A novel computer test to assess driving-relevant cognitive functions - a pilot study

ABSTRACT Background: The assessment of driving-relevant cognitive functions in older drivers is a difficult challenge as there is no clear-cut dividing line between normal cognition and impaired cognition and not all cognitive functions are equally important for driving. Methods: To support decision makers, the Bern Cognitive Screening Test (BCST) for older drivers was designed. It is a computer-assisted test battery assessing visuo-spatial attention, executive functions, eye-hand coordination, distance judgment, and speed regulation. Here we compare the performance in BCST with the performance in paper and pencil cognitive screening tests and the performance in the driving simulator testing of 41 safe drivers (without crash history) and 14 unsafe drivers (with crash history). Results: Safe drivers performed better than unsafe drivers in BCST (Mann-Whitney U test: U = 125.5; p = 0.001) and in the driving simulator (Student's t-test: t(44) = -2.64, p = 0.006). No clear group differences were found in paper and pencil screening tests (p > 0.05; ns). BCST was best at identifying older unsafe drivers (sensitivity 86%; specificity 61%) and was also better tolerated than the driving simulator test with fewer dropouts. Conclusions: BCST is more accurate than paper and pencil screening tests, and better tolerated than driving simulator testing when assessing driving-relevant cognition in older driver

MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets

PURPOSE To introduce a widely applicable workflow for pulmonary lobe segmentation of MR images using a recurrent neural network (RNN) trained with chest CT datasets. The feasibility is demonstrated for 2D coronal ultrafast balanced SSFP (ufSSFP) MRI. METHODS Lung lobes of 250 publicly accessible CT datasets of adults were segmented with an open-source CT-specific algorithm. To match 2D ufSSFP MRI data of pediatric patients, both CT data and segmentations were translated into pseudo-MR images that were masked to suppress anatomy outside the lung. Network-1 was trained with pseudo-MR images and lobe segmentations and then applied to 1000 masked ufSSFP images to predict lobe segmentations. These outputs were directly used as targets to train Network-2 and Network-3 with non-masked ufSSFP data as inputs, as well as an additional whole-lung mask as input for Network-2. Network predictions were compared to reference manual lobe segmentations of ufSSFP data in 20 pediatric cystic fibrosis patients. Manual lobe segmentations were performed by splitting available whole-lung segmentations into lobes. RESULTS Network-1 was able to segment the lobes of ufSSFP images, and Network-2 and Network-3 further increased segmentation accuracy and robustness. The average all-lobe Dice similarity coefficients were 95.0 ± 2.8 (mean ± pooled SD [%]) and 96.4 ± 2.5, 93.0 ± 2.0; and the average median Hausdorff distances were 6.1 ± 0.9 (mean ± SD [mm]), 5.3 ± 1.1, 7.1 ± 1.3 for Network-1, Network-2, and Network-3, respectively. CONCLUSION Recurrent neural network lung lobe segmentation of 2D ufSSFP imaging is feasible, in good agreement with manual segmentations. The proposed workflow might provide access to automated lobe segmentations for various lung MRI examinations and quantitative analyses

MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets.

PURPOSE To introduce a widely applicable workflow for pulmonary lobe segmentation of MR images using a recurrent neural network (RNN) trained with chest CT datasets. The feasibility is demonstrated for 2D coronal ultrafast balanced SSFP (ufSSFP) MRI. METHODS Lung lobes of 250 publicly accessible CT datasets of adults were segmented with an open-source CT-specific algorithm. To match 2D ufSSFP MRI data of pediatric patients, both CT data and segmentations were translated into pseudo-MR images that were masked to suppress anatomy outside the lung. Network-1 was trained with pseudo-MR images and lobe segmentations and then applied to 1000 masked ufSSFP images to predict lobe segmentations. These outputs were directly used as targets to train Network-2 and Network-3 with non-masked ufSSFP data as inputs, as well as an additional whole-lung mask as input for Network-2. Network predictions were compared to reference manual lobe segmentations of ufSSFP data in 20 pediatric cystic fibrosis patients. Manual lobe segmentations were performed by splitting available whole-lung segmentations into lobes. RESULTS Network-1 was able to segment the lobes of ufSSFP images, and Network-2 and Network-3 further increased segmentation accuracy and robustness. The average all-lobe Dice similarity coefficients were 95.0 ± 2.8 (mean ± pooled SD [%]) and 96.4 ± 2.5, 93.0 ± 2.0; and the average median Hausdorff distances were 6.1 ± 0.9 (mean ± SD [mm]), 5.3 ± 1.1, 7.1 ± 1.3 for Network-1, Network-2, and Network-3, respectively. CONCLUSION Recurrent neural network lung lobe segmentation of 2D ufSSFP imaging is feasible, in good agreement with manual segmentations. The proposed workflow might provide access to automated lobe segmentations for various lung MRI examinations and quantitative analyses

Effects of age and eccentricity on visual target detection

The aim of this study was to examine the effects of aging and target eccentricity on a visual search task comprising 30 images of everyday life projected into a hemisphere, realizing a ±90° visual field. The task performed binocularly allowed participants to freely move their eyes to scan images for an appearing target or distractor stimulus (presented at 10°; 30°, and 50° eccentricity). The distractor stimulus required no response, while the target stimulus required acknowledgment by pressing the response button. One hundred and seventeen healthy subjects (mean age = 49.63 years, SD = 17.40 years, age range 20–78 years) were studied. The results show that target detection performance decreases with age as well as with increasing eccentricity, especially for older subjects. Reaction time also increases with age and eccentricity, but in contrast to target detection, there is no interaction between age and eccentricity. Eye movement analysis showed that younger subjects exhibited a passive search strategy while older subjects exhibited an active search strategy probably as a compensation for their reduced peripheral detection performance

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Development of a novel driving behavior adaptations questionnaire

ABSTRACT Background: Driving a car requires adapting one's behavior to current task demands taking into account one's capacities. With increasing age, driving-relevant cognitive performance may decrease, creating a need for risk-reducing behavioral adaptations. Three different kinds of behavioral adaptations are known: selection, optimization, and compensation. These can occur on the tactical and the strategic level. Risk-reducing behavioral adaptations should be considered when evaluating older drivers' traffic-related risks. Methods: A questionnaire to assess driving-related behavioral adaptations in older drivers was created. The questionnaire was administered to 61 years older (age 65-87 years; mean age = 70.2 years; SD = 5.5 years; 30 female, 31 male) and 31 younger participants (age 22-55 years; mean age = 30.5 years; SD = 6.3 years; 16 female and 15 male) to explore age and gender differences in behavioral adaptations. Results: Two factors were extracted from the questionnaire, a risk-increasing factor and a risk-reducing factor. Group comparisons revealed significantly more risk-reducing behaviors in older participants (t(84.5) = 2.21, p = 0.013) and females (t(90) = 2.52, p = 0.014) compared, respectively, to younger participants and males. No differences for the risk-increasing factor were found (p > 0.05). Conclusions: The questionnaire seems to be a useful tool to assess driving-related behavioral adaptations aimed at decreasing the risk while driving. The possibility to assess driving-related behavioral adaptations in a systematic way enables a more resource-oriented approach in the evaluation of fitness to drive in older drivers

Analyse und Entwicklungsszenarien der Spitex-Organisationen im Kanton Luzern : Bericht im Auftrag des Kantonalen Spitexverbandes Luzern

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