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At least 1,783 records · Page 99Linked to original sources
PubMed1993

Bayesian inference for model-based segmentation of computed radiographs of the hand.

We present a method for medical image understanding by computer that uses model-based, hierarchical Bayesian inference to accurately segment imaged anatomy. A first application is a prototype system that automatically segments and measures symptoms of arthridities in hand radiographs. This is potentially useful in radiological diagnosis and tracking of arthridities. Key steps of the model-based, Bayesian inference approach are: (1) prediction of imagery features from 3D models of anatomy, parameterized by population statistics, (2) local image feature extraction in predicted sub-regions, and (3) the use of a probabilistic calculus to accrue results of image processing and image feature matching procedures in support or denial of hypotheses about the imaged anatomy. The prototype system for hand radiograph analysis accurately segments normal and somewhat degenerated hand anatomy. Results are shown of the ability of the automated system to 'fail soft', recognizing when segmentation is inadequate for accurate measurement. This self evaluation capability improves reliability of measurements for potential clinical use.

Arthritis↗
PubMed2004

Probabilistic representation of the exposure of consumers to Clostridium botulinum neurotoxin in a minimally processed potato product.

We have examined the potential of a well-specified, minimally processed potato product as a vehicle for the exposure of consumers to Clostridium botulinum neurotoxin. The product is a relatively simple combination of raw potato flakes, flour, starch and other minor ingredients and has an extended lifetime under refrigeration conditions. A combination of information and data, from a variety of sources that includes the manufacturer, has shown that the product is particularly safe with respect to non-proteolytic C. botulinum hazards. The model concentrates on a simple end point, the toxicity of an individual retail unit of the product at the point of consumer preparation, which is related to an individual risk. The probabilistic analysis was built using Bayesian Belief Network (BBN) techniques.

Bayes Theorem↗
PubMed2006

Probabilistic encoding of vocalizations in macaque ventral lateral prefrontal cortex.

We examined strategies for classifying macaque vocalizations into their corresponding categories, as well as whether or not there was evidence that prefrontal auditory neurons were related to this process. We found that static estimates of the spectral and temporal contrasts of the calls were not effective features for discriminating among the call classes. A hidden Markov model (HMM), however, was more effective at discriminating among the call classes, reaching a performance of almost 75% correct. Finally, we found that the responses of prefrontal auditory neurons could be predicted more effectively as linear functions of the probabilistic output of the HMM than as linear functions of the spectral features of the calls. This provides evidence that, for call recognition, the macaque auditory system likely performs dynamic processing of vocalizations, and that prefrontal auditory neurons carry a signal related to the output of this processing.

Acoustic Stimulation↗
PubMed2005

An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours.

In this paper, a novel method for the boundary detection of human kidneys from three dimensional (3D) ultrasound (US) is proposed. The inherent difficulty of interpretation of such images, even by a trained expert, makes the problem unsuitable for classical methods. The method here proposed finds the kidney contours in each slice. It is a probabilistic Bayesian method. The prior defines a Markov field of deformations and imposes the restriction of contour smoothness. The likelihood function imposes a probabilistic behavior to the data, conditioned to the contour position. This second function, which is also Markov, uses an empirical model of distribution of the echographical data and a function of the gradient of the data. The model finally includes, as a volumetric extension of the prior, a term that forces smoothness along the depth coordinate. The experiments that have been carried out on echographies from real patients validate the model here proposed. A sensitivity analysis of the model parameters has also been carried out.

Bayes Theorem↗
PubMed2005

Unified segmentation.

A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.

Algorithms↗
PubMed2005

Management options to reduce exposure to methyl mercury through the consumption of fish and fishery products by the French population.

