Deep learning to automate the labelling of head MRI datasets for computer vision applications

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images

Grouping practices in the primary school: what influences change?

During the 1990s, there was considerable emphasis on promoting particular kinds of pupil grouping as a means of raising educational standards. This survey of 2000 primary schools explored the extent to which schools had changed their grouping practices in responses to this, the nature of the changes made and the reasons for those changes. Forty eight percent of responding schools reported that they had made no change. Twenty two percent reported changes because of the literacy hour, 2% because of the numeracy hour, 7% because of a combination of these and 21% for other reasons. Important influences on decisions about the types of grouping adopted were related to pupil learning and differentiation, teaching, the implementation of the national literacy strategy, practical issues and school self-evaluation

Partial loss-of-function of sodium channel SCN8A in familial isolated myoclonus.

Variants in the neuronal sodium channel gene SCN8A have been implicated in several neurological disorders. Early infantile epileptic encephalopathy type 13 results from de novo gain-of-function mutations that alter the biophysical properties of the channel. Complete loss-of-function variants of SCN8A have been identified in cases of isolated intellectual disability. We now report a novel heterozygous SCN8A variant, p.Pro1719Arg, in a small pedigree with five family members affected with autosomal dominant upper limb isolated myoclonus without seizures or cognitive impairment. Functional analysis of the p.Pro1719Arg variant in transfected neuron-derived cells demonstrated greatly reduced Nav 1.6 channel activity without altered gating properties. Hypomorphic alleles of Scn8a in the mouse are known to result in similar movement disorders. This study expands the phenotypic and functional spectrum of SCN8A variants to include inherited nonepileptic isolated myoclonus. SCN8A can be considered as a candidate gene for isolated movement disorders without seizures

Sexual Size Dimorphism and Body Condition in the Australasian Gannet

Funding: The research was financially supported by the Holsworth Wildlife Research Endowment. Acknowledgments We thank the Victorian Marine Science Consortium, Sea All Dolphin Swim, Parks Victoria, and the Point Danger Management Committee for logistical support. We are grateful for the assistance of the many field volunteers involved in the study.Peer reviewedPublisher PD

Resolving the ancestry of Austronesian-speaking populations

There are two very different interpretations of the prehistory of Island Southeast Asia (ISEA), with genetic evidence invoked in support of both. The “out-of-Taiwan” model proposes a major Late Holocene expansion of Neolithic Austronesian speakers from Taiwan. An alternative, proposing that Late Glacial/postglacial sea-level rises triggered largely autochthonous dispersals, accounts for some otherwise enigmatic genetic patterns, but fails to explain the Austronesian language dispersal. Combining mitochondrial DNA (mtDNA), Y-chromosome and genome-wide data, we performed the most comprehensive analysis of the region to date, obtaining highly consistent results across all three systems and allowing us to reconcile the models. We infer a primarily common ancestry for Taiwan/ISEA populations established before the Neolithic, but also detected clear signals of two minor Late Holocene migrations, probably representing Neolithic input from both Mainland Southeast Asia and South China, via Taiwan. This latter may therefore have mediated the Austronesian language dispersal, implying small-scale migration and language shift rather than large-scale expansion

Cancer incidence in the south Asian population of England (1990–92)

Cancer incidence among English south Asians (residents in England with ethnic origins in India, Pakistan or Bangladesh) is described and compared with non-south Asian and Indian subcontinent rates. The setting for the study was areas covered by Thames, Trent, West Midlands and Yorkshire cancer registries. The study identified 356 555 cases of incident cancer (ICD9:140–208) registered between 1990 and 1992, including 3845 classified as English south Asian. The main outcome measures were age specific and directly standardized incidence rates for all cancer sites (ICD9:140–208). English south Asian incidence rates for all sites combined were significantly lower than non-south Asian rates but higher than Indian subcontinent rates. English south Asian rates were substantially higher than Indian subcontinent rates for a number of common sites including lung cancer in males, breast cancer in females and lymphoma in both sexes. English south Asian rates for childhood and early adult cancer (0–29 years) were similar or higher than non-south Asian rates. English south Asian rates were significantly higher than non-south Asian rates for Hodgkin's disease in males, cancer of the tongue, mouth, oesophagus, thyroid gland and myeloid leukaemia in females, and cancer of the hypopharynx, liver and gall bladder in both sexes. The results are consistent with a transition from the lower cancer risk of the country of ethnic origin to that of the country of residence. They suggest that detrimental changes in lifestyle and other exposures have occurred in the migrant south Asian population. © 1999 Cancer Research Campaig

Does prenatal micronutrient supplementation improve children's mental development? A systematic review

<p>Abstract</p> <p>Background</p> <p>Although maternal nutrient status influences all aspects of fetal development including the brain, the impact of micronutrient supplementation on the baby's mental function is a topic of debate. This systematic review assesses the effect of single and multiple micronutrient supplementation during pregnancy on offspring mental development.</p> <p>Methods</p> <p>Eleven electronic literature databases were searched using key terms of various combinations and filter string terms. Reference lists of articles selected for review were scanned for citations fitting the same inclusion criteria. Each stage of the literature retrieval and review process was conducted independently by two reviewers. The CONSORT checklist was used to assess study quality.</p> <p>Results</p> <p>A total of 1316 articles were retrieved from the electronic database search, of which 18 met the inclusion criteria and were evaluated. The selected studies were randomized controlled trials published from 1983 to 2010, with high variance in sample size, intervention type, and outcome measures. The median CONSORT score was 15 (range 12 - 19). Due to inconsistent interventions and outcome measures among the studies, no conclusive evidence was found that enhancing the intrauterine environment through micronutrient supplementation was associated with child mental development in a number of dimensions. There was some evidence to support n-3 fatty acids or multi-micronutrients having some positive effect on mental development, but the evidence for single nutrients was much weaker.</p> <p>Conclusions</p> <p>The study of children's mental outcomes as a function of prenatal supplementation is still relatively new, but the results of this systematic review suggest that further work with multiple micronutrients and/or n-3 fatty acids should be conducted.</p

Implementation of Fourier transform infrared spectroscopy for the rapid typing of uropathogenic Escherichia coli.

In this paper, we demonstrate that Fourier transform infrared (FT-IR) spectroscopy is able to discriminate rapidly between uropathogenic Escherichia coli (UPEC) of key lineages with only relatively simple sample preparation. A total of 95 bacteria from six different epidemiologically important multilocus sequence types (ST10, ST69, ST95, ST73, ST127 and ST131) were used in this project and principal component-discriminant function analysis (PC-DFA) of these samples produced clear separate clustering of isolates, based on the ST. Analysis of data using partial least squares-discriminant analysis (PLS-DA), incorporating cross-validation, indicated a high prediction accuracy of 91.19% for ST131. These results suggest that FT-IR spectroscopy could be a useful method for the rapid identification of members of important UPEC STs