A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Effectiveness of Machine Learning Classifiers for Cataract Screening

Cataract is the leading cause of blindness and vision loss globally. The implementation of artificial intelligence (AI) in the healthcare industry has been on the rise in the past few decades and machine learning (ML) classifiers have shown to be able to diagnose patients with cataracts. A systematic review and meta-analysis were conducted to assess the diagnostic accuracy of these ML classifiers for cataracts currently published in the literature. Retrieved from nine articles, the pooled sensitivity was 94.8% and the specificity was 96.0% for adult cataracts. Additionally, an economic analysis was conducted to explore the cost-effectiveness of implementing ML to diagnostic eye camps in rural Nepal compared to traditional diagnostic eye camps. There was a total of 22,805 patients included in the decision tree, and the ML-based eye camp was able to identify 31 additional cases of cataracts, and 2546 additional cases of non-cataract

Technology utilization program report, 1974

The adaptation of various technological innovations from the NASA space program to industrial and domestic applications is summarized

Visual Impairment and Blindness

Blindness and vision impairment affect at least 2.2 billion people worldwide with most individuals having a preventable vision impairment. The majority of people with vision impairment are older than 50 years, however, vision loss can affect people of all ages. Reduced eyesight can have major and long-lasting effects on all aspects of life, including daily personal activities, interacting with the community, school and work opportunities, and the ability to access public services. This book provides an overview of the effects of blindness and visual impairment in the context of the most common causes of blindness in older adults as well as children, including retinal disorders, cataracts, glaucoma, and macular or corneal degeneration

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.ope

Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study

Objectives:To analyze the effect of macular choroidal thickness (MCT) and peripapillary choroidal thickness (PPCT) on the classification of obese and healthy children by comparing the performance of the random forest (RF), support vector machine (SVM), and multilayer perceptrons (MLP) algorithms.Materials and Methods:Fifty-nine obese children and 35 healthy children aged 6 to 15 years were studied in this prospective comparative study using optical coherence tomography. MCT and PPCT were measured at distances of 500 μm, 1,000 μm, and 1,500 μm from the fovea and optic disc. Three different feature selection algorithms were used to determine the most prominent features of all extracted features. The classification efficiency of the extracted features was analyzed using the RF, SVM, and MLP algorithms, demonstrating their efficacy for distinguishing obese from healthy children. The precision and reliability of measurements were assessed using kappa analysis.Results:The correlation feature selection algorithm produced the most successful classification results among the different feature selection methods. The most prominent features for distinguishing the obese and healthy groups from each other were PPCT temporal 500 μm, PPCT temporal 1,500 μm, PPCT nasal 1,500 μm, PPCT inferior 1,500 μm, and subfoveal MCT. The classification rates for the RF, SVM, and MLP algorithms were 98.6%, 96.8%, and 89%, respectively.Conclusion:Obesity has an effect on the choroidal thicknesses of children, particularly in the subfoveal region and the outer semi-circle at 1,500 μm from the optic disc head. Both the RF and SVM algorithms are effective and accurate at classifying obese and healthy children

Medical Devices Competitiveness and Impact on Public Health Expenditure

This study provides an analytical overview of the state of the European Union medical device industry. The medical device industry sector encompasses an extremely large variety of products and technologies. It covers hundreds of thousands of products that range from more traditional products, such as bandages or syringes, to sophisticated devices that incorporate bioinformatics, nanotechnology and engineered cells. These are designed for use by practitioners, patients and healthy individuals in a variety of settings: hospitals, surgeries and private homes. Besides being a vital and innovative industry, medical devices are a key component of healthcare systems and represent, together with pharmaceuticals, the bulk of ‘medical technology’. The analysis of the sector must therefore investigate medical devices as an industry – an innovative contributor to the economy – as well its key input to healthcare systems. The following aspects are taken into account: a) the impact of innovation in medical devices on health costs and expenditure; b) the innovativeness of the European medical device industry; c) the competitiveness of the European medical device industry as compared to that of the United States and Japan.healthcare expenditure; medical devices; competitiveness; innovation

NASA technology utilization applications

The work is reported from September 1972 through August 1973 by the Technology Applications Group of the Science Communication Division (SCD), formerly the Biological Sciences Communication Project (BSCP) in the Department of Medical and Public Affairs of the George Washington University. The work was supportive of many aspects of the NASA Technology Utilization program but in particular those dealing with Biomedical and Technology Application Teams, Applications Engineering projects, new technology reporting and documentation and transfer activities. Of particular interest are detailed reports on the progress of various hardware projects, and suggestions and criteria for the evaluation of candidate hardware projects. Finally some observations about the future expansion of the TU program are offered

Department of Veterinary and Biomedical Sciences 2007 Annual Report

The first class of twenty· five Nebraska students began their DVM degree program at the University of Nebraska-Lincoln. The new program provides for students from Nebraska to complete their first two years of the professional school at UNL. The second two years of training will be completed at the College of Veterinary Medicine, Iowa State University. Under the agreement, the students will pay Iowa State resident tuition rates all four years. To prepare for the new program, an anatomy teaching laboratory, classroom and microbiology laboratory were develop by renovating space in the Animal Science Complex. New faculty members hired to teach the courses that make up the first two years of the professional curriculum include Dr. Jennifer Wood and Dr. Tom Burkey, veterinary physiology; Dr. John Kammermann, veterinary anatomy; Dr. Jay Reddy, veterinary immunology; Dr. Gary Pickard, neurobiology; Dr. Doug Hostetler, veterinary surgery. Faculty searches are underway for a veterinary parasitologist, veterinary pathologist and veterinary epidemiologist. In addition to these positions, Dr. Jeff Ondrak join the faculty at GPVEC as a Beef Cattle Clinical Veterinarian. The Department completed its CSREES and UNL S·year review during the year and the feedback from the review team was very favorable. The department is encourage to maintain its research focus in the area of infectious diseases and biomedical research and commented on the positive addition of the 2 + 2 Program and how it complemented the program. The Veterinary Diagnostic Center prepared for its five year AA VLD accreditation visit. The report was prepared and the site visit is schedule for early January 2008. We are concerned regarding the crowded conditions within the laboratories. In addition, this will be the first time the accreditation process will focus on Standard Operating Procedures within the laboratory. The undergraduate program has had steady growth since a low point in 2003. Much of this growth is credited to the creation of the Professional Program in Veterinary Medicine. The graduate program remains solid, as does the extramural research funding. To strengthen our extension program, Dr. Richard Randle was hired to focus on beef cattle extension activities. Additional activities include discussion with the Department of Animal Science and the Dean\u27s Office to strengthen collaborative efforts in student recruitment and clarify some of the confusion related to Pre-vet students