Automated Assessment of the DWI-FLAIR Mismatch in Patients with Acute Ischemic Stroke: Added Value to Routine Clinical Practice.

BACKGROUND AND PURPOSE: The DWI-FLAIR mismatch is used to determine thrombolytic eligibility in patients with acute ischemic stroke when the time since stroke onset is unknown. Commercial software packages have been developed for automated DWI-FLAIR classification. We aimed to use e-Stroke software for automated classification of the DWI-FLAIR mismatch in a cohort of patients with acute ischemic stroke and in a comparative analysis with 2 expert neuroradiologists. MATERIALS AND METHODS: In this retrospective study, patients with acute ischemic stroke who had MR imaging and known time since stroke onset were included. The DWI-FLAIR mismatch was evaluated by 2 neuroradiologists blinded to the time since stroke onset and automatically by the e-Stroke software. After 4 weeks, the neuroradiologists re-evaluated the MR images, this time equipped with automated predicted e-Stroke results as a computer-assisted tool. Diagnostic performances of e-Stroke software and the neuroradiologists were evaluated for prediction of DWI-FLAIR mismatch status. RESULTS: A total of 157 patients met the inclusion criteria. A total of 82 patients (52%) had a time since stroke onset of ≤4.5 hours. By means of consensus reads, 81 patients (51.5%) had a DWI-FLAIR mismatch. The diagnostic accuracy (area under the curve/sensitivity/specificity) of e-Stroke software for the determination of the DWI-FLAIR mismatch was 0.72/90.0/53.9. The diagnostic accuracy (area under the curve/sensitivity/specificity) for neuroradiologists 1 and 2 was 0.76/69.1/84.2 and 0.82/91.4/73.7, respectively; both significantly (P < .05) improved to 0.83/79.0/86.8 and 0.89/92.6/85.5, respectively, following the use of e-Stroke predictions as a computer-assisted tool. The interrater agreement (κ) for determination of DWI-FLAIR status was improved from 0.49 to 0.57 following the use of the computer-assisted tool. CONCLUSIONS: This automated quantitative approach for DWI-FLAIR mismatch provides results comparable with those of human experts and can improve the diagnostic accuracies of expert neuroradiologists in the determination of DWI-FLAIR status.

Incorporating advanced language models into the P300 speller using particle filtering.

OBJECTIVE: The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject's electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity. APPROACH: Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model. MAIN RESULT: This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method. SIGNIFICANCE: These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.

Field note phytovolatilization of oxygenated gasoline-impacted groundwater at an underground storage tank site via conifers.

A stand of five conifers (Pinus sp.) bordering a gasoline service station was studied to estimate the methyl tert-butyl ether (MTBE) emission rate from gasoline-impacted groundwater. Groundwater was impacted with gasoline oxygenates MTBE and tert-butyl alcohol (TBA) at combined concentrations exceeding 200,000 microg/L. Condensate from trees was collected in sealed environmental chambers and analyzed. Concentrations of MTBE in condensate ranged from 0.51 to 460 microg/L; TBA ranged from 12 to 4100 microg/L (n=19). Transpirate concentrations were derived from MTBE air-liquid partitioning data exhibited in controls spiked with known concentrations of analyte. Tree emissions were estimated by multiplying average transpirate concentrations by transpiration rates derived from evapotranspiration data. Stand evapotranspiration was calculated using meteorological data from the California Irrigation Management Information System (CIMIS) applied in the Standardized Reference Evapotranspiration Equation.

The occupational health status of African-American women health care workers.

Race, ethnicity, and gender are significant indicators of occupational status, general health status, and thus, occupational health status. Although African-American women constitute only 6.8% of the total U.S. labor force, they hold 20% of the jobs in the health care industry and are disproportionately represented in those jobs that have the highest levels of workplace exposure to hazards. As a result, they are therefore more likely to be at greater exposure and risk to the spectrum of occupational health problems. In order to gain insight into the effects of race and gender on the occupational health status of African-American women health care workers, this article uses three data sources that provide different but complementary sources of information on the demographic characteristics of workers, location of categories of occupations, working conditions of jobs, and other job and worker characteristics. Given the concentration of African-American women in health care positions where there exists a greater likelihood of being exposed to occupational hazards, it is therefore both logical and appropriate for primary care physicians, especially those engaged in office-based practices, to identify this target population for special services and to be more aware of the type of health issues with which these patients are more likely to present and to experience during their working lives. Health care providers have a responsibility to assess occupational factors related to a patient's health problems and to incorporate this information into their treatment protocols and into the design and explanation of each patient's care plan.