Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.Comment: Presented at: 2015 IEEE International Symposium on Biomedical Imaging, April 16-19, 2015, New York Marriott at Brooklyn Bridge, NY, US

Sustainability of the Global Water Supply: An Exploratory Study of International Managers\u27 Perspectives

Water risk, both in terms of the risk to human life related to such threats as water scarcity and the risk to business related to such challenges as mismanagement of a critical resource, is a topic of increasing interest and importance to policymakers worldwide. The problems associated with water risk are being tackled by international development organizations such as the United Nations and the Organization for Economic Co-operation and Development; by international business organizations such as the World Business Council for Sustainable Development and the World Resources Institute; and by international aid organizations such as Water.org and countless corporate foundations. There is no shortage of brainpower, passionate commitment, or money spent on finding solutions to a growing problem. However, the problems continue. This research effort’s premise is that solutions to problems around the sustainability of the water supply remain elusive because such considerations have not yet become part of the day-to-day problem-solving routine of managers across the hierarchy of business organizations. In this study, managers in Brazil and South Korea were surveyed on their perspectives on the factors in their external environments that held promise for improving the sustainability of the world’s water supply. Our analysis identifies stakeholders in the external environment with whom managers might build effective coalitions for addressing this critical issue

Health related quality of life in pregeriatric patients with chronic diseases at urban, public supported clinics

BACKGROUND: Understanding health-related quality of life (HRQOL) leads to more effective and focused healthcare. America's growing health disparities makes it is increasingly necessary to understand the HRQOL of pregeriatric individuals who are now 55–64 years old, i.e. before they are eligible for federally mandated health care at age 65. Our study measured the self-perceived HRQOL of pregeriatric, poor patients with multiple chronic diseases treated at 2 public clinics. METHODS: Consecutive patients aged 55–64 years, many with multiple chronic diseases, responded in an interview to the 36-Item Short-Form Health Survey (SF–36) as a general measure of HRQOL during a regular visit to one of two university-staffed urban public clinics. RESULTS: The perceived physical and mental functioning of 316 pregeriatric patients was tabulated from SF–36 scores to yield their HRQOL. Their scores were statistically significantly lower than those of the general US pregeriatric population and lower than averages for US patients with multiple chronic diseases. All eight subscale scores of SF–36 were 16% to 36% lower compared with the averages of the general US pregeriatric population. Further, as the number of chronic diseases increased, the lower was the HRQOL. Lower physical and mental scores were associated with a lower income, unemployment, and higher numbers of multiple chronic diseases. CONCLUSION: Chronic diseases have a powerful negative impact on perceived mental and physical functioning in pregeriatric patients. HRQOL information can assist health care providers to gain a more complete picture of their pregeriatric patients' health

Interleaved text/image Deep Mining on a large-scale radiology database

Despite tremendous progress in computer vision, effec-tive learning on very large-scale (> 100K patients) medi-cal image databases has been vastly hindered. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital’s picture archiv-ing and communication system. Instead of using full 3D medical volumes, we focus on a collection of representa-tive ~216K 2D key images/slices (selected by clinicians for diagnostic reference) with text-driven scalar and vector la-bels. Our system interleaves between unsupervised learn-ing (e.g., latent Dirichlet allocation, recurrent neural net language models) on document- and sentence-level texts to generate semantic labels and supervised learning via deep convolutional neural networks (CNNs) to map from images to label spaces. Disease-related key words can be predicted for radiology images in a retrieval manner. We have demon-strated promising quantitative and qualitative results. The large-scale datasets of extracted key images and their cat-egorization, embedded vector labels and sentence descrip-tions can be harnessed to alleviate the deep learning “data-hungry ” obstacle in the medical domain

Patient survival after renal transplantation: IV. Impact of post-transplant diabetes

