K-t GRAPPA-accelerated 4D flow MRI of liver hemodynamics: influence of different acceleration factors on qualitative and quantitative assessment of blood flow.

OBJECTIVE We sought to evaluate the feasibility of k-t parallel imaging for accelerated 4D flow MRI in the hepatic vascular system by investigating the impact of different acceleration factors. MATERIALS AND METHODS k-t GRAPPA accelerated 4D flow MRI of the liver vasculature was evaluated in 16 healthy volunteers at 3T with acceleration factors R = 3, R = 5, and R = 8 (2.0 × 2.5 × 2.4 mm(3), TR = 82 ms), and R = 5 (TR = 41 ms); GRAPPA R = 2 was used as the reference standard. Qualitative flow analysis included grading of 3D streamlines and time-resolved particle traces. Quantitative evaluation assessed velocities, net flow, and wall shear stress (WSS). RESULTS Significant scan time savings were realized for all acceleration factors compared to standard GRAPPA R = 2 (21-71 %) (p < 0.001). Quantification of velocities and net flow offered similar results between k-t GRAPPA R = 3 and R = 5 compared to standard GRAPPA R = 2. Significantly increased leakage artifacts and noise were seen between standard GRAPPA R = 2 and k-t GRAPPA R = 8 (p < 0.001) with significant underestimation of peak velocities and WSS of up to 31 % in the hepatic arterial system (p <0.05). WSS was significantly underestimated up to 13 % in all vessels of the portal venous system for k-t GRAPPA R = 5, while significantly higher values were observed for the same acceleration with higher temporal resolution in two veins (p < 0.05). CONCLUSION k-t acceleration of 4D flow MRI is feasible for liver hemodynamic assessment with acceleration factors R = 3 and R = 5 resulting in a scan time reduction of at least 40 % with similar quantitation of liver hemodynamics compared with GRAPPA R = 2.

Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge

Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

Detection of the pan neuronal marker PGP9.5 by immuno-histochemistry and quantitative PCR in eutopic endometrium from women with and without endometriosis.

PURPOSE To assess endometrial gene as well as protein expression of neuroendocrine and supposedly endometriosis-associated product PGP9.5 and pain symptoms in women with endometriosis and controls undergoing laparoscopy, using molecular biological and immuno-histochemical approaches in the same patients. METHODS Biopsy of eutopic endometrium from 29 patients by sharp curettage, and preparation of paraffin blocks. Determination of PGP9.5 gene expression and protein abundance using qPCR and immuno-histochemistry. RESULTS qPCR; The PGP9.5 mRNA expression level between women with (N = 16) and without (N = 13) endometriosis was not different, regardless of pain symptoms or menstrual cycle phase. PGP9.5 expression was higher in women who reported pain compared to those who did not; however, this association was not statistically significant. The expression of PGP9.5 mRNA was higher in women with endometriosis and pain during the proliferative than in the secretory phase (P = 0.03). Furthermore, in the first half of the cycle, the abundance of the PGP9.5 transcript was also significantly higher in endometriosis patients compared to those without (P = 0.03). Immuno-histochemistry; Thirteen of the 16 endometriosis patients showed positive PGP9.5 immuno-reactivity in the endometrium, whereas no such signal was observed in women without endometriosis. The absolute number of nerve fibres per mm(2) in women with endometriosis was similar, regardless of the pain symptoms. CONCLUSIONS PGP9.5 mRNA expression is increased in the proliferative phase of endometriotic women with pain. The presence of nerve fibres was demonstrated by a PGP9.5 protein signal in immuno-histochemistry and restricted to patients with endometriosis. Based on these results, however, there did not appear to be a direct association between the gene expression and protein abundance in women with and without endometriosis or those that experienced pain.

