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Machine learning and ultrasound

Adrian D Haimovich, Zachary Lehmann, R Andrew Taylor
We describe a new graphical user interface-based application, US-Pro, designed to enable customized, high-throughput ultrasound video anonymization and dynamic cropping before output to video or high-efficiency disk storage. This application is distributed in a Docker container environment, which supports facile software installation on the most commonly used operating systems, as well as local processing of data sets, precluding the external transfer of electronic protected health information. The US-Pro application will facilitate the reproducible production of large-scale ultrasound video data sets for varied applications, including machine-learning analysis, educational distribution, and quality assurance review...
February 4, 2019: Journal of Ultrasound in Medicine: Official Journal of the American Institute of Ultrasound in Medicine
Narendra N Khanna, Ankush D Jamthikar, Deep Gupta, Matteo Piga, Luca Saba, Carlo Carcassi, Argiris A Giannopoulos, Andrew Nicolaides, John R Laird, Harman S Suri, Sophie Mavrogeni, A D Protogerou, Petros Sfikakis, George D Kitas, Jasjit S Suri
PURPOSE OF THE REVIEW: Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor-based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators...
January 25, 2019: Current Atherosclerosis Reports
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
Kaya Kuru, Darren Ansell, Martin Jones, Christian De Goede, Peter Leather
Unsatisfactory cure rates for the treatment of nocturnal enuresis (NE), i.e. bed-wetting, have led to the need to explore alternative modalities. New treatment methods that focus on preventing enuretic episodes by means of a pre-void alerting system could improve outcomes for children with NE in many aspects. No such technology exists currently to monitor the bladder to alarm before bed-wetting. The aim of this study is to carry out the feasibility of building, refining and evaluating a new, safe, comfortable and non-invasive wearable autonomous intelligent electronic device to monitor the bladder using a single-element low-powered low-frequency ultrasound with the help of Machine Learning techniques and to treat NE by warning the patient at the pre-void stage, enhancing quality of life for these children starting from the first use...
December 26, 2018: Medical & Biological Engineering & Computing
An Tang, François Destrempes, Siavash Kazemirad, Julian Garcia-Duitama, Bich N Nguyen, Guy Cloutier
OBJECTIVES: To develop a machine learning model based on quantitative ultrasound (QUS) parameters to improve classification of steatohepatitis with shear wave elastography in rats by using histopathology scoring as the reference standard. METHODS: This study received approval from the institutional animal care committee. Sixty male Sprague-Dawley rats were either fed a standard chow or a methionine- and choline-deficient diet. Ultrasound-based radiofrequency images were recorded in vivo to generate QUS and elastography maps...
December 17, 2018: European Radiology
Yuan-Yuan Wang, Chen-Hui Qiu, Jun Jiang, Shun-Ren Xia
The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images. First, a novel morphologic feature describing the relative position of each structure relative to the MA border, called RPES for short, is proposed...
December 16, 2018: Ultrasonic Imaging
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
Takanori Masuda, Takeshi Nakaura, Yoshinori Funama, Tomokazu Okimoto, Tomoyasu Sato, Toru Higaki, Noritaka Noda, Naoyuki Imada, Yasutaka Baba, Kazuo Awai
BACKGROUND: To determine whether machine learning with histogram analysis of coronary CT angiography (CCTA) yields higher diagnostic performance for coronary plaque characterization than the conventional cut-off method using the median CT number. METHODS: We included 78 patients with 78 coronary plaques who had undergone CCTA and integrated backscatter intravascular ultrasound (IB-IVUS) studies. IB-IVUS diagnosed 32 as fibrous- and 46 as fatty or fibro-fatty plaques...
October 21, 2018: Journal of Cardiovascular Computed Tomography
Lucas F M Rodrigues, Fábio C Cruz, Moisés A Oliveira, Eduardo F Simas Filho, Maria C S Albuquerque, Ivan C Silva, Cláudia T T Farias
Ultrasound nondestructive testing is commonly applied in industry to guarantee structural integrity. HP steel pyrolysis furnaces are used in petrochemical industry for lightweight hydrocarbon production. HP steel chromium content may be reduced in high-temperatures due to carbon diffusion. This characterizes the carburization phenomenon, which modifies magnetic properties, reduces mechanical resistance and may lead to structural rupture. For safe operation it is required to frequently determine carburizing level in pyrolysis furnace pipes...
October 10, 2018: Ultrasonics
Prashant Parulekar, Ed Neil-Gallacher, Alex Harrison
Acute kidney injury is common in critically ill patients, with ultrasound recommended to exclude renal tract obstruction. Intensive care unit clinicians are skilled in acquiring and interpreting ultrasound examinations. Intensive Care Medicine Trainees wish to learn renal tract ultrasound. We sought to demonstrate that intensive care unit clinicians can competently perform renal tract ultrasound on critically ill patients. Thirty patients with acute kidney injury were scanned by two intensive care unit physicians using a standard intensive care unit ultrasound machine...
