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Machine learning and radiology

S L Andersen, F B S Briggs, J H Winnike, Y Natanzon, S Maichle, K J Knagge, L K Newby, S G Gregory
BACKGROUND: Diagnostic delays are common for multiple sclerosis (MS) since diagnosis typically depends on the presentation of nonspecific clinical symptoms together with radiologically-determined central nervous system (CNS) lesions. It is important to reduce diagnostic delays as earlier initiation of disease modifying therapies mitigates long-term disability. Developing a metabolomic blood-based MS biomarker is attractive, but prior efforts have largely focused on specific subsets of metabolite classes or analytical platforms...
March 9, 2019: Multiple Sclerosis and related Disorders
Chulho Kim, Vivienne Zhu, Jihad Obeid, Leslie Lenert
BACKGROUND AND PURPOSE: This project assessed performance of natural language processing (NLP) and machine learning (ML) algorithms for classification of brain MRI radiology reports into acute ischemic stroke (AIS) and non-AIS phenotypes. MATERIALS AND METHODS: All brain MRI reports from a single academic institution over a two year period were randomly divided into 2 groups for ML: training (70%) and testing (30%). Using "quanteda" NLP package, all text data were parsed into tokens to create the data frequency matrix...
2019: PloS One
Yoav Mintz, Ronit Brodie
The term Artificial Intelligence (AI) was coined by John McCarthy in 1956 during a conference held on this subject. However, the possibility of machines being able to simulate human behavior and actually think was raised earlier by Alan Turing who developed the Turing test in order to differentiate humans from machines. Since then, computational power has grown to the point of instant calculations and the ability evaluate new data, according to previously assessed data, in real time. Today, AI is integrated into our daily lives in many forms, such as personal assistants (Siri, Alexa, Google assistant etc...
February 27, 2019: Minimally Invasive Therapy & Allied Technologies: MITAT
I Lavdas, B Glocker, D Rueckert, S A Taylor, E O Aboagye, A G Rockall
Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characterisation. We are currently involved in an National Institute of Health Research (NIHR)-funded project, which aims to develop machine learning methods to improve the diagnostic performance and reduce the radiology reading time of whole-body magnetic resonance imaging (MRI) scans, in patients being staged for cancer (MALIBO study). We describe here the main challenges we have encountered during the course of this project...
February 22, 2019: Clinical Radiology
Sang Min Lee, Joon Beom Seo, Jihye Yun, Young-Hoon Cho, Jens Vogel-Claussen, Mark L Schiebler, Warren B Gefter, Edwin J R van Beek, Jin Mo Goo, Kyung Soo Lee, Hiroto Hatabu, James Gee, Namkug Kim
Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction using numerous processing layers. A distinctive feature of deep learning, compared with conventional machine learning methods, is that it can generate appropriate models for tasks directly from the raw data, removing the need for human-led feature extraction. Medical images are particularly suited for deep learning applications. Deep learning techniques have already demonstrated high performance in the detection of diabetic retinopathy on fundoscopic images and metastatic breast cancer cells on pathologic images...
March 2019: Journal of Thoracic Imaging
Li-Qiang Zhou, Jia-Yu Wang, Song-Yuan Yu, Ge-Ge Wu, Qi Wei, You-Bin Deng, Xing-Long Wu, Xin-Wu Cui, Christoph F Dietrich
Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload...
February 14, 2019: World Journal of Gastroenterology: WJG
Chen Cui, Shinn-Huey S Chou, Laura Brattain, Constance D Lehman, Anthony E Samir
OBJECTIVE: Data engineering is the foundation of effective machine learning model development and research. The accuracy and clinical utility of machine learning models fundamentally depend on the quality of the data used for model development. This article aims to provide radiologists and radiology researchers with an understanding of the core elements of data preparation for machine learning research. We cover key concepts from an engineering perspective, including databases, data integrity, and characteristics of data suitable for machine learning projects, and from a clinical perspective, including the HIPAA, patient consent, avoidance of bias, and ethical concerns related to the potential to magnify health disparities...
February 19, 2019: AJR. American Journal of Roentgenology
Heidi Coy, Kevin Hsieh, Willie Wu, Mahesh B Nagarajan, Jonathan R Young, Michael L Douek, Matthew S Brown, Fabien Scalzo, Steven S Raman
PURPOSE: Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution...
February 18, 2019: Abdominal Radiology
Ryad Zemouri, Christine Devalland, Séverine Valmary-Degano, Noureddine Zerhouni
Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models. Two phases are required: a learning and a using phase. The two main applications are classification and regression Computer tools such as GPU computational accelerators or some development tools such as MATLAB libraries are used. Their application field is vast and allows the management of big data in genomics and molecular biology as well as the automated analysis of histological slides...
