Sensitivity and Bias within the Binary Signal Detection Theory, BSDT

Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.

Expanding the cone location and magnitude index to include corneal thickness and posterior surface information for the detection of keratoconus

PURPOSE To extend the capabilities of the Cone Location and Magnitude Index algorithm to include a combination of topographic information from the anterior and posterior corneal surfaces and corneal thickness measurements to further improve our ability to correctly identify keratoconus using this new index: ConeLocationMagnitudeIndex_X. DESIGN Retrospective case-control study. METHODS Three independent data sets were analyzed: 1 development and 2 validation. The AnteriorCornealPower index was calculated to stratify the keratoconus data from mild to severe. The ConeLocationMagnitudeIndex algorithm was applied to all tomography data collected using a dual Scheimpflug-Placido-based tomographer. The ConeLocationMagnitudeIndex_X formula, resulting from analysis of the Development set, was used to determine the logistic regression model that best separates keratoconus from normal and was applied to all data sets to calculate PercentProbabilityKeratoconus_X. The sensitivity/specificity of PercentProbabilityKeratoconus_X was compared with the original PercentProbabilityKeratoconus, which only uses anterior axial data. RESULTS The AnteriorCornealPower severity distribution for the combined data sets are 136 mild, 12 moderate, and 7 severe. The logistic regression model generated for ConeLocationMagnitudeIndex_X produces complete separation for the Development set. Validation Set 1 has 1 false-negative and Validation Set 2 has 1 false-positive. The overall sensitivity/specificity results for the logistic model produced using the ConeLocationMagnitudeIndex_X algorithm are 99.4% and 99.6%, respectively. The overall sensitivity/specificity results for using the original ConeLocationMagnitudeIndex algorithm are 89.2% and 98.8%, respectively. CONCLUSIONS ConeLocationMagnitudeIndex_X provides a robust index that can detect the presence or absence of a keratoconic pattern in corneal tomography maps with improved sensitivity/specificity from the original anterior surface-only ConeLocationMagnitudeIndex algorithm.

Correlation of the Corneal Toricity Between Anterior and Posterior Corneal Surfaces in the Normal Human Eye

Purpose: To evaluate the correlation of the magnitude of corneal toricity and power vector components of both corneal surfaces measured with a Scheimpflug photography-based system. Methods: A total of 117 healthy normal eyes of 117 subjects selected randomly with ages ranging from 7 to 80 years were included. All eyes received an anterior segment and corneal analysis with the Sirius system (CSO) evaluating the anterior and posterior mean toricity for 3 and 7 mm (aAST and pAST). The vector components J0 and J45 as well as the overall strength blur (B) were calculated for each keratometric measurement using the procedure defined by Thibos and Horner. Results: The coefficient of correlation between aAST and pAST was 0.52 and 0.62 and the mean anteroposterior ratio for toricity was 0.46 ± 0.39 and 0.57 ± 0.75 for 3 and 7 mm, respectively. These ratios correlated significantly with aAST, anterior corneal J0, and manifest refraction J0 (r ≥ 0.39, P < 0.01). The coefficient of correlation was 0.69 and 0.81 between anterior and posterior J0 for 3 and 7 mm, respectively. For J45, the coefficients were 0.62 and 0.71, respectively. The linear regression analysis revealed that the pAST and power vectors could be predicted from the anterior corneal data (R2 ≥ 0.40, P < 0.01). Conclusions: The toricity and astigmatic power vector components of the posterior corneal surface in the human healthy eye are related to those of the anterior and therefore can be predicted consistently from the anterior toricity and astigmatic power vectors.

Prior and posterior linear pooling for combining expert opinions: Uses and impact on Bayesian Networks

The use of expert knowledge to quantify a Bayesian Network (BN) is necessary when data is not available. This however raises questions regarding how opinions from multiple experts can be used in a BN. Linear pooling is a popular method for combining probability assessments from multiple experts. In particular, Prior Linear Pooling (PrLP), which pools opinions then places them into the BN is a common method. This paper firstly proposes an alternative pooling method, Posterior Linear Pooling (PoLP). This method constructs a BN for each expert, then pools the resulting probabilities at the nodes of interest. Secondly, it investigates the advantages and disadvantages of using these pooling methods to combine the opinions of multiple experts. Finally, the methods are applied to an existing BN, the Wayfinding Bayesian Network Model, to investigate the behaviour of different groups of people and how these different methods may be able to capture such differences. The paper focusses on 6 nodes Human Factors, Environmental Factors, Wayfinding, Communication, Visual Elements of Communication and Navigation Pathway, and three subgroups Gender (female, male),Travel Experience (experienced, inexperienced), and Travel Purpose (business, personal) and finds that different behaviors can indeed be captured by the different methods.

Finite mixture distributions, sequential likelihood and the EM algorithm

A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a finite mixture distribution. A barrier to using finite mixture models is that parameters that could previously be estimated in stages must now be estimated jointly: using mixture distributions destroys any additive separability of the log-likelihood function. We show, however, that an extension of the EM algorithm reintroduces additive separability, thus allowing one to estimate parameters sequentially during each maximization step. In establishing this result, we develop a broad class of estimators for mixture models. Returning to the likelihood problem, we show that, relative to full information maximum likelihood, our sequential estimator can generate large computational savings with little loss of efficiency.

The intraocular lens: challenges in the prevention and therapy of infectious endophthalmitis and posterior capsular opacification

Cataract is the leading cause of visual impairment worldwide. In the UK, some 30% of the population over 65 years of age have visually impairing cataract. Importantly, 88% of those with treatable visual impairment from cataract are not in contact with any ocular healthcare service, representing a major potential healthcare need [1]. In the USA, it has been estimated that 17.2% of the population (approximately 20.5 million) over 40 years of age have cataract in either eye and by 2020, this number is expected to rise to 30.1 million. Currently, cataract is responsible for 60% of Medicare costs associated with vision [2]. Furthermore, as the populations of industrialized countries such as the UK and the USA continue to age, the costs associated with treatment of cataract can only be expected to increase. Consequently, the development of the intraocular lens to replace the cataractous lens and the advances in intraocular lens design and implantation represent a major development in cataract treatment. However, despite such advances, cataract surgery is not without complications, such as postoperative infectious endophthalmitis, a rare but potentially devastating condition, and posterior capsular opacification, a less serious but much more common problem. This review will examine the epidemiology of cataracts, the polymeric construction of intraocular lenses implanted during cataract surgery and the complications of postoperative infectious endophthalmitis and posterior capsular opacification with regard to therapeutic interventions and prophylactic strategies. Advances in biomaterial design and function will be discussed as novel approaches to prevent such postoperative complications.