Patient survival after renal transplantation: IV. Impact of post-transplant diabetes.BackgroundThe development of de novo diabetes mellitus is a serious complication of kidney transplantation. This study examined the cardiovascular risk profile of patients with post-transplant diabetes (PTDM) and assessed the impact of PTDM on patient survival.MethodsThis analysis included 1811 adult, renal allograft recipients, transplanted in a single institution between 1983 and 1998. Patient survival was analyzed by univariable and multivariable Cox regression considering PTDM as a time dependent variable.ResultsAfter a follow-up period of 8.3 ± 4.5 years, 293 patients (20%) developed PTDM, 14% lost their graft, and 20% died. Compared to patients without DM (NoDM, N = 1186) patients with PTDM were significantly older (40 ± 14 vs. 48 ± 12 years, P < 0.001), heavier (76 ± 23 vs. 86 ± 25 kg, P < 0.001), and included more African Americans (18 vs. 28%, P = 0.001). In addition, the incidence of PTDM was significantly higher in patients who were transplanted after 1995 than prior to that year. In contrast, there were no significant differences between PTDM and patients who had DM before the transplant (DM; N = 332). Compared to NoDM, patients with PTDM had significantly higher total serum cholesterol and triglycerides (TG), higher systolic blood pressure and higher pulse pressure throughout the post-transplant period. Of interest, all of these abnormalities preceded the development of PTDM. Hypertriglyceridemia was particularly pronounced in PTDM and elevated TG levels correlated with the subsequent development of PTDM, independent of other risk factors (P = 0.001 by multivariate Cox). Compared to NoDM (16% mortality) a significantly higher percent of DM (31%, P < 0.001) and PTDM (22%, P = 0.005) patients died. By Cox regression, PTDM correlated with reduced patient survival (hazard ratio = 1.80, CI 1.35 to 2.41, P = 0.001), and that relationship was independent of other correlates of reduced survival that included: increasing age; transplant year; reduced serum albumin; and male sex.Conclusions: PTDM is associated with an unfavorable cardiovascular risk profile that precedes the development of hyperglycemia. PTDM is an independent predictor of reduced survival in renal allograft recipients

Endogenous Sex Steroid Hormones, Lipid Subfractions, and Ectopic Adiposity in Asian Indians

Background: Estradiol, testosterone (T), and sex hormone binding globulin (SHBG) levels are associated with lipid subfractions in men and women. Our objective was to determine if associations are independent from adipose tissue area among Asian Indians. Methods: We used data from 42 women and 57 Asian Indian men who did not use exogenous steroids or lipid-lowering medications. Lipoprotein subfractions including low-density lipoprotein cholesterol (LDL), very low-density lipoprotein cholesterol (VLDL), and intermediate density lipoprotein (IDL) were assessed by ion mobility spectrometry. Intra-abdominal adiposity was assessed by computed tomography. Multivariable regression models estimated the association between sex hormones with lipoprotein subfractions before and after adjustment for adiposity. Results: Among women, lower logSHBG levels were associated with smaller logLDL particle size and higher logtriglycerides, logVLDL, and logIDL, although these associations were attenuated with adjustment for visceral adiposity in particular. Among women, lower logSHBG levels was significantly associated with lower logmedium LDL and logsmall LDL concentrations even after consideration of visceral and hepatic adiposity and insulin resistance as represented by the homeostasis model assessment of insulin resistance (HOMA-IR). Among men, lower logSHBG was also associated with smaller logLDL peak diameter size and higher logtriglycerides and logVLDL, even after adjustment for HOMA-IR and adiposity. Relationships between sex steroids and lipid subfractions were not significant among women. Among men, higher total testosterone was associated with higher logHDL and logLDL particle size, and lower logtriglycerides and logVLDL, but these associations were partially attenuated with adjustment for adiposity and HOMA-IR. Conclusions: Among Asian Indians, SHBG is associated with more favorable lipid subfraction concentrations, independent of hepatic and visceral fat.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140166/1/met.2015.0063.pd

Discovery of Stable and Selective Antibody Mimetics from Combinatorial Libraries of Polyvalent, Loop-Functionalized Peptoid Nanosheets.

The ability of antibodies to bind a wide variety of analytes with high specificity and high affinity makes them ideal candidates for therapeutic and diagnostic applications. However, the poor stability and high production cost of antibodies have prompted exploration of a variety of synthetic materials capable of specific molecular recognition. Unfortunately, it remains a fundamental challenge to create a chemically diverse population of protein-like, folded synthetic nanostructures with defined molecular conformations in water. Here we report the synthesis and screening of combinatorial libraries of sequence-defined peptoid polymers engineered to fold into ordered, supramolecular nanosheets displaying a high spatial density of diverse, conformationally constrained peptoid loops on their surface. These polyvalent, loop-functionalized nanosheets were screened using a homogeneous Förster resonance energy transfer (FRET) assay for binding to a variety of protein targets. Peptoid sequences were identified that bound to the heptameric protein, anthrax protective antigen, with high avidity and selectivity. These nanosheets were shown to be resistant to proteolytic degradation, and the binding was shown to be dependent on the loop display density. This work demonstrates that key aspects of antibody structure and function-the creation of multivalent, combinatorial chemical diversity within a well-defined folded structure-can be realized with completely synthetic materials. This approach enables the rapid discovery of biomimetic affinity reagents that combine the durability of synthetic materials with the specificity of biomolecular materials