Itineraries of pottery: a mixed methods approach to mobility

Neolithic wetland sites in the Swiss Plateau provide an extraordinary database for the study of mobilities, entanglements and transformations in material culture. Based on dendrochronologically dated settlements between 3900 and 3500 BC, two regional pottery styles and their local variations are well known, Pfyn and Cortaillod. The vessels share the same habitus and were made of local raw materials. However, some vessels specific to other pottery styles are also present in the sites. By focusing on itineraries of vessels and shifts in pottery knowledge, their appropriation in different contexts and the resulting material entanglements, we want to approach the multiple regimes of mobility: At Lake Constance - known for Pfyn pottery - specific Michelsberg vessels like tulip beakers and lugged jars occur in small numbers. These travelling objects were produced with exogenous raw materials and transported to the sites from Southern Germany. At Concise (Lake Neuchâtel) besides the local Cortaillod pottery the whole repertoire of NMB pottery, characteristic for Eastern France, was also produced. Further cases from the same space-time frame point to other regimes of mobility. In our two PhD-projects we compare pottery practices - styles, techniques, raw materials - from over 20 key sites in the region. Based on Bourdieu’s reflexive anthropology, we apply different qualitative and quantitative archaeological and archaeometrical methods, thus striving for a deeper understanding of habitus and the transformative potential of moving people, objects and ideas on local and regional scales and related social contexts.

Reliability of Quantitative Sensory Tests in a Low Back Pain Population.

BACKGROUND AND OBJECTIVES Reliability is an essential condition for using quantitative sensory tests (QSTs) in research and clinical practice, but information on reliability in patients with chronic pain is sparse. The aim of this study was to evaluate the reliability of different QST in patients with chronic low back pain. METHODS Eighty-nine patients with chronic low back pain participated in 2 identical experimental sessions, separated by at least 7 days. The following parameters were recorded: pressure pain detection and tolerance thresholds at the toe, electrical pain thresholds to single and repeated stimulation, heat pain detection and tolerance thresholds at the arm and leg, cold pain detection threshold at the arm and leg, and conditioned pain modulation using the cold pressor test.Reliability was analyzed using the coefficient of variation, the coefficient of repeatability, and the intraclass correlation coefficient. It was judged as acceptable or not based primarily on the analysis of the coefficient of repeatability. RESULTS The reliability of most tests was acceptable. Exceptions were cold pain detection thresholds at the leg and arm. CONCLUSIONS Most QST measurements have acceptable reliability in patients with chronic low back pain.

Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images.

PURPOSE Quantification of retinal layers using automated segmentation of optical coherence tomography (OCT) images allows for longitudinal studies of retinal and neurological disorders in mice. The purpose of this study was to compare the performance of automated retinal layer segmentation algorithms with data from manual segmentation in mice using the Spectralis OCT. METHODS Spectral domain OCT images from 55 mice from three different mouse strains were analyzed in total. The OCT scans from 22 C57Bl/6, 22 BALBc, and 11 C3A.Cg-Pde6b(+)Prph2(Rd2) /J mice were automatically segmented using three commercially available automated retinal segmentation algorithms and compared to manual segmentation. RESULTS Fully automated segmentation performed well in mice and showed coefficients of variation (CV) of below 5% for the total retinal volume. However, all three automated segmentation algorithms yielded much thicker total retinal thickness values compared to manual segmentation data (P < 0.0001) due to segmentation errors in the basement membrane. CONCLUSIONS Whereas the automated retinal segmentation algorithms performed well for the inner layers, the retinal pigmentation epithelium (RPE) was delineated within the sclera, leading to consistently thicker measurements of the photoreceptor layer and the total retina. TRANSLATIONAL RELEVANCE The introduction of spectral domain OCT allows for accurate imaging of the mouse retina. Exact quantification of retinal layer thicknesses in mice is important to study layers of interest under various pathological conditions.