This paper presents an updated assessment of exposure in France to methyl mercury through the consumption of fish and fishery products, and proposes several management scenarios which could reduce this exposure through changes to fish contamination levels or fish consumption patterns. The exposure model was applied in line with previous methodological results [Tressou, J., Crépet, A., Bertail, P., Feinberg, M.H., Leblanc J.Ch., 2004a. Probabilistic exposure assessment to food chemicals based on extreme value theory: application to heavy metals from fish and sea products. Food Chem. Toxicol. 42, 1349-1358; Tressou, J., Leblanc, J.Ch., Feinberg, M., Bertail, P., 2004b. Statistical methodology to evaluate food exposure to a contaminant and influence of sanitary limits: application to ochratoxin A. Regul. Toxicol. Pharmacol. 40, 252-263] so as to obtain a realistic estimate of probability and confidence intervals (95% CI) concerning French consumers exposed to levels exceeding the revised fixed provisional tolerable weekly intake (PTWI) for methyl mercury of 1.6 microg/week/kg of body weight, established by the Joint FAO/WHO Expert Committee on Food Additives in 2003. The results showed that young children aged between 3 and 6 years old or 7 and 10 years old, and women of childbearing age were at the risk groups. With respect to these groups and according to the fish consumers patterns (consumers of predatory fish only or consumers of predatory and nonpredatory fish), the results suggested that strategies to diminish MeHg exposure by reducing the amount of predatory fish consumed would be more efficient in significantly decreasing the probability of exceeding the PTWI than the implementation of international standards.

Adolescent↗
PubMed2001

Ethical principles and pitfalls of genetic testing for dementia.

Progress in the genetics of dementing disorders and the availability of clinical tests for practicing physicians increase the need for a better understanding of multifaceted issues associated with genetic testing. The genetics of dementia is complex, and genetic testing is fraught with many ethical concerns. Genetic testing can be considered for patients with a family history suggestive of a single gene disorder as a cause of dementia. Testing of affected patients should be accompanied by competent genetic counseling that focuses on probabilistic implications for at-risk first-degree relatives. Predictive testing of at-risk asymptomatic patients should be modeled after presymptomatic testing for Huntington's disease. Testing using susceptibility genes has only a limited diagnostic value at present because potential improvement in diagnostic accuracy does not justify potentially negative consequences for first-degree relatives. Predictive testing of unaffected subjects using susceptibility genes is currently not recommended because individual risk cannot be quantified and there are no therapeutic interventions for dementia in presymptomatic patients.

Algorithms↗
PubMed2003

Hierarchical Bayesian inference in the visual cortex.

Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas.

Animals↗
PubMed1987

Computer aided diagnosis of jaundice. A comparison of two data bases.

A computer system for probabilistic diagnosis of jaundice was tested on a patient sample from a geographical area different from that for which it was first constructed. 144 consecutive patients with jaundice seen in two Stockholm hospitals were interviewed and examined to record a total of 82 indicants from history, demographic details, physical findings and laboratory tests. Data were compared with those of 319 jaundiced patients previously interviewed and examined at different London hospitals. It was found that disease incidences were different in the two patient samples. There were more patients with acute viral hepatitis, chronic active hepatitis and primary biliary cirrhosis in the London data base whereas the Stockholm data base included significantly more patients with Gilbert's syndrome and alcoholic cirrhosis. Indicant frequencies, standardised for disease incidence, differed with respect to age (Stockholm patients were on average six years older), time from onset of first symptom to hospital admission (Stockholm patients had on average a two-week shorter history of disease) and a number of symptoms such as nausea, vomiting, anorexia, weight loss, itching, pale stools and dark urine which were more frequent among the London patients. Differences in hospital admission policy was regarded as an important reason for the differences in indicant frequency. The results of probabilistic diagnosis were poor. Only 49% of the cases were correctly classified into twelve diagnostic groups. In particular the computer model was poor at separating different causes of malignant bile duct obstruction and at differentiating between malignant and benign bile duct obstruction. However, all cases of acute viral hepatitis were correctly classified and the computer model was 87% accurate in differentiating between medical and surgical jaundice. Reclassification of the 144 patients on their own data showed the computer system to be well calibrated and 97% of the cases were correctly classified according to this procedure. In conclusion, the computer system could not be directly transferred for use in a Swedish hospital but the results of reclassification were sufficiently encouraging to warrant prospective studies.

Adult↗
PubMed2005

Time precision of hippocampal CA3 neurons.

