Contrast response function estimation with nonparametric Bayesian active learning

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast sensitivity functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This article describes the development of the machine learning contrast response function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the machine learning contrast sensitivity function (MLCSF) was evaluated to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential

Ultrasonic, photocatalytic and sonophotocatalytic degradation of Basic Red-2 by using Nb₂O₅ nano catalyst

The ultrasonic, photocatalytic and sonophotocatalytic degradation of Basic Red-2 accompanied by Nb2O5 nano catalysts were studied. The structure and morphology of synthesized Nb2O5 nano catalyst was investigated using scanning election microscopy (SEM), Electron dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD).The effects of various experimental parameters such as the Basic Red-2 concentration, catalyst dose, pH and addition of H2O2 on the ultrasonic, photocatalytic and sonophotocatalytic degradation were investigated. Photocatalytic and sonophotocatalytic degradation of Basic Red-2 was strongly affected by initial dye concentration, catalyst dose, H2O2 addition and pH. Basic pH (pH-10) was favored for the ultrasonic (US), photocatalytic (UV + Nb2O5) and sonophotocatalytic (US + UV + Nb2O5) degradation of Basic Red-2 by using Nb2O5 nano catalyst. The ultrasonic degradation of Basic Red-2 was enhanced by the addition of photocatalyst. Then, the effect of Nb2O5 dose on photocatalytic and sonophotocatalytic degradation were studied, and it was found that increase in catalyst dose increase in the percentage degradation of Basic Red-2. In addition, the effects of H2O2 on ultrasonic, photolytic, photocatalytic and sonophotocatalytic degradation was also investigated, and it was found that H2O2 enhances the % degradation of Basic Red-2. The possible mechanism of ultrasonic, photocatalytic and sonophotocatalytic degradation of Basic Red-2 reported by LC-MS shows generation of different degradation product

Comparison of shape-based analysis of retinal nerve fiber layer data obtained from OCT and GDx-VCC

10.1097/IJG.0b013e31818c6f2bJournal of Glaucoma186464-47

Predicting subsequent visual field loss in glaucomatous subjects with disc hemorrhage using retinal nerve fiber layer polarimetry

To predict progression of visual field loss after an episode of disc hemorrhage in glaucoma patients on the basis of retinal nerve fiber layer (RNFL) GDx polarimetry measurements analyzed by wavelet-Fourier analysis (WFA). Retrospective GDx data from 16 subjects (10 progressors and 6 non-progressors based on visual fields) obtained near the time of disc hemorrhage were analyzed to predict which patients would have visual field progression. Polarimetry scans throughout a follow-up period (31 months average) were also analyzed to compare field progression to RNFL thickness change after the hemorrhage. Mean RNFL thickness inferred from the polarimetry data at sixteen 22.5 degrees sectors at distances of 1.6, 1.7, and 1.8 disc diameters were used. Data were analyzed by applying to appropriate regions of disc hemorrhage patients a structural analysis (WFA) we had developed previously. A linear discriminant function (Fischer) was produced and a leave-one-out method using separate training and test data was used to assure validity of the results. Patients who subsequently progressed were successfully predicted with moderate success (sensitivity / specificity was 0.77 / 0.88 with ROC area = 0.858). A separate analysis comparing pre- and post-hemorrhage RNFL sector thickness revealed clear evidence of RNFL thinning at the inferior and superior sectors before progression of visual field. The thinning of RNFL thickness was not restricted to regions corresponding to the location of the hemorrhage. Wavelet-Fourier analysis can differentiate progressors from non-progressors with moderate accuracy. Comparison to a prior study of this same cohort emphasizes that relatively small regions must be considered (as opposed to larger quadrants) to see these significant changes in RNFL

Spatial Working Memory as a Cognitive Endophenotype of Schizophrenia: Assessing Risk for Pathophysiological Dysfunction

Research suggests that first-degree relatives and individuals with schizophrenia spectrum personality disorders (SSPD) may represent nonpenetrant carriers of the genetic diathesis for schizophrenia. This study examined visuospatial working memory (SWM) as a cognitive endophenotype of schizophrenia by expanding the concept of risk for pathophysiological dysfunction beyond overt psychosis. Risk was thus defined by familial status and the presence or absence of SSPD. SWM was assessed in the following groups, in order of decreasing likelihood of genetic vulnerability: 23 patients with schizophrenia, 17 SSPD relatives of patients with schizophrenia, 23 non-SSPD relatives of patients with schizophrenia, 14 SSPD community members with no family history of psychosis, and 36 non-SSPD community members. SWM performance during a computer task was quantified by A-Prime. Relative risk ratios for SWM deficits were compared among the groups. Compared with community non-SSPD volunteers, relative risk (RR) of SWM deficits was significantly elevated in patients with schizophrenia (RR = 3.76, p = .002) and SSPD family members (RR = 2.97, p = .027), but not in the family non-SSPD (RR = 1.88, p = .241) or community SSPD (RR = 1.03, p = .971) groups. The pattern of SWM performance deficits reflected the proposed model of latent genetic liability, upholding SWM as a viable cognitive endophenotype. The results underscore the importance of including both familial liability and the schizophrenia spectrum when considering risk for schizophrenia and schizophrenia-related traits. This is particularly relevant for research efforts to identify pathophysiological components of the disease

Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury

Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers' decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN using 64,728 ECGs from 32,479 patients who underwent ECG within 2 h prior to a serum TnI laboratory result at the University of California, San Francisco (UCSF). In our primary analysis, we classified patients into groups of TnI < 0.02 or ≥ 0.02 µg/L using 12-lead ECGs. This was repeated with an alternative threshold of 1.0 µg/L and with single-lead ECG inputs. We also performed multiclass prediction for a set of serum troponin ranges. Finally, we tested the CNN in a cohort of patients selected for coronary angiography, including 3038 ECGs from 672 patients. Cohort patients were 49.0% female, 42.8% white, and 59.3% (19,283) never had a positive TnI value (≥ 0.02 µg/L). CNNs accurately predicted elevated TnI, both at a threshold of 0.02 µg/L (AUC = 0.783, 95% CI 0.780-0.786) and at a threshold of 1.0 µg/L (AUC = 0.802, 0.795-0.809). Models using single-lead ECG data achieved significantly lower accuracy, with AUCs ranging from 0.740 to 0.773 with variation by lead. Accuracy of the multi-class model was lower for intermediate TnI value-ranges. Our models performed similarly on the cohort of patients who underwent coronary angiography. Biomarker-defined myocardial injury can be predicted by CNNs from 12-lead and single-lead ECGs

Effect of corneal parameters on measurements using the pulsatile ocular blood flow tonograph and Goldmann applanation tonometer

Aims: To investigate the effect of central corneal thickness and corneal curvature on intraocular pressure measurements using the pulsatile ocular blood flow tonograph and the Goldmann applanation tonometer, and to assess the agreement between the pulsatile ocular blood flow tonograph and the Goldmann applanation tonometer in intraocular pressure measurement. Methods: 479 subjects underwent intraocular pressure measurements with the Goldmann applanation tonometer and the pulsatile ocular blood flow tonograph. Of these, 334 patients underwent additional measurement of central corneal thickness with an ultrasonic pachymeter and corneal curvature measurement with a keratometer. Results: The intraocular pressure measurements obtained with both the Goldmann applanation tonometer and the pulsatile ocular blood flow tonograph varied with central corneal thickness and mean keratometric reading. Intraocular pressure measured using the Goldmann applanation tonometer increased by 0.027 mm Hg per µm increase in central corneal thickness. Intraocular pressure measured using the pulsatile ocular blood flow tonograph increased by 0.048 mm Hg per μm increase in central corneal thickness. For an increase of 1 mm of mean corneal curvature there was rise in intraocular pressure of 1.14 mm Hg measured by the Goldmann applanation tonometer and of 2.6 mm Hg measured by the pulsatile ocular blood flow tonograph. When compared to the Goldmann applanation tonometer, the pulsatile ocular blood flow tonograph underestimated at low intraocular pressure and overestimated at higher intraocular pressure. Conclusion: Central corneal thickness and corneal curvature affected measurements obtained with the pulsatile ocular blood flow tonograph more than they affected measurements obtained with the Goldmann applanation tonometer

Clinical language search algorithm from free-text: facilitating appropriate imaging.

BackgroundThe comprehensiveness and maintenance of the American College of Radiology (ACR) Appropriateness Criteria (AC) makes it a unique resource for evidence-based clinical imaging decision support, but it is underutilized by clinicians. To facilitate the use of imaging recommendations, we develop a natural language processing (NLP) search algorithm that automatically matches clinical indications that physicians write into imaging orders to appropriate AC imaging recommendations.MethodsWe apply a hybrid model of semantic similarity from a sent2vec model trained on 223 million scientific sentences, combined with term frequency inverse document frequency features. AC documents are ranked based on their embeddings' cosine distance to query. For model testing, we compiled a dataset of simulated simple and complex indications for each AC document (n = 410) and another with clinical indications from randomly sampled radiology reports (n = 100). We compare our algorithm to a custom google search engine.ResultsOn the simulated indications, our algorithm ranked ground truth documents as top 3 for 98% of simple queries and 85% of complex queries. Similarly, on the randomly sampled radiology report dataset, the algorithm ranked 86% of indications with a single match as top 3. Vague and distracting phrases present in the free-text indications were main sources of errors. Our algorithm provides more relevant results than a custom Google search engine, especially for complex queries.ConclusionsWe have developed and evaluated an NLP algorithm that matches clinical indications to appropriate AC guidelines. This approach can be integrated into imaging ordering systems for automated access to guidelines