 2018: AJR. American Journal of Roentgenology
Céline De Looze, Alan Beausang, Jane Cryan, Teresa Loftus, Patrick G Buckley, Michael Farrell, Seamus Looby, Richard Reilly, Francesca Brett, Hugh Kearney
INTRODUCTION: Machine learning methods have been introduced as a computer aided diagnostic tool, with applications to glioma characterisation on MRI. Such an algorithmic approach may provide a useful adjunct for a rapid and accurate diagnosis of a glioma. The aim of this study is to devise a machine learning algorithm that may be used by radiologists in routine practice to aid diagnosis of both: WHO grade and IDH mutation status in de novo gliomas. METHODS: To evaluate the status quo, we interrogated the accuracy of neuroradiology reports in relation to WHO grade: grade II 96...
May 16, 2018: Journal of Neuro-oncology
Daniel Pinto Dos Santos, Elmar Kotter
Significant technical advances have been made in radiology since the first discovery of X-rays. Diagnostic techniques have become more and more complex, workflows have been digitized, and data production has increased exponentially. However, the radiology report as the main method for communicating examination results has largely remained unchanged. Growing evidence supports that more structured radiology reports offer various benefits over conventional narrative reports. Various efforts have been made to further develop and promote structured reporting...
May 16, 2018: Annals of the New York Academy of Sciences
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