Deep learning predicted perceived age is a reliable approach for analysis of facial ageing: A proof of principle study.

BACKGROUND: Perceived age (PA) has been associated with mortality, genetic variants linked to ageing and several age-related morbidities. However, estimating PA in large datasets is laborious and costly to generate, limiting its practical applicability. OBJECTIVES: To determine if estimating PA using deep learning-based algorithms results in the same associations with morbidities and genetic variants as human-estimated perceived age. METHODS: Self-supervised learning (SSL) and deep feature transfer (DFT) deep learning (DL) approaches were trained and tested on human-estimated PAs and their corresponding frontal face images of middle-aged to elderly Dutch participants (n = 2679) from a population-based study in the Netherlands. We compared the DL-estimated PAs with morbidities previously associated with human-estimated PA as well as genetic variants in the gene MC1R; we additionally tested the PA associations with MC1R in a new validation cohort (n = 1158). RESULTS: The DL approaches predicted PA in this population with a mean absolute error of 2.84 years (DFT) and 2.39 years (SSL). In the training-test dataset, we found the same significant (p < 0.05) associations for DL PA with osteoporosis, ARHL, cognition, COPD and cataracts and MC1R, as with human PA. We also found a similar but less significant association for SSL and DFT PAs (0.69 and 0.71 years per allele, p = 0.008 and 0.011, respectively) with MC1R variants in the validation dataset as that found with human, SSL and DFT PAs in the training-test dataset (0.79, 0.78 and 0.71 years per allele respectively; all p < 0.0001). CONCLUSIONS: Deep learning methods can automatically estimate PA from facial images with enough accuracy to replicate known links between human-estimated perceived age and several age-related morbidities. Furthermore, DL predicted perceived age associated with MC1R gene variants in a validation cohort. Hence, such DL PA techniques may be used instead of human estimations in perceived age studies thereby reducing time and costs.

Pre-hospital management of mass casualty civilian shootings: a systematic literature review.

BACKGROUND: Mass casualty civilian shootings present an uncommon but recurring challenge to emergency services around the world and produce unique management demands. On the background of a rising threat of transnational terrorism worldwide, emergency response strategies are of critical importance. This study aims to systematically identify, describe and appraise the quality of indexed and non-indexed literature on the pre-hospital management of modern civilian mass shootings to guide future practice. METHODS: Systematic literature searches of PubMed, Cochrane Database of Systematic Reviews and Scopus were conducted in conjunction with simple searches of non-indexed databases; Web of Science, OpenDOAR and Evidence Search. The searches were last carried out on 20 April 2016 and only identified those papers published after the 1 January 1980. Included documents had to contain descriptions, discussions or experiences of the pre-hospital management of civilian mass shootings. RESULTS: From the 494 identified manuscripts, 73 were selected on abstract and title and after full text reading 47 were selected for inclusion in analysis. The search yielded reports of 17 mass shooting events, the majority from the USA with additions from France, Norway, the UK and Kenya. Between 1994 and 2015 the shooting of 1649 people with 578 deaths at 17 separate events are described. Quality appraisal demonstrated considerable heterogeneity in reporting and revealed limited data on mass shootings globally. CONCLUSION: Key themes were identified to improve future practice: tactical emergency medical support may harmonise inner cordon interventions, a need for inter-service education on effective haemorrhage control, the value of senior triage operators and the need for regular mass casualty incident simulation.