Time precision of generation of action potentials (AP) is limited by the electrical properties of neuronal membrane, by the probabilistic nature of synaptic transmission and by the membrane noise. In the software environment GENESIS 2.2, we constructed a multicompartmental model of a rat hippocampal neuron, made up of soma, dendritic tree and axonal initial segment (IS). In the model were implemented channel and thermal noise, corrupting the propagation of postsynaptic signal at the soma-dendritic (SD) membrane and the AP initiation at the IS. Various levels of synaptic redundancy, connecting the presynaptic neurons with the SD membrane by various numbers N of unreliable synapses of varying probability P of vesicle release, were modelled. During simulations, random spatio-temporal patterns of synaptic activity were presented to the neuron repeatedly, eliciting sequences of APs, which were further statistically processed. The influence of P, N, channel and thermal noise on the time precision of AP generation was tested by repeated stimulation of identical input. The precision was mostly influenced by the synaptic unreliability and substantially dependent upon values N and P. The channel noise was about an order less corruptive than the synaptic unreliability, causing the time precision of neuronal responses to vary from submilliseconds to tens of microseconds, depending on the values P and N.

Action Potentials↗
PubMed1991

Generation of synaptic noise: selective involvement of neuronal subsets.

All central neurons are subjected to continuous and random variations of their membrane potential because of "spontaneous" activity in their presynaptic afferents. This activity, which is called synaptic noise, is presumed to be responsible for the uncertainty of the input-output relation in these cells. In the Mauthner cell of teleosts, noise is mainly inhibitory, and is generated by the release of neurotransmitter in a probabilistic manner. This inhibitory activity has been studied in detail previously. Taking advantage of this understanding, we have constructed a model of the inhibitory networks and their target in order to determine the conditions required to reproduce the main stochastic aspects of synaptic noise. We have used a combination of computer simulations and simple semianalytical arguments. We conclude that, surprisingly, cells in the presynaptic networks do not contribute equally to these background fluctuations. Rather, noise is generated primarily by the operation of subsets of afferent cells: the spectrum is either dominated by signals originating from interneurons which make few terminals on the Mauthner cell, or by the output of "burster" cells firing spike trains rather than single spikes. Both possibilities lead to specific predictions, one of which has already been verified.

Analysis of Variance↗
PubMed2002

Comparison of deterministic and probabilistic calculation of ecological soil screening levels.

The U.S. Environmental Protection Agency (U.S. EPA) is sponsoring development of ecological soil screening levels (Eco-SSLs) for terrestrial wildlife. These are intended to be used to identify chemicals of potential ecological concern at Superfund sites. Ecological soil screening levels represent concentrations of contaminants in soils that are believed to be protective of ecological receptors. An exposure model, based on soil- and food-ingestion rates and the relationship between the concentrations of contaminants in soil and food, has been developed for estimation of wildlife Eco-SSLs. It is important to understand how conservative and protective these values are, how parameterization of the model influences the resulting Eco-SSL, and how the treatment of uncertainty impacts results. The Eco-SSLs were calculated for meadow voles (Microtus pennsylvanicus) and northern short-tailed shrews (Blarina brevicauda) for lead and DDT using deterministic and probabilistic methods. Conclusions obtained include that use of central-tendency point estimates may result in hazard quotients much larger than one; that a Monte Carlo approach also leads to hazard quotients that can be substantially larger than one; that, if no hazard quotients larger than one are allowed, any probabilistic approach is identical to a worst-case approach; and that an improvement in the quality and amount of data is necessary to increase confidence that Eco-SSLs are protective at their intended levels of conservatism.

Animals↗
PubMed1997

Large hierarchical Bayesian analysis of multivariate survival data.

Failure times that are grouped according to shared environments arise commonly in statistical practice. That is, multiple responses may be observed for each of many units. For instance, the units might be patients or centers in a clinical trial setting. Bayesian hierarchical models are appropriate for data analysis in this context. At the first stage of the model, survival times can be modelled via the Cox partial likelihood, using a justification due to Kalbfleisch (1978, Journal of the Royal Statistical Society, Series B 40, 214-221). Thus, questionable parametric assumptions are avoided. Conventional wisdom dictates that it is comparatively safe to make parametric assumptions at subsequent stages. Thus, unit-specific parameters are modelled parametrically. The posterior distribution of parameters given observed data is examined using Markov chain Monte Carlo methods. Specifically, the hybrid Monte Carlo method, as described by Neal (1993a, in Advances in Neural Information Processing 5, 475-482; 1993b, Probabilistic inference using Markov chain Monte Carlo methods), is utilized.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed1995

Economic evaluation using mathematical models: the case of misoprostol prophylaxis.