November 2018: Journal of the Intensive Care Society
Q Zheng, S L Furth, G E Tasian, Y Fan
INTRODUCTION: Anatomic characteristics of kidneys derived from ultrasound images are potential biomarkers of children with congenital abnormalities of the kidney and urinary tract (CAKUT), but current methods are limited by the lack of automated processes that accurately classify diseased and normal kidneys. OBJECTIVE: The objective of the study was to evaluate the diagnostic performance of deep transfer learning techniques to classify kidneys of normal children and those with CAKUT...
October 31, 2018: Journal of Pediatric Urology
Hasmila A Omar, Arijit Patra, Joao S Domingos, Paul Leeson, Alison J Noblel
Compared to other modalities such as computed tomography or magnetic resonance imaging, the appearance of ultrasound images is highly dependent on the expertise of the sonographer or clinician making the image acquisition, as well as the machine used, making it a challenge to analyze due to the frequent presence of artefacts, missing boundaries, attenuation, shadows, and speckle. In addition, manual contouring of the epicardial and endocardial walls exhibits large inconsistencies and variations as it is strongly dependent on the sonographer's training and expertise...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Fanqing Meng, Jun Shi, Bangming Gong, Qi Zhang, Lehang Guo, Dan Wang, Huixiong Xu
Contrast-enhanced ultrasound (CEUS) is a valuable imaging modality for diagnosis of liver cancers. However, the complexity of CEUS-based diagnosis limits its wide application, and the B-mode ultrasound (BUS) is still the most popular diagnosis modality in clinical practice. In order to promote BUS-based computer-aided diagnosis (CAD) for liver cancers, we propose a learning using privileged information (LUPI) based CAD with BUS as the diagnosis modality and CEUS as PI. Particularly, the multimodal restricted Boltzmann machine (MRBM) works as a LUPI paradigm...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Tae Joon Jun, Soo-Jin Kang, June-Goo Lee, Jihoon Kweon, Wonjun Na, Daeyoun Kang, Dohyeun Kim, Daeyoung Kim, Young-Hak Kim
Acute coronary syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture. In this paper, we propose methods to classify TCFA using various machine learning classifiers including feed-forward neural network (FNN), K-nearest neighbor (KNN), random forest (RF), and convolutional neural network (CNN) to figure out a classifier that shows optimal TCFA classification accuracy...
November 14, 2018: Medical & Biological Engineering & Computing
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
Ray O Bahado-Singh, Jiri Sonek, David McKenna, David Cool, Buket Aydas, Onur Turkoglu, Trent Bjorndahl, Rupasri Mandal, David Wishart, Perry Friedman, Stewart F Graham, Ali Yilmaz
OBJECTIVE: To evaluate the utility of Artificial Intelligence i.e. Deep Learning (DL) and other machine learning techniques for the prediction of important pregnancy outcomes in asymptomatic short cervical length (CL). METHOD: The amniotic fluid (AF) had been obtained from second trimester patients with asymptomatic women with short cervical length (<15 mm). CL, funneling and the presence of AF 'sludge' were assessed in all cases. Combined targeted metabolomic and proteomic analysis of amniotic fluid (AF) was performed...
October 31, 2018: Ultrasound in Obstetrics & Gynecology
Yuzhou Hu, Yi Guo, Yuanyuan Wang, Jinhua Yu, Jiawei Li, Shichong Zhou, Cai Chang
PURPOSE: Due to the low contrast, blurry boundaries, and large amount of shadows in breast ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep learning provides a solution to this problem, since it can effectively extract representative features from lesions and the background in BUS images. METHODS: A novel automatic tumor segmentation method is proposed by combining a dilated fully convolutional network (DFCN) with a phase-based active contour (PBAC) model...
October 29, 2018: Medical Physics
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
Xavier Benoit D'Journo
Epidemiology of esophageal cancer and esophagogastric junction (EGJ) has deeply changed for the past two decades with a dramatically increase of adenocarcinoma whereas squamous cell carcinoma (SCC) has slowly decreased. Moreover, the two histological types differ in a number of features including risks factors, tumor location, tumor biology and outcomes. In acknowledgement of these differences, the newest 8th edition of the American Joint Committee on Cancer (AJCC) tumor, node and metastasis (TNM) staging classification of epithelial cancers of the esophagus and EGJ has refined this histology-specific disease stage with incorporation of new anatomic and non-anatomic categories...
August 2018: Journal of Thoracic Disease
Prabal Poudel, Alfredo Illanes, Debdoot Sheet, Michael Friebe
The thyroid is one of the largest endocrine glands in the human body, which is involved in several body mechanisms like controlling protein synthesis and the body's sensitivity to other hormones and use of energy sources. Hence, it is of prime importance to track the shape and size of thyroid over time in order to evaluate its state. Thyroid segmentation and volume computation are important tools that can be used for thyroid state tracking assessment. Most of the proposed approaches are not automatic and require long time to correctly segment the thyroid...
2018: Journal of Healthcare Engineering
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