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Machine learning and radiology

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https://www.readbyqxmd.com/read/30532667/a-review-of-denoising-medical-images-using-machine-learning-approaches
#1
REVIEW
Prabhpreet Kaur, Gurvinder Singh, Parminder Kaur
Background: This paper attempts to identify suitable Machine Learning (ML) approach for image denoising of radiology based medical application. The Identification of ML approach is based on (i) Review of ML approach for denoising (ii) Review of suitable Medical Denoising approach. Discussion: The review focuses on six application of radiology: Medical Ultrasound (US) for fetus development, US Computer Aided Diagnosis (CAD) and detection for breast, skin lesions, brain tumor MRI diagnosis, X-Ray for chest analysis, Breast cancer using MRI imaging...
October 2018: Current Medical Imaging Reviews
https://www.readbyqxmd.com/read/30526348/with-a-little-help-from-machine-learning-precision-radiology-can-be-feasible
#2
Paul J Chang
No abstract text is available yet for this article.
December 11, 2018: Radiology
https://www.readbyqxmd.com/read/30515717/big-data-artificial-intelligence-and-structured-reporting
#3
REVIEW
Daniel Pinto Dos Santos, Bettina Baeßler
The past few years have seen a considerable rise in interest towards artificial intelligence and machine learning applications in radiology. However, in order for such systems to perform adequately, large amounts of training data are required. These data should ideally be standardised and of adequate quality to allow for further usage in training of artificial intelligence algorithms. Unfortunately, in many current clinical and radiological information technology ecosystems, access to relevant pieces of information is difficult...
December 5, 2018: European radiology experimental
https://www.readbyqxmd.com/read/30515089/deep-synthesis-of-realistic-medical-images-a-novel-tool-in-clinical-research-and-training
#4
Evgeniy Bart, Jay Hegdé
Making clinical decisions based on medical images is fundamentally an exercise in statistical decision-making. This is because in this case, the decision-maker must distinguish between image features that are clinically diagnostic (i.e., signal) from a large amount of non-diagnostic features. (i.e., noise). To perform this task, the decision-maker must have learned the underlying statistical distributions of the signal and noise to begin with. The same is true for machine learning algorithms that perform a given diagnostic task...
2018: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/30507747/classification-of-amyloid-pet-images-using-novel-features-for-early-diagnosis-of-alzheimer-s-disease-and-mild-cognitive-impairment-conversion
#5
Yu Yan, Edward Somer, Vicente Grau
BACKGROUND: New PET tracers could have a substantial impact on the early diagnosis of Alzheimer's disease (AD), particularly if they are accompanied by optimised image analysis and machine learning methods. Fractal dimension (FD) analysis, a measure of shape complexity, has been proven useful in MRI but its application to fluorine-18 amyloid PET has not yet been demonstrated. Shannon entropy (SE) has also been proposed as a measure of image complexity in DTI imaging, but it is not yet widely used in radiology...
November 29, 2018: Nuclear Medicine Communications
https://www.readbyqxmd.com/read/30480490/the-rsna-pediatric-bone-age-machine-learning-challenge
#6
Safwan S Halabi, Luciano M Prevedello, Jayashree Kalpathy-Cramer, Artem B Mamonov, Alexander Bilbily, Mark Cicero, Ian Pan, Lucas Araújo Pereira, Rafael Teixeira Sousa, Nitamar Abdala, Felipe Campos Kitamura, Hans H Thodberg, Leon Chen, George Shih, Katherine Andriole, Marc D Kohli, Bradley J Erickson, Adam E Flanders
Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs...
November 27, 2018: Radiology
https://www.readbyqxmd.com/read/30477892/comparative-effectiveness-of-convolutional-neural-network-cnn-and-recurrent-neural-network-rnn-architectures-for-radiology-text-report-classification
#7
Imon Banerjee, Yuan Ling, Matthew C Chen, Sadid A Hasan, Curtis P Langlotz, Nathaniel Moradzadeh, Brian Chapman, Timothy Amrhein, David Mong, Daniel L Rubin, Oladimeji Farri, Matthew P Lungren
This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost. We proposed two distinct deep learning models - (i) CNN Word - Glove, and (ii) Domain phrase attention-based hierarchical recurrent neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from over 7370 clinical thoracic computed tomography (CT) free-text radiology reports collected from four major healthcare centers...
