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Machine learning and radiology

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...
July 16, 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
Ronald M Summers
Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image analysis and interpretation. In this editorial, I compare the expectations with the realities with particular attention to applications in abdominal oncology imaging. I explore whether these technologies will leave us at a crossroads to an exciting future or to a sustained plateau and disillusionment...
May 5, 2018: Abdominal Radiology
Elie Diamandis, Carl Phillip Simon Gabriel, Urs Würtemberger, Konstanze Guggenberger, Horst Urbach, Ori Staszewski, Silke Lassmann, Oliver Schnell, Jürgen Grauvogel, Irina Mader, Dieter Henrik Heiland
BACKGROUND: The purpose of this study is to map spatial metabolite differences across three molecular subgroups of glial tumors, defined by the IDH1/2 mutation and 1p19q-co-deletion, using magnetic resonance spectroscopy. This work reports a new MR spectroscopy based classification algorithm by applying a radiomics analytics pipeline. MATERIALS: 65 patients received anatomical and chemical shift imaging (5 × 5 × 20 mm voxel size). Tumor regions were segmented and registered to corresponding spectroscopic voxels...
April 27, 2018: Journal of Neuro-oncology
W Katherine Tan, Saeed Hassanpour, Patrick J Heagerty, Sean D Rundell, Pradeep Suri, Hannu T Huhdanpaa, Kathryn James, David S Carrell, Curtis P Langlotz, Nancy L Organ, Eric N Meier, Karen J Sherman, David F Kallmes, Patrick H Luetmer, Brent Griffith, David R Nerenz, Jeffrey G Jarvik
RATIONALE AND OBJECTIVES: To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. MATERIALS AND METHODS: We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports...
March 28, 2018: Academic Radiology
Shahein H Tajmir, Tarik K Alkasab
Radiology practice will be altered by the coming of artificial intelligence, and the process of learning in radiology will be similarly affected. In the short term, radiologists will need to understand the first wave of artificially intelligent tools, how they can help them improve their practice, and be able to effectively supervise their use. Radiology training programs will need to develop curricula to help trainees acquire the knowledge to carry out this new supervisory duty of radiologists. In the longer term, artificially intelligent software assistants could have a transformative effect on the training of residents and fellows, and offer new opportunities to bring learning into the ongoing practice of attending radiologists...
June 2018: Academic Radiology
Les R Folio, Laura B Machado, Andrew J Dwyer
Multimedia-enhanced radiology report (MERR) development is defined and described from an informatics perspective, in which the MERR is seen as a superior information-communicating entity. Recent technical advances, such as the hyperlinking of report text directly to annotated images, improve MERR information content and accessibility compared with text-only reports. The MERR is analyzed by its components, which include hypertext, tables, graphs, embedded images, and their interconnections. The authors highlight the advantages of each component for improving the radiologist's communication of report content information and the user's ability to extract information...
March 2018: Radiographics: a Review Publication of the Radiological Society of North America, Inc
Tanveer Syeda-Mahmood
The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era...
March 2018: Journal of the American College of Radiology: JACR
Simukayi Mutasa, Peter D Chang, Carrie Ruzal-Shapiro, Rama Ayyala
Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269-1274, 2016; Arch Dis Child 81.2:172-173, 1999) atlas. The evaluation of BAA can be a tedious and time-consuming process for the radiologist. As such, several computer-assisted detection/diagnosis (CAD) methods have been proposed for automation of BAA. Classical CAD tools have traditionally relied on hard-coded algorithmic features for BAA which suffer from a variety of drawbacks...
February 5, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
James H Thrall, Xiang Li, Quanzheng Li, Cinthia Cruz, Synho Do, Keith Dreyer, James Brink
Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations...
March 2018: Journal of the American College of Radiology: JACR
Maryellen L Giger
Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data...
March 2018: Journal of the American College of Radiology: JACR
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