Clinical Use of Quantitative Computed Tomography–Based Advanced Techniques in the Management of Osteoporosis in Adults: the 2015 ISCD Official Positions—Part III

The International Society for Clinical Densitometry (ISCD) has developed new official positions for the clinical use of computed tomography (CT) scans acquired without a calibration phantom, for example, CT scans obtained for other diagnosis such as colonography. This also addresses techniques suggested for opportunistic screening of osteoporosis. The ISCD task force for quantitative CT reviewed the evidence for clinical applications of these new techniques and presented a report with recommendations at the 2015 ISCD Position Development Conference. Here we discuss the agreed upon ISCD official positions with supporting medical evidence, rationale, controversy, and suggestions for further study. Advanced techniques summarized as statistical parameter mapping methods were also reviewed. Their future use is promising but the clinical application is premature. The clinical use of QCT of the hip is addressed in part I and of finite element analysis of the hip and spine in part II.

Detection of Malnutrition in Patients Undergoing Maintenance Haemodialysis: A Quantitative Data Analysis on 12 Parameters

Background Protein-energy-malnutrition (PEM) is common in people with end stage kidney disease (ESKD) undergoing maintenance haemodialysis (MHD) and correlates strongly with mortality. To this day, there is no gold standard for detecting PEM in patients on MHD. Aim of Study The aim of this study was to evaluate if Nutritional Risk Screening 2002 (NRS-2002), handgrip strength measurement, mid-upper arm muscle area (MUAMA), triceps skin fold measurement (TSF), serum albumin, normalised protein catabolic rate (nPCR), Kt/V and eKt/V, dry body weight, body mass index (BMI), age and time since start on MHD are relevant for assessing PEM in patients on MHD. Methods The predictive value of the selected parameters on mortality and mortality or weight loss of more than 5% was assessed. Quantitative data analysis of the 12 parameters in the same patients on MHD in autumn 2009 (n = 64) and spring 2011 (n = 40) with paired statistical analysis and multivariate logistic regression analysis was performed. Results Paired data analysis showed significant reduction of dry body weight, BMI and nPCR. Kt/Vtot did not change, eKt/v and hand grip strength measurements were significantly higher in spring 2011. No changes were detected in TSF, serum albumin, NRS-2002 and MUAMA. Serum albumin was shown to be the only predictor of death and of the combined endpoint “death or weight loss of more than 5%”. Conclusion We now screen patients biannually for serum albumin, nPCR, Kt/V, handgrip measurement of the shunt-free arm, dry body weight, age and time since initiation of MHD.

Quantitative multi-slice computed tomography assessment of the mitral valvular complex for transcatheter mitral valve interventions part 1: systematic measurement methodology and inter-observer variability

AIMS Transcatheter mitral valve replacement (TMVR) is an emerging technology with the potential to treat patients with severe mitral regurgitation at excessive risk for surgical mitral valve surgery. Multimodal imaging of the mitral valvular complex and surrounding structures will be an important component for patient selection for TMVR. Our aim was to describe and evaluate a systematic multi-slice computed tomography (MSCT) image analysis methodology that provides measurements relevant for transcatheter mitral valve replacement. METHODS AND RESULTS A systematic step-by-step measurement methodology is described for structures of the mitral valvular complex including: the mitral valve annulus, left ventricle, left atrium, papillary muscles and left ventricular outflow tract. To evaluate reproducibility, two observers applied this methodology to a retrospective series of 49 cardiac MSCT scans in patients with heart failure and significant mitral regurgitation. For each of 25 geometrical metrics, we evaluated inter-observer difference and intra-class correlation. The inter-observer difference was below 10% and the intra-class correlation was above 0.81 for measurements of critical importance in the sizing of TMVR devices: the mitral valve annulus diameters, area, perimeter, the inter-trigone distance, and the aorto-mitral angle. CONCLUSIONS MSCT can provide measurements that are important for patient selection and sizing of TMVR devices. These measurements have excellent inter-observer reproducibility in patients with functional mitral regurgitation.

BPA qualtitative and quantitative assessment associated with orthodontic bonding in vivo

OBJECTIVE To assess the in vivo amount of BPA released from a visible light-cured orthodontic adhesive, immediately after bracket bonding. METHODS 20 orthodontic patients were recruited after obtaining informed consent. All patients received 24 orthodontic brackets in both dental arches. In Group A (11 patients), 25 ml of tap water were used for mouth rinsing, whereas in Group B (9 patients) a simulated mouth rinse formulation was used: a mixture of 20 ml de-ionized water plus 5 ml absolute ethanol. Rinsing solutions were collected before, immediately after placing the orthodontic appliances and after washing out the oral cavity and were then stored in glass tubes. Rinsing was performed in a single phase for 60s with the entire volume of each liquid. The BPA analysis was performed by gas chromatography-mass spectrometry. RESULTS An increase in BPA concentration immediately after the 1st post-bonding rinse was observed, for both rinsing media, which was reduced after the 2nd post-bonding rinse. Water exhibited higher levels of BPA concentration than water/ethanol after 1st and 2nd post-bonding rinses. Two-way mixed Repeated Measures ANOVA showed that the primary null hypothesis declaring mean BPA concentration to be equal across rinsing medium and rinsing status was rejected (p-value <0.001). The main effects of the rinsing medium and status, as well as their interaction were found to be statistically significant (p-values 0.048, <0.001 and 0.011 respectively). SIGNIFICANCE A significant pattern of increase of BPA concentration, followed by a decrease that reached the initial values was observed. The amount of BPA was relatively low and far below the reference limits of tolerable daily intake.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Identification of genes associated with quantitative traits involved in cardiovascular disease and lipoprotein metabolism

Cardiovascular disease (CVD) is a threat to public health. It has been reported to be the leading cause of death in United States. The invention of next generation sequencing (NGS) technology has revolutionized the biomedical research. To investigate NGS data of CVD related quantitative traits would contribute to address the unknown etiology and disease mechanism of CVD. NHLBI's Exome Sequencing Project (ESP) contains CVD related phenotypes and their associated NGS exomes sequence data. Initially, a subset of next generation sequencing data consisting of 13 CVD-related quantitative traits was investigated. Only 6 traits, systolic blood pressure (SBP), diastolic blood pressure (DBP), height, platelet counts, waist circumference, and weight, were analyzed by functional linear model (FLM) and 7 currently existing methods. FLM outperformed all currently existing methods by identifying the highest number of significant genes and had identified 96, 139, 756, 1162, 1106, and 298 genes associated with SBP, DBP, Height, Platelet, Waist, and Weight respectively. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Complications of misoprostol and other abortion induction methods in the developing world: A systematic review

OBJECTIVE: To systematically review published literature to examine the complications associated with the use of misoprostol and compare these complications to those associated with other forms of abortion induction. ^ DATA SOURCES: Studies were identified through searches of medical literature databases including Medline (Ovid), PubMed (NLM), LILACS, sciELO, and AIM (AFRO), and review of references of relevant articles. ^ STUDY SELECTION AND METHODS: A descriptive systematic review that included studies reported in English and published before December 2012. Eligibility criteria included: misoprostol (with or without other methods) and any other method of abortion in a developing country, as well as quantitative data on the complication of each method. The following is information extracted from each study: author/year, country/city, study design/study sample, age range, setting of data collection, sample size, the method of abortion induction, the number of cases for each method, and the percentage of complications with each method. RESULTS: A total of 4 studies were identified (all in Latin America) describing post-abortion complications of misoprostol and other methods in countries where abortion is generally considered unsafe and/or illegal. The four studies reported on a range of complications including: bleeding, infection, incomplete abortion, intense pelvic pain, uterine perforation, headache, diarrhea, nausea, mechanical lesions, and systemic collapse. The most prevalent complications of misoprostol-induced abortion reported were: bleeding (7-82%), incomplete abortion (33-70%), and infection (0.8-67%). The prevalence of these complications reported from other abortion methods include: bleeding (16-25%), incomplete abortion (15-82%), and infection (13-50%). ^ CONCLUSION: The literature identified by this systematic review is inadequate for determining the complications of misoprostol used in unsafe settings. Abortion is considered an illicit behavior in these countries, therefore making it difficult to investigate the details needed to conduct a study on abortion complications. Given the differences between the reviewed studies as well as a variety of study limitations, it is not possible to draw firm conclusions about the rates of specific-abortion related complications.^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^