SignificanceHistorically, computer-assisted detection (CAD) in radiology has failed to achieve improvements in diagnostic accuracy, decreasing clinician sensitivity and leading to unnecessary further diagnostic tests. With the advent of deep learning approaches to CAD, there is great excitement about its application to medicine, yet there is little evidence demonstrating improved diagnostic accuracy in clinically-relevant applications. We trained a deep learning model to detect fractures on radiographs with a diagnostic accuracy similar to that of senior subspecialized orthopedic surgeons. We demonstrate that when emergency medicine clinicians are provided with the assistance of the trained model, their ability to accurately detect fractures significantly improves.

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Background - There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment. Methods - Using the UK Biobank (UKB) resource, we developed our own polygenic risk score (PRS) for coronary artery disease (CAD). We used an additional 60,000 UKB individuals to develop an integrated risk tool (IRT) that combined our PRS with established risk tools (either the American Heart Association/American College of Cardiology's Pooled Cohort Equations (PCE) or UK's QRISK3), and we tested our IRT in an additional, independent, set of 186,451 UKB individuals. Results - The novel CAD PRS shows superior predictive power for CAD events, compared to other published PRSs and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an integrated risk tool, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared to 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI 4.7-7.0). When individuals were stratified into age-by-sex subgroups the improvement was larger for all subgroups (range 8.3%-15.4%), with best performance in 40-54yo men (15.4%, 95% CI 11.6-19.3). Comparable results were found using a different risk tool (QRISK3), and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12,000 deaths in the USA over a 5-year period. Conclusions - An integrated risk tool that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk.

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The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters, such as dN/dS, but analyzing very large data sets poses a major statistical challenge. Here, I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: 1) It is fast no matter how large the sample size and 2) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well estimated even when genomegaMap’s simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in Mycobacterium tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species.