November 23, 2018: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/30457565/a-dataset-of-clinically-generated-visual-questions-and-answers-about-radiology-images
#8
Jason J Lau, Soumya Gayen, Asma Ben Abacha, Dina Demner-Fushman
Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area of artificial intelligence, Visual Question Answering (VQA) in the medical domain explores approaches to this form of clinical decision support. Success of such machine learning tools hinges on availability and design of collections composed of medical images augmented with question-answer pairs directed at the content of the image...
November 20, 2018: Scientific Data
https://www.readbyqxmd.com/read/30453459/machine-learning-applications-for-the-differentiation-of-primary-central-nervous-system-lymphoma-from-glioblastoma-on-imaging-a-systematic-review-and-meta-analysis
#9
(no author information available yet)
OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered attention for their potential to aid in the diagnostic assessment of a variety of pathologies. Several ML algorithms have recently been designed to differentiate GBM from PCNSL radiologically with a high sensitivity and specificity...
November 1, 2018: Neurosurgical Focus
https://www.readbyqxmd.com/read/30453454/utility-of-deep-neural-networks-in-predicting-gross-total-resection-after-transsphenoidal-surgery-for-pituitary-adenoma-a-pilot-study
#10
(no author information available yet)
OBJECTIVEGross-total resection (GTR) is often the primary surgical goal in transsphenoidal surgery for pituitary adenoma. Existing classifications are effective at predicting GTR but are often hampered by limited discriminatory ability in moderate cases and by poor interrater agreement. Deep learning, a subset of machine learning, has recently established itself as highly effective in forecasting medical outcomes. In this pilot study, the authors aimed to evaluate the utility of using deep learning to predict GTR after transsphenoidal surgery for pituitary adenoma...
November 1, 2018: Neurosurgical Focus
https://www.readbyqxmd.com/read/30407743/computer-vs-human-deep-learning-versus-perceptual-training-for-the-detection-of-neck-of-femur-fractures
#11
Matthew Adams, Weijia Chen, David Holcdorf, Mark W McCusker, Piers Dl Howe, Frank Gaillard
INTRODUCTION: To evaluate the accuracy of deep convolutional neural networks (DCNNs) for detecting neck of femur (NoF) fractures on radiographs, in comparison with perceptual training in medically-naïve individuals. METHODS: This study extends a previous study that conducted perceptual training in medically-naïve individuals for the detection of NoF fractures on a variety of dataset sizes. The same anteroposterior hip radiograph dataset was used to train two DCNNs (AlexNet and GoogLeNet) to detect NoF fractures...
November 8, 2018: Journal of Medical Imaging and Radiation Oncology
https://www.readbyqxmd.com/read/30398438/machine-learning-in-radiology-resistance-is-futile
#12
Mykol Larvie
No abstract text is available yet for this article.
November 6, 2018: Radiology
https://www.readbyqxmd.com/read/30391224/using-a-natural-language-processing-and-machine-learning-algorithm-program-to-analyze-inter-radiologist-report-style-variation-and-compare-variation-between-radiologists-when-using-highly-structured-versus-more-free-text-reporting
#13
Lane F Donnelly, Robert Grzeszczuk, Carolina V Guimaraes, Wei Zhang, George S Bisset Iii
PURPOSE: To use a natural language processing and machine learning algorithm to evaluate inter-radiologist report variation and compare variation between radiologists using highly structured versus more free text reporting. MATERIALS AND METHODS: 28,615 radiology reports were analyzed for 4 metrics: verbosity, observational terms only, unwarranted negative findings, and repeated language in different sections. Radiology reports for two imaging examinations were analyzed and compared - one which was more templated (ultrasound - appendicitis) and one which relied on more free text (chest radiograph - single view)...
October 9, 2018: Current Problems in Diagnostic Radiology
https://www.readbyqxmd.com/read/30371657/predicting-treatment-response-to-image-guided-therapies-using-machine-learning-an-example-for-trans-arterial-treatment-of-hepatocellular-carcinoma
#14
Aaron Abajian, Nikitha Murali, Lynn Jeanette Savic, Fabian Max Laage-Gaupp, Nariman Nezami, James S Duncan, Todd Schlachter, MingDe Lin, Jean-François Geschwind, Julius Chapiro
Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. The objective of this study was to develop a method to predict response to intra-arterial treatment prior to intervention. The method provides a general framework for predicting outcomes prior to intra-arterial therapy. It involves pooling clinical, demographic and imaging data across a cohort of patients and using these data to train a machine learning model. The trained model is applied to new patients in order to predict their likelihood of response to intra-arterial therapy...
