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- Javaher, P., et al.
(författare)
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Genetic Screening in Europe
- 2010
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Ingår i: Public Health Genomics. - : S. Karger AG. - 1662-8063 .- 1662-4246. ; 13:7-8, s. 524-537
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Tidskriftsartikel (refereegranskat)abstract
- Genetic screening has been defined as any kind of test performed systematically for the early detection or exclusion of a genetic disease, genetic predisposition or resistance to a disease, or to determine whether a person carries a gene variant that may produce disease in his or her offspring. In comparison to 'genetic testing', the term 'genetic screening' should be reserved for the explicit and systematic application of a diagnostic genetic test across a whole population of asymptomatic people (population screening) or a subset of a population such as pregnant women (prenatal/antenatal screening) or newborn infants (neonatal screening). This survey intends to present the current (2006-2008) status of genetic screening and the organization of genetic screening programmes in selected European countries as a background for future attempts to harmonize standards and procedures of genetic screening, an explicit aim of the European Network of Excellence, EuroGentest (www.eurogentest.org). Our report builds on the first comprehensive assessment of genetic screening programmes in Germany by the European Society of Human Genetics, starting with a workshop of experts in 1999, the production of background documentation in 2000, and a final report in 2003. Copyright (C) 2010 S. Karger AG, Basel
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| 4. |
- Huhtanen, JT, et al.
(författare)
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Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs
- 2022
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1, s. 11803-
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Tidskriftsartikel (refereegranskat)abstract
- Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional neural network algorithms in joint effusion classification in pediatric and adult elbow radiographs. This retrospective study consisted of a total of 4423 radiographs in a 3-year period from 2017 to 2020. Data was randomly separated into training (n = 2672), validation (n = 892) and test set (n = 859). Two models using VGG16 as the base architecture were trained with either only lateral projection or with four projections (AP, LAT and Obliques). Three radiologists evaluated joint effusion separately on the test set. Accuracy, precision, recall, specificity, F1 measure, Cohen’s kappa, and two-sided 95% confidence intervals were calculated. Mean patient age was 34.4 years (1–98) and 47% were male patients. Trained deep learning framework showed an AUC of 0.951 (95% CI 0.946–0.955) and 0.906 (95% CI 0.89–0.91) for the lateral and four projection elbow joint images in the test set, respectively. Adult and pediatric patient groups separately showed an AUC of 0.966 and 0.924, respectively. Radiologists showed an average accuracy, sensitivity, specificity, precision, F1 score, and AUC of 92.8%, 91.7%, 93.6%, 91.07%, 91.4%, and 92.6%. There were no statistically significant differences between AUC's of the deep learning model and the radiologists (p value > 0.05). The model on the lateral dataset resulted in higher AUC compared to the model with four projection datasets. Using deep learning it is possible to achieve expert level diagnostic accuracy in elbow joint effusion classification in pediatric and adult radiographs. Deep learning used in this study can classify joint effusion in radiographs and can be used in image interpretation as an aid for radiologists.
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- McGovern, Margaret M., et al.
(författare)
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Report of an international survey of molecular genetic testing laboratories
- 2007
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Ingår i: Community Genetics. - : S. Karger AG. - 1422-2795. ; 10:3, s. 123-131
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To collect data on the practices of molecular genetic testing (MGT) laboratories for the development of national and international policies for quality assurance (QA). Methods: A web-based survey of MGT laboratory directors (n = 827; response rate 63%) in 18 countries on 3 continents. QA and reporting indices were developed and calculated for each responding laboratory. Results: Laboratory setting varied among and within countries, as did qualifications of the directors. Respondents in every country indicated that their laboratory receives specimens from outside their national borders (64%, n = 529). Pair-wise comparisons of the QA index revealed a significant association with the director having formal training in molecular genetics (p < 0.005), affiliation with a genetics unit (p = 0.003), accreditation of the laboratory (p < 0.005) and participation in proficiency testing (p < 0.005). Research labs had a lower mean report score compared to all other settings (p < 0.05) as did laboratories accessioning <150 samples per year. Conclusion: MGT is provided under widely varying conditions and regulatory frameworks. The data provided here may be a useful guide for policy action at both governmental and professional levels.
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