February 14, 2019: Annales de Pathologie
Virginia Mato-Abad, Andrés Labiano-Fontcuberta, Santiago Rodríguez-Yáñez, Rafael García-Vázquez, Cristian R Munteanu, Javier Andrade-Garda, Angela Domingo-Santos, Victoria Galán Sánchez-Seco, Yolanda Aladro, Mª Luisa Martínez-Ginés, Lucía Ayuso, Julián Benito-León
INTRODUCTION: The unanticipated magnetic resonance imaging (MRI) detection in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named as radiologically isolated syndrome (RIS). As the difference between early MS (i.e., clinically isolated syndrome [CIS]) and RIS is the occurrence of a clinical event, it should be logical to improve detection of subclinical form without interfering with MRI as there are radiological diagnostic criteria for that...
February 3, 2019: European Journal of Neurology: the Official Journal of the European Federation of Neurological Societies
Marthony Robins, Justin Solomon, Lynne M Hurwitz Koweek, Jared Christensen, Ehsan Samei
PURPOSE: To make available to the medical imaging community a computed tomography (CT) image database composed of hybrid datasets (patient CT images with digitally inserted anthropomorphic lesions) where lesion ground truth is known a priori. It is envisioned that such a dataset could be a resource for the assessment of CT image quality, machine learning, and imaging technologies (e.g., computer aided detection (CAD) and segmentation algorithms). ACQUISITION AND VALIDATION METHODS: This HIPPA compliant, IRB waiver of approval study consisted of utilizing 120 chest and 100 abdominal clinically acquired adult CT exams...
January 31, 2019: Medical Physics
Peter Brotchie
No abstract text is available yet for this article.
January 30, 2019: Journal of Medical Imaging and Radiation Oncology
Sarfaraz Hussein, Pujan Kandel, Candice W Bolan, Michael B Wallace, Ulas Bagci
Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computeraided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D Convolutional Neural Network and Transfer Learning...
January 23, 2019: IEEE Transactions on Medical Imaging
Jo Sung Jung, Yoon Seong Choi, Sung Soo Ahn, Seong Yi, Se Hoon Kim, Seung-Koo Lee
PURPOSE: Diffuse midline glioma with histone H3 K27M mutation is a new entity described in the 2016 update of the World Health Organization Classification of Tumors of the Central Nervous System. The purpose of this study was to evaluate the clinical and imaging characteristics to predict the presence of H3 K27M mutation in spinal cord glioma using a machine learning-based classification model. METHODS: A total of 41 spinal cord glioma patients consisting of 24 H3 K27M mutants and 17 wild types were enrolled in this retrospective study...
January 20, 2019: Neuroradiology
Marina Codari, Simone Schiaffino, Francesco Sardanelli, Rubina Manuela Trimboli
OBJECTIVE: The purpose of this study is to review literature from the past decade on applications of artificial intelligence (AI) to breast MRI. MATERIALS AND METHODS: In June 2018, a systematic search of the literature was performed to identify articles on the use of AI in breast MRI. For each article identified, the surname of the first author, year of publication, journal of publication, Web of Science Core Collection journal category, country of affiliation of the first author, study design, dataset, study aim(s), AI methods used, and, when available, diagnostic performance were recorded...
January 2, 2019: AJR. American Journal of Roentgenology
Emmanuel Carrodeguas, Ronilda Lacson, Whitney Swanson, Ramin Khorasani
PURPOSE: The aims of this study were to assess follow-up recommendations in radiology reports, develop and assess traditional machine learning (TML) and deep learning (DL) models in identifying follow-up, and benchmark them against a natural language processing (NLP) system. METHODS: This HIPAA-compliant, institutional review board-approved study was performed at an academic medical center generating >500,000 radiology reports annually. One thousand randomly selected ultrasound, radiography, CT, and MRI reports generated in 2016 were manually reviewed and annotated for follow-up recommendations...
December 29, 2018: Journal of the American College of Radiology: JACR
Kenji Karako, Yu Chen, Wei Tang
Neural networks have garnered attention over the past few years. A neural network is a typical model of machine learning that is used to identify visual patterns. Neural networks are used to solve a wide variety of problems, including image recognition problems and time series prediction problems. In addition, neural networks have been applied to medicine over the past few years. This paper classifies the ways in which neural networks have been applied to medicine based on the type of data used to train those networks...
December 17, 2018: Bioscience Trends
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
Paul J Chang
No abstract text is available yet for this article.
December 11, 2018: Radiology
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
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