October 10, 2018: Journal of Visualized Experiments: JoVE
https://www.readbyqxmd.com/read/30356808/diagnostic-point-of-care-ultrasound-pocus-for-gastrointestinal-pathology-state-of-the-art-from-basics-to-advanced
#15
REVIEW
Fikri M Abu-Zidan, Arif Alper Cevik
The use of point-of-care ultrasound (POCUS) by non-radiologists has dramatically increased. POCUS is completely different from the routine radiological studies. POCUS is a Physiological, On spot, extension of the Clinical examination, that is Unique, and Safe. This review aims to lay the basic principles of using POCUS in diagnosing intestinal pathologies so as to encourage acute care physicians to learn and master this important tool. It will be a useful primer for clinicians who want to introduce POCUS into their clinical practice...
2018: World Journal of Emergency Surgery: WJES
https://www.readbyqxmd.com/read/30353365/artificial-intelligence-in-medical-imaging-threat-or-opportunity-radiologists-again-at-the-forefront-of-innovation-in-medicine
#16
REVIEW
Filippo Pesapane, Marina Codari, Francesco Sardanelli
One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as "machine/deep learning" and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100-150 per year in 2007-2008 to 700-800 per year in 2016-2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6-9% of articles...
October 24, 2018: European radiology experimental
https://www.readbyqxmd.com/read/30342682/extraction-of-bi-rads-findings-from-breast-ultrasound-reports-in-chinese-using-deep-learning-approaches
#17
Shumei Miao, Tingyu Xu, Yonghui Wu, Hui Xie, Jingqi Wang, Shenqi Jing, Yaoyun Zhang, Xiaoliang Zhang, Yinshuang Yang, Xin Zhang, Tao Shan, Li Wang, Hua Xu, Shui Wang, Yun Liu
BACKGROUND: The wide adoption of electronic health record systems (EHRs) in hospitals in China has made large amounts of data available for clinical research including breast cancer. Unfortunately, much of detailed clinical information is embedded in clinical narratives e.g., breast radiology reports. The American College of Radiology (ACR) has developed a Breast Imaging Reporting and Data System (BI-RADS) to standardize the clinical findings from breast radiology reports. OBJECTIVES: This study aims to develop natural language processing (NLP) methods to extract BI-RADS findings from breast ultrasound reports in Chinese, thus to support clinical operation and breast cancer research in China...
November 2018: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/30332296/state-of-the-art-machine-learning-applications-in-glioma-imaging
#18
Eyal Lotan, Rajan Jain, Narges Razavian, Girish M Fatterpekar, Yvonne W Lui
OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION: We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making...
October 17, 2018: AJR. American Journal of Roentgenology
https://www.readbyqxmd.com/read/30332290/peering-into-the-black-box-of-artificial-intelligence-evaluation-metrics-of-machine-learning-methods
#19
Guy S Handelma, Hong Kuan Kok, Ronil V Chandra, Amir H Razavi, Shiwei Huang, Mark Brooks, Michael J Lee, Hamed Asadi
OBJECTIVE: Machine learning (ML) and artificial intelligence (AI) are rapidly becoming the most talked about and controversial topics in radiology and medicine. Over the past few years, the numbers of ML- or AI-focused studies in the literature have increased almost exponentially, and ML has become a hot topic at academic and industry conferences. However, despite the increased awareness of ML as a tool, many medical professionals have a poor understanding of how ML works and how to critically appraise studies and tools that are presented to us...
October 17, 2018: AJR. American Journal of Roentgenology
https://www.readbyqxmd.com/read/30325645/will-machine-learning-end-the-viability-of-radiology-as-a-thriving-medical-specialty
#20
Stephen Chan, Eliot L Siegel
There have been tremendous advances in artificial intelligence (AI) and machine learning (ML) within the past decade, especially in the application of deep learning to various challenges. These include advanced competitive games (such as Chess and Go), self-driving cars, speech recognition, and intelligent personal assistants. Rapid advances in computer vision for recognition of objects in pictures have led some individuals, including computer science experts and health care system experts in machine learning, to make predictions that ML algorithms will soon lead to the replacement of the radiologist...
November 1, 2018: British Journal of Radiology
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