Using misoprostol prophylaxis as an example, many of the methods employed in economic analyses that incorporate mathematical models were described. These included: decision analysis, cost effectiveness analysis (including incremental cost effectiveness analysis), one-way, multi-way, and probabilistic sensitivity analysis, and the estimation of quality adjusted life years for use in cost utility analysis. In the case of misoprostol prophylaxis, the cost effectiveness analysis demonstrated that, compared to the no prophylaxis alternative, prophylaxis cost an extra $650, on average, for every additional ulcer prevented, and was potentially cost saving for some high risk groups. The cost utility analysis demonstrated that prophylaxis resulted (on average) in modest additional costs and no additional quality of life benefits. Sensitivity analysis demonstrated that, at worst, prophylaxis reduced quality of life; at best, the incremental cost effectiveness ratio was $9333 for each quality adjusted life-year gained by prophylaxis compared to no prophylaxis. The results of the cost utility analysis also showed that prophylaxis may be cost saving in high risk groups, confirming the results of the cost effectiveness analysis. Finally, and perhaps most importantly, this analysis illustrated the importance of incorporating measures of health related quality of life into economic evaluation.

Cost-Benefit Analysis↗
PubMed1993

A probabilistic estimation of atmospheric tritium dose.

A radiation dose distribution has been calculated for an individual exposed to tritium oxide (tritiated water vapor) in the atmosphere. A specific activity model was used that is similar to that detailed in the Nuclear Regulatory Commission's Regulatory Guide 1.109. The values of 19 parameters are sampled consistent with assigned probability distributions using a Latin Hypercube approach. Dose was estimated assuming a concentration of tritium in atmospheric moisture of 0.13 +/- 0.16 Bq mL-1. Conventional dosimetric methods for tritium that were consistent with the Regulatory Guide result in an estimate of 0.63 microSv for annual exposures to airborne tritiated water. For the same atmospheric tritium concentration, the estimated dose distribution is lognormal and ranges from 0.08-3.2 microSv (at 95% confidence) with a median value of 0.59 microSv and a 95th percentile of 2.6 microSv.

Air Pollution, Radioactive↗
PubMed2026

Robust optimisation for photon radiotherapy: A scoping review of models, paradigms, and reporting.

BACKGROUND AND PURPOSE: Robust optimisation offers an alternative to conventional margin-based photon radiotherapy planning by explicitly modelling uncertainty, but practice is variable and not standardised. MATERIALS AND METHODS: A scoping review was conducted to map robust optimisation for photon external beam radiotherapy. Electronic searches of Scopus, PubMed and Google Scholar (2000-2025, English language) identified planning studies that incorporated modelled uncertainties into the optimisation process and reported at least one robustness-related outcome. Data were charted on clinical context, uncertainty models, optimisation paradigms, robustness metrics and evidence for clinical implementation. RESULTS: Seventy-one studies were included. Most investigated prostate, breast or lung cancer and used intensity-modulated radiotherapy or volumetric-modulated arc therapy in commercial or research treatment planning systems. Scenario-based worst-case (minimax) optimisation was the dominant paradigm in clinically oriented work, while chance-constrained, conditional value at-risk, distributionally robust and adaptive formulations were confined to small methodological series. Uncertainty modelling focused mainly on rigid set-up error; fewer studies incorporated respiratory motion, inter-fraction anatomical change, dose-calculation uncertainty or biological variation. Robustness was evaluated with diverse scenario-based dose-volume metrics, probabilistic coverage measures, composite robustness indices and, less often, biological endpoints. Direct clinical implementation reports were scarce. CONCLUSION: Robust photon planning is technically feasible and generally maintains or improves target coverage and organ sparing compared with margin-based planning. However, heterogeneity in uncertainty models, optimisation configuration and robustness reporting limits comparison and synthesis. Pragmatic minimum standards are proposed to support future consensus and wider clinical adoption.

Humans↗
PubMed2003

Probabilistic Monte Carlo based mapping of cerebral connections utilising whole-brain crossing fibre information.

A methodology is presented for estimation of a probability density function of cerebral fibre orientations when one or two fibres are present in a voxel. All data are acquired on a clinical MR scanner, using widely available acquisition techniques. The method models measurements of water diffusion in a single fibre by a Gaussian density function and in multiple fibres by a mixture of Gaussian densities. The effects of noise on complex MR diffusion weighted data are explicitly simluated and parameterised. This information is used for standard and Monte Carlo streamline methods. Deterministic and probabilistic maps of anatomical voxel scale connectivity between brain regions are generated.